data pitch evaluation · 3.12 comparison with odine and nesta-reviewed accelerator 69 4 case...

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December2019

DataPitchevaluation

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Authors

MoritzGodel,WouterLandzaat,RyanPerkins

TableofContents Page

LondonEconomicsDataPitchevaluation i

Executivesummary 3

1 Background&context 81.1 Realisingthevalueofdatathroughopenness 81.2 Theopeninnovationframework 91.3 Studyapproach 12

2 TheDataPitchopeninnovationprogramme 142.1 HowtheDataPitchmodelfitsinthestart-upsupportlandscape 142.2 Descriptionoftheprogramme 152.3 Pre-selection 162.4 TheDataPitchaccelerator 182.5 Additionalbenefits&programmeservices 192.6 Selectionprocess 20

3 Programmeperformance 253.1 Performanceobjectives 253.2 Sectorsofactivity 253.3 Geographicaldistribution 263.4 Europeancollaboration 283.5 Start-upperformance 293.6 Useofdata 363.7 Driversofoutcomes 493.8 Counterfactualanalysis 583.9 Impactprojections 613.10 Additionality 663.11 Evidenceontheimpactofstart-upincubators&accelerators 683.12 ComparisonwithODINEandNesta-reviewedaccelerator 69

4 Casestudies 724.1 OBUU 724.2 Bliq 784.3 Energeo 824.4 Predina 874.5 Radiobotics 924.6 Recogn.ai 96

5 Conclusions&recommendations 1015.1 Conclusions 1015.2 Recommendations 104

References 106

TableofContents Page

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Indexoftables&figures 107

Annex1 ListofDataPitchchallenges 110

Annex2 Impactforecastmethodology 112A2.1 Employmentgrowth 112A2.2 Revenuegrowth 116

Annex3 Datacollection 118A3.1 Summary 118A3.2 DataPitchevaluation–Start-uptopicguide 119A3.3 DataPitchevaluation–Dataprovidertopicguide 121A3.4 DataPitchevaluation–successfulparticipantssurvey 122A3.5 DataPitchevaluation–unsuccessfulapplicantssurvey 130

Annex4 Noteonquantitativeevaluation 135A4.1 TheFuzzyRegressionDiscontinuityDesign 135A4.2 CanFRDDbeappliedtoDataPitch? 136

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Executivesummary

Executivesummary

TheDataPitchprogramme

DataPitchisapioneeringprogrammeaimedatfacilitatingopeninnovationthroughdata-sharing.DataPitchhelpeddataproviderstodevelopopeninnovationchallengesandfacilitatedthematchingbetween data providers and start-ups. Data Pitch establishes a transnational data innovationecosystemthatcreatescollaborationbetweendataprovidersontheonehand,andstart-upswithfreshideasfordata-drivenproductsandservicesontheother.

Data Pitch combines a thorough preparation phase to identify suitable challenges and anaccelerationphaseduringwhichparticipatingstart-upsaresupportedthrough:

¢ Agrant(equity-freeinvestment)ofupto€100,000;¢ introductionstoinvestors;¢ 6-monthbusinessacceleratorprogrammewithODIandBeta-I;¢ peer-networkingandsupportviameetups;¢ accesstotrainingmaterialsandwebinarsbyDataPitchexperts;¢ legalcounselforIPprotection;¢ draftingofadatasharingagreementbetweenDataPitchpartners.

ThekeydistinguishingfeatureofDataPitchisthatitmatcheddataprovidersthatpossesseddatathattheywantedtouseforopen innovation,withstart-upswiththewherewithaltoexploitthisdata.Start-upsinthe“provider”challengeswerematchedbytheDataPitchconsortium,whereasstart-upsinthe“sector”or“open”challengeshadtofindtheirowndataprovider.Allparticipantshadtohaveapartnereddataprovider.

Thisstudy

ThestudyexploresandevaluatestheimpactofDataPitchonparticipants;participantsincludebothdataprovidersandstart-ups.Thestudyusesamixofquantitativeandqualitativedataobtainedfromprogrammedocumentation,publishedsources,andprimarydatacollection.

Themainquantitativedatacollectionforthisimpactassessmentwasasurveyofthe47start-upsfundedthroughDataPitch(successfulapplicants).41observationswerecollected.Thissurveywascomplementedwithasurveyofapplicantswhowerenotsuccessful(9observations).

Qualitativeinformationwascollectedthroughinterviewswithstart-upsanddataprovidersbetweenSeptemberandNovember,2019.

AdditionalinformationwasobtainedfrominterviewswiththeDataPitchconsortium,programmedocumentation,andexternaldatasources.Sixcasestudiesillustratetheachievementsofselectedstart-upsintheproviderandsectoralchallenges.

Participants

OverthetwoDataPitchcalls,47start-ups,outofatotalnumberof239applicants,weresuccessfulingainingadmissiontotheprogramme.22ofthedifferentchallengeswerematchedwithstart-ups,withoneDataProviderchallengeineachcallleftunmatched.18start-upswereselectedforfunding

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inCall1andthisincreasedto27inCall2.Thirteendataprovidersparticipatedintheprogramme(5inCall1and8inCall2).

Manyoftheparticipatingstart-upsworkedacrosssectors,withtheaimtoenternewsectorsbeingakeymotivator forparticipating inDataPitch.A concentrationofparticipating start-ups canbefoundinthehealth,financialandtransport/mobilitysectors.

Programmeimpacts

During the Data Pitch programme, firms increased their sales by a mean of €36,554, receivedinvestmentsofameanof€82,448,realisedefficienciesof€17,168andincreasedtheiremploymentbyanaverageof2employees.Onaverage,start-upsgenerated€599,432insalesand€338,862ininvestmentperGBofdatasharedwiththemthroughDataPitch.

BothROIandleveragedinvestmentarealreadysubstantialduringtheprogramme.Bytheendoftheprogramme,thetotalDataPitchresourcesalreadyattracted50%equivalentvaluefromotherinvestmentopportunities.Oneyearaftertheendoftheprogramme,ROIalreadyexceeded100%andleveragedinvestmentwasalreadyapproaching300%.

Table1 ReturnonInvestmentandleveragedinvestment;realisedandprojectedfigures

Duringtheprogramme

6monthsfollowingtheprogramme


Projectedannualfigureby2022

ReturnonInvestment

23% 91% 103% 459%

Leveragedinvestment 50% 82% 278% N/A

Note:dataon6and12monthsaftertheprogrammearebasedondatafromCohort1only.ThefiguresaccountforthisbyadjustingtheinvestmentprovidedthroughDataPitchbasedonthenumberofstart-upsforwhichdataisavailable.

Source:Bi-weeklymonitoringupdates,6monthsprogressupdate,12monthsprogressupdate,revenuegrowthprojection

Themajorityofstart-upswouldnothavebeenabletoaccessthesame,orsimilar,datawithoutDataPitch;thisisespeciallytrueforstart-upsworkinginthefinancialandmedicalsectors.Accesstodatadoesseemto influencetheabilityofstart-ups toattractadditional funding.Start-ups thatcouldaccessdataoutsideofDataPitchattracted,onaverage,€141,000moreinadditionalfundingthanstart-upsthatwouldnotbeabletoaccessdata.Start-upstypicallyhadfullcontroloverthedatawhenbuildingthesolution.Similarly,datawastypicallystoredunderthecontrolofthestart-ups.

Themajorityofstart-upsusedmachinelearningintheirsolution.Theoutcomestrackedduringtheprogrammeprovidesomeevidencethatusingmachinelearninghelpsattractinvestment.Thereisanimpressivedifferencebetweenstart-upsthatusemachinelearningandthosethatdonot.Start-upsthatdidusemachinelearningattracted,onaverage,€108,000moreininvestmentthanthosethatdidnotusemachinelearning.

Regardinggrowthopportunities,start-upsinthesectorchallengeshadhighergrowthexpectationsfortheirproduct.This(perceived)abilitytoscaleasolutionhasadistinctimpactontheabilitytoattractfunding;witheasierperceivedabilitytoscalebeingassociatedwithincreasesinadditionalfundingreceived.


Executivesummary

Successfulapplicantsreceived,onaverage,moreexternalfundingthanunsuccessfulapplicants(notincluding the €100,000 received through Data Pitch). Some applicants, both successful andunsuccessful,wereabletoattractsubstantialinvestmentsupwardsof€500,000.

Projectingperformanceoffundedstart-upsintothefuture,aforecastmodelpredictsthataveragerevenueforstart-upswillgrowfrom€147,723 in2019, to€833,555 in2022.Combinedwiththesuccess/failurerateofbusinesses,thisimpliesagrowthoftotalannualrevenueto€35,784,385from€6,896,000.Thisisequivalenttoagrowthof73%peryearupuntil2022.

There is considerable variation between outcomes for start-ups operating in the same sector.Tentatively, it seems that health start-ups still rely on investment, start-ups working in heavyindustryarematuringtobesales-drivenandstart-upsinthefinancialsectorshowmostemploymentgrowth.

Useofdata

DataPitchenabledstart-ups toaccessdata theywouldotherwisenothavebeenable toaccess.ThroughthedataproviderchallengesDataPitchopenedupdatafromdataprovidersthatwouldnotbeavailableforusebystart-upsotherwise.

Themainobjectivesofstart-upsinDataPitchwastomakepredictionswithoridentifypatternsindata.Themajorityusedmachinelearningtodothis,especiallystart-upsinthesectorchallenges.Start-upsratedtheirsolutionsasmoderatelyuniqueandinnovative.

Impactondataproviders

Theimpactondataprovidershasbeenreportedmostlyintermsofpromotingtheopeninnovationapproachwithintheproviderorganisations.Dataproviderssawthemselvesparticipating inopeninnovationinthefutureandseveralfelttheyhadabsorbedsufficientknowledgetodosowithoutexternalhelp.Severalremarkedespeciallythattheywerenowfurtheralongintheirjourneytomaketheirdatamoreaccessibleandunderstandableforthirdparties.Somedataprovidersmentionedthatmorecontactwithotherdataproviderswouldhavebeenuseful.CreatinganetworkofdataprovidersthatcontinuesaftertheaccelerationphasemightbeausefulextensionofaprogrammelikeDataPitch.

AlthoughDataProviderswentintoDataPitchwithabusinesschallenge,theysawDataPitchasalearningopportunityregardingOpenInnovationanddatasharing.Assuch,measuringquantitativeimpactswerenotprioritisedandgenerallynotavailable.However,onedataproviderestimatedthatthesolutiondevelopedwiththeirdatainDataPitchreducedthecostofaparticularbusinessprocessby35%.

Europeancollaboration

Data Pitch reached start-ups based in 13 countries, with cohort 2 broadening the reach of theprogramme. The United Kingdom represented a large proportion of all participants. The UK,togetherwithSpain,alsoprovidedthehighestnumberofparticipantsinvitedforinterviews.

24outof47(51%)start-upswerepartneredwithadataproviderlocatedinadifferentcountry.Inthedataproviderchallenge,allbutoneofthe18partnerships(94%)wasaEuropeancollaboration.

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Longertermimpacts

Takingintoaccountfactorsthatmaydecreasethenetpositiveimpact(programme‘additionality’)ofDataPitch,weconcludethattheimpactofDataPitchislikelytobestrictlypositive,especiallyonce longer-term impacts are taken into account. Data Pitch laid the groundwork for ongoingengagement in open innovation by somemajor European data providers,who appear ready topursuetheirown initiativesgoingforward;DataPitchhasthereforeactedasademonstratorfordata-drivenopeninnovation.

ThemainbenefitofDataPitchfordataproviderswasthelearningexperiencefromtheprogramme.It is therefore likely thatmore tangible impacts as a result of participating in data-driven openinnovationwillonlyemergeovertime.

Recommendations

¢ Sectorchallengesandproviderchallengesseemtohaveworkeddifferently.Forexample,start-ups in the data providers challenges and the sector challenges differed in themethodologiesused(sectorchallengestart-upsweremorelikelytousemachinelearning),thetypeofdataused(sectorchallengestart-upsusedvideodata,whereasdataproviderchallengestart-upsdidnot)andthewaydatawasstored(dataproviderchallengestart-upsmostlyfiles,whereassectorchallengestart-upsmostlyusedrelationaldatabases)asshowninsection3.6.Amoreexclusivefocusonmatchedchallengesislikelytoproducegreaterbenefitsandprovidesabettertestbedforthehypothesisthatreducingfrictionsinhibitingdatasharingfacilitatescanunlockdata-drivenopeninnovation.Theexperiencefor participants in the sector challenges more closely resembled that of a standardaccelerator,wherethestart-upisabletosourcedatafromexternalpartiesindependently.

¢ More resources to prepare and connect data providersmaymaximise the programmeimpact.Thiscouldinvolvea“Phase0”withaselectionprocessandan‘acceleration’periodfocusedondataproviderstopreparethemforworkingwithstart-upsontheirchallenges.Thelong-termbenefitsfordata-drivenopeninnovationcouldbecementedbythecreationofanetworkofdataprovidersthatpersistsaftertheendoftheprogramme.

¢ Despite the comprehensive support provided to the start-ups byData Pitch, themoremature start-ups seem to have performed better. A clearer focus on start-ups with‘acceleration-stage’maturity(provenabilitytodeliveranMVP)mayenhancetheoverallimpact.Partlythiscouldreflectthefactthatmoreexperiencedstart-upsarebetterableto select appropriate challenges both based on their technical viability, but alsostrategically, in terms of the applicability of an existing solution to a new market orindustry. In this regard,a stricter separationbetween ‘incubation-stage’ start-ups,whomaynotbeabletoproduceaworkingprototypebytheendoftheprogramme,andthemorematurestart-upsmaybecontemplated,soastoprovideeachtypeofstart-upwiththeoptimalsupportpackage,withmorestrategicadvicebeingmoreappropriateforthemorematurestart-ups.

¢ ThereissomeevidencethattheimpactofDataPitchwasstrongerinsectorswithhigherbarriers to data sharing (such as healthcare and finance, which data subjects see asparticularlysensitive).Forexample,asdiscussedinsection3.7.2,start-upsinhealthcareandfinanceweremorelikelytonotethat,withoutDataPitch,theywouldnotbeabletoaccess data. An ex-ante focus on such sectorsmay increase benefits. The selection ofsectorsshouldtakeintoaccounttheirspecificbarriersto,andenablersofdatasharing.For example, data-drivenopen innovation in the finance sector is supportedby strongregulatoryaction(OpenBanking,PSD2).


Executivesummary

¢ Theprogrammedesignandsetupshouldfacilitaterobustevaluationoftheprogrammeitself.Thisincludeshavingaclearevaluationstrategywithwell-definedsuccessmetricsforthe programme, and comprehensive baseline data collection. Making long-term datasharingobligatory forparticipantsmaybeconsidered.Whichdata is shared long-term,which may be a couple of years, depends on the success metric targeted by theprogramme,butmayincluderevenuesandemploymentfiguresofparticipants.

¢ More broadly, investment in further pilot projects is needed to develop the openinnovationmodelandtofindoutwhatworks.Parameterssuchastheselectionofstart-upsandtypeofsupportgivenshouldbecomparativelyanalysed.Anopportunityexistswith using other European incubators, such as the European Data Incubator. OtherEuropean programmes may be used to experiment with, for instance, on-boardingprocessesinsimilar,butnotidentical,circumstances.

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1|Background&context

1 Background&context

1.1 Realisingthevalueofdatathroughopenness

Theabilitytoexploitdataunderpinsmoreandmoreoftheeconomy.Datadrivesinnovationandproductivityimprovementsfororganisationsthatcollectorcontrolit,butalsothroughitsusebyotherparties.Often,theseputdatatoinnovative,unforeseenusesandcreatenewapplicationsthatinvolvenovelcombinationsofdatafromdifferentsources.

The key to a productive data ecosystem is to enable reuse of data and integration with otherdatasets inadatavaluechain,whichcan includeopen,closed,andshareddatasets.Thismeansrethinking access to data and creating opportunities for data owners to work with otherorganisations, inparticular innovativestart-upsandSMEsthatbringthecreativity tousedata innewwaystocreatevalue-addedproductsandservices.

The keyobjectiveofDataPitch is tounlock thepotential of data to solve critical challenges forindustry, public institutions, individuals, and society as a whole by matching data holders withinnovativestart-upsandSMEs.DataPitchthusaddressesoneofthemostfundamentalchallengesfacedbythedataeconomy:Howcandatabeusedwhereitismostuseful,andwhereitwillgeneratethegreatestpositiveimpact.

Traditionally,mostdatatendtobelockedinorganisational‘silos’1–limitingthescopeofwhatthedatacanbeusedfor,andbywhom(Ctrl-Shift,2018).DataPitch,bycreatingmutuallybeneficialdatapartnerships,andsupportingtheinnovativebusinessesthatcanexploitthem,canhelptoengenderamultitudeofeconomicandsocialbenefits.

Ineconomicterms,benefitscomefromtwosources:areductioninthecostofaccessingdata,andtheabilitytocombinedatafromdifferentsources.Thisinturnleadsto:

¢ externalbenefitsarisingfromincreaseduseofdata(i.e.,moredatabeingavailable,whichmayhavevaluethatisnotreflectedinthebenefitreceivedbytheorganisationsanddatasubjectsinvolvedintheexchange);

¢ Higherproductivity(i.e.,making iteasiertocombinedatafromdifferentsources lowersthe cost of producing data-enabled products and services; access to bigger and richerdatasetsallowsmoreprecisepredictionsthroughbettertrainedalgorithms,etc.);and

¢ innovation in the formofnewproductsandservices fromcombiningdata innewwaysacrossorganisationsandindustry‘silos’.

Accesstodatacanbeverycostly,especiallyfornew-to-marketSMEs2.Simplyidentifyingrelevantdatasets,identifyingtheirownersandnegotiatingaccesscanbeprohibitivelydifficultandresource-intensive.Forparticipatingstart-ups,accessingdatathroughDataPitchpartnershiptoavoidsuchcostsandreducestheoverallcostofdatausage,allowingthemtoimprovetheirtechnologyandtoproducemore(andbetter)output.

1DataSilosreferstosituationswheredatararelyleavestheorganisationthatcollectsitandisrarelyusedforpurposesotherthanwhatitwasfirstcollectedfor.2TheDataPitchprogrammewasaimedatstart-upsandSMEs.Sincethemajorityofparticipantswerestart-ups,thisreportreferstoallparticipantsassuch.Participantsmayalso,interalia,bereferredtoasfirms,businessesand(successful)applicantsdependingoncontext.



Weexpect thebeneficialeffectsofeasieraccess todata tobevisible in theperformanceof theparticipatingstart-upsintermsofinvestmentandrevenueachieved,aswellasthenumberofjobscreated.

Measuringtheseeffectsquantitativelyisoneofthekeyobjectivesofthisevaluation.Inaddition,qualitative assessment is required to ascertain whether the pilots delivered on expectations,especially in relation to innovation and the use of data; andwhat potential future impact fromprogramme results on the data economy can be expected. The evaluation also addresses thescalabilityofthesolutionsthatweredevelopedbyparticipantorganisations;providesprojectionsof impacts over a 3-year time horizon; and discusses data providers’ willingness to work withinnovatorsinthefuture.

1.2 Theopeninnovationframework

1.2.1 DataPitchasanimplementationofopeninnovation

“Openinnovation”isbuiltaroundtheinsightthattheinputsintotheinnovationprocess–suchasdata–arewidelydistributedacrosseconomicactors.Morespecifically,openinnovationreferstoorganisationsmakinggreateruseofexternalideasandtechnologiestoachievetheirownpurposes,and letting unused internal ideas and resources “go outside” for others to use (Chesbrough &Bogers,2014).

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Figure1 TheOpenInnovationmodel

Source:LondonEconomicsbasedonChesbrough&Bogers(2014),figure1.5.p.18.

Thecrucialinsightisthatthereisbothoutside-inmovementofknowledgeandsolutions(benefitingthedataprovider),butalsoinside-outmovement(benefitingthestart-up),andcoupledinnovation,resultinginclosepartnershipsbetweendataprovidersandstart-ups.

¢ Outside-In:involvesopeningupacompany’sowninnovationprocessestoexternalinputsandcontributions,forexamplethroughacquiringorsourcing

¢ Inside-Out:requiresorganisationstoallowunusedandunderutilisedideasandassetstogooutsidetheorganizationforotherstouseintheirbusinessesandbusinessmodels

¢ Coupled:involvescombininginflowsandoutflowsofknowledgetocollaborativelydevelopand/orcommercializeaninnovation(Chesbrough&Bogers,2014).

DataPitchisapioneeringprogrammeaimedatfacilitatingopeninnovationthroughdata-sharing.Coupled open innovation is the most characteristic mode for Data Pitch, which helped dataproviders to develop their challenges, and facilitated thematching between data providers andstart-ups. Data Pitch establishes a transnational data innovation ecosystem that createscollaborationbetweendataproviderson theonehand, and start-upswith fresh ideas for data-drivenproductsandservicesontheother.



Data Pitch creates a “cross-sectoral, secure data experimentation facility”, giving participants a“purposefulenvironmenttotestandnurturetheir ideas”,whilealsoprovidingthefunctionsofatraditional business accelerator, supporting participating businesses through funding, technical,legal,marketing,andcommercialassistance.

In addition, Data Pitch sets itself up as “an ecosystem enabler and digital innovation catalystthroughouttheEU”beyondtheaccelerationphasethatendedinNovember2019.ThismeansthatDataPitchparticipationcreatesknow-howintheparticipatingdataprovidersthatenablesthemtopursue open innovation, on their own or through other collaborative programmes, whiledemonstrating its usefulness through validated examples in the form of successful solutionsappearinginthemarket.

The Data Pitch approach is rooted in a commitment to openness in its various manifestations(software, partnerships, data, etc.) and to the Quadruple Helix model of broad stakeholderengagement. This model acknowledges the value of interactions among citizens, government,industry,andacademia,andaimstocreateimpactsbeyondimmediatecorporaterevenuestocreatevalueforprofit,people,andtheplanet.

Figure2 TheDataPitchapproachtoopeninnovation

Source:DataPitchgrantproposal

1.2.2 MeasuringinnovationinDataPitch

“Aninnovationistheimplementationofaneworsignificantlyimprovedproduct(goodorservice),orprocess,anewmarketingmethod,oraneworganisationalmethodinbusinesspractices,

workplaceorganisationorexternalrelations.”(OECD,2005)

Innovation in Data Pitch comes from combining datasets, skills and knowledge in new ways,transferringexistingproductsandservicestonewindustries,andbuildingnewdata-drivenbusinessmodels,ratherthantheinventionoffundamentallynewmethodsorfirst-of-a-kindproducts3.

3 Interviewsconfirmedthat thestart-upsallmove incompetitivemarketswhere thereare–sometimesclose–substitutes for theirsolutions

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Innovationisthereforenotmeasuredasadiscreteoutcome.Instead,innovationisembeddedintheprocessofselectingtheparticipatingstart-upsbasedontheirabilitytocreateasolutionthatisanew or improvedway of using data providers’ data to solve specific challenges, and ultimatelygeneratecommercialsuccessforthestart-ups.

1.3 Studyapproach

ThestudyexploresandevaluatestheimpactofDataPitchonthetwoparticipantcohorts4.Thestudyusesamixofquantitativeandqualitativedataobtainedfromprogrammedocumentation,publishedsources,andprimarydatacollection.

Themainquantitativedatacollectionforthisimpactassessmentwasasurveyofthe47start-upsfundedthroughDataPitch(successfulapplicants)5.41observationswerecollected.Thissurveywascomplementedwithasurveyofapplicantswhowerenotsuccessful(9observations)6.

Qualitativeinformationwascollectedthroughinterviewswithstart-upsanddataprovidersduringSeptember-November2019:

¢ 17interviewswithstart-ups7conductedoverthephoneandfacetoface,covering£ Background/motivationforparticipation£ Datareceived£ Useoffunds/othersupportreceived£ Thesolution£ Benefitsforthestart-up£ Reflectionsontheprogramme

¢ 6phoneinterviewswithdataproviders8,covering:£ Background/motivationforparticipation£ Dataprovided£ Othersupportprovided£ Benefitsforthedataprovider£ Reflectionsontheprogramme

WealsoheldconsultationswithDataPitchstaff(ODI,UniversityofSouthampton),inparticularonthebackgroundandobjectivesoftheprogramme,ontheapplicationandselectionprocess,andonthesupportprovidedtoparticipants.

Additional information was obtained from programme documentation provided by the ODI,includingparticipants’applicationsandevaluationscores,andbi-weekly,6-months&12-monthsprogressforms.Externaldatasourcesthatwereusedintheanalysisinclude:

4SeeAnnex1.5Thequestionnaireoftheparticipants’surveyisreproducedinAnnexA3.4.6Thequestionnaireoftheunsuccessfulapplicants’surveyisreproducedinAnnexA3.5.7TopicguideinAnnexA3.28TopicguideinAnnexA3.3


¢ Crunchbase¢ Orbis

A focused literature review was conducted about the impact and performance of technologyacceleratorsandincubators9.

Six case studies illustrate the achievements of selected start-ups in the provider and sectoralchallenges.Thecasestudiescoverdetailsonsolutionanddataused,aswellastheroleofthedataprovidersandbusinessoutcomes.

Thedatacollectedthroughthesurveys,togetherwithperformancedatacollectedbytheDataPitchconsortiumformedthebasisofacounterfactualanalysisofprogrammeimpacts(Section3.8)andasimulation-based estimation of future benefits (Section 3.9). A methodology for a quantitative(econometric)evaluationoftheprogrammeisdocumentedinAnnex4.

9AkeyreferenceisBoneetal.(2019).

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2|TheDataPitchopeninnovationprogramme

2 TheDataPitchopeninnovationprogramme

2.1 HowtheDataPitchmodelfitsinthestart-upsupportlandscape

DataPitchsharesmanycharacteristicswithincubatorsand,evenmoreso,accelerators.DataPitchisdistinguishablefromotheracceleratorsthroughitsfocusonopeninnovationanddata.

DataPitchsharesmanyofthecharacteristicsofincubatorsandaccelerators,withthecohort-basedapproachandfixeddurationplacingitmoreintheacceleratorspectrum.

Figure3 Definingcharacteristicsofincubatorsandaccelerators

Source:Boneetal.(2019),Figure1

AccordingtoMiller&Bound(2011),anacceleratorprogrammehasfivemainfeatures:

¢ Applicationprocessopentoall,yethighlycompetitive.¢ Provisionofpre-seedinvestment,usuallyinexchangeforequity.¢ Afocusonsmallteamsnotindividualfounders.¢ Time-limitedsupportcomprisingprogrammedeventsandintensivementoring.¢ Cohortsor‘classes’ofstart-upsratherthanindividualcompanies.

Likeotheraccelerators(andunlikeincubators),DataPitchoffereditsservicesthroughanintensivecohort-basedprogrammeoflimitedduration(6months,inlinewiththe3-12monthsrangeusuallyseenforaccelerators)afteranopencompetitiontoentertheprogramme10.Likeotheraccelerators,DataPitchalsofocusedonservicesoverphysicalspace.However,unlikethemajorityofaccelerators,DataPitch’sdirectfundingofparticipatingstart-upsdoesnottaketheformofequityinvestment.AnotherdistinguishingfeatureofDataPitchisitsvirtualsetup.

10SeeBoneetal.(2017),Clarysseetal.(2015)



Accelerator programmes are set up to benefit start-ups primarily, but often also have widereconomicdevelopmentobjectives.Miller&Bound(2011)liststhebeneficiariesofacceleratorsasfollows:

¢ Angelinvestors£ Reducetheneedforduediligenceasthatroleisperformedbyaccelerator.£ Reducethecostandtimerequiredtofindnewcompaniestoworkwith.£ Abilitytomeetotherinvestorsandcompanyfounders.

¢ VentureCapitalFirms£ Improvedealpipeline,creatingmorehigh-qualitystart-ups.£ Getfirstsightofnewtechnologyandabilitytomaptrendsinstart-ups.£ Abilitytomeetotherinvestorsandcompanyfounders.

¢ LargeTechfirms£ Talentscoutingfornewemployees.£ Newcustomersfortheirplatformsandservices.£ Associatetheirbrandwithsupportingnewbusinesses.

¢ Otherstart-upfounders£ Talentscoutingfornewemployees.£ Rapidlycreateaveryhigh-qualitybusinessnetwork.£ Meetcustomersandlater-stageinvestorsthatmightberelevanttotheirbusinesses.

¢ Serviceproviders(e.g.accountancyfirms,lawfirms,PRfirms)£ Newcustomersintheformofthestart-upstheacceleratorssupport.

WhileDataPitchinitsacceleratorroleislikelytoproducethesamebenefits,itisdistinguishablefromotheracceleratorsthroughitsfocusonopeninnovation,whichshapedtheprogrammedesignaswellastherecruitmentmechanism.Furthermore,DataPitchalsocontributedtothewiderdatainnovationecosystembyprovidingaframeworkandprovencasestudiesofopeninnovationwhichmayinspireotherdataholderstoengagewithopeninnovation.

ThefollowingsectionprovidesadescriptionoftheDataPitchprogramme.

2.2 Descriptionoftheprogramme

DataPitchismorethananaccelerator.Itcombinesathoroughpreparationphasetoidentifysuitablechallengeswithtechnicalandbusinesssupport,networkingopportunities,andfunding,toachieveimpactthroughopeninnovation.

DataPitchisanEU-fundedprogrammewiththeaimoffosteringopeninnovationforstart-upsandSMEs thatworkwith data.11 The programme consists ofmultiple componentswhich all help tofacilitatethisacceleration:12

¢ Agrant(equity-freeinvestment)ofupto€100,000;¢ introductionstoinvestors;

11https://datapitch.eu/wp-content/uploads/2018/07/Guide-for-applicants-call-2018vfq.pdf12https://datapitch.eu/about-us/

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¢ 6-monthbusinessacceleratorprogrammewithODIandBeta-I;¢ peer-networkingandsupportviameetups;¢ accesstotrainingmaterialsandwebinarsbyDataPitchexperts;¢ legalcounselforIPprotection;¢ draftingofadatasharingagreementbetweenDataPitchpartners.

ThekeydistinguishingfeatureofDataPitchisthatitmatcheddataprovidersthatpossesseddatathattheywantedtouseforopen innovation,withstart-upswiththewherewithaltoexploitthisdata.Start-upsinthe“provider”challengeswerematchedbytheDataPitchconsortium,whereasstart-upsinthe“sector”or“open”challengeshadtofindtheirowndataprovider.Allparticipantshadtohaveapartnereddataprovider.

Data Pitch aimed for the programme to involve less arduous paperwork (akin to the standardacceleratorprocess)thanausualEUfundingcompetition13withtheapplicationformonlyrequiringdetailsonbasiccompanyinformation,detailsabouttheirteamandtheirideaproposal.

2.3 Pre-selection

The challenges for the Data Pitch calls were chosen based on extensive consultationwith dataproviders, industry experts and scientific communities. The resultwas a collectionof challengescreatedwiththeaimofachievingthehighestlevelsofimpactinsectorsidentifiedfortheirpotentialforimpact,accesstodataandopportunityforadditionalnetworkeffects.

Priortotheacceleratorperiod,DataPitchconductedconsultationswithkeystakeholders(suchasdataproviders,industryexpertsandscientificcommunities)toidentifythemosteffectivedomainsforthesechallengestoaddress.Thechallenges(bothdataproviderandsectoral)werecreatedwiththefollowingconceptsandideas14:

¢ Be inclusive and engaged – provide a context and forum for fostering discussion andcollaboration;

¢ Targetmarketfailures;¢ Haveclearrequirements–ensureresultsaremeasurable;¢ Besolutionagnostic–toallowforparticipantstoorganicallydevelopasolutionwhichis

notconstrainedbyoverlyprescriptive,technicalandnon-technicalrequirements.

ForCall1,thefinalchallengeswerewithinthesectorsof15:

¢ Retail(dataprovider:Sonae);¢ DataAnalytics(dataprovider:SpazioDati);¢ Sports&Recreation(dataprovider:imin);¢ Transport(dataprovider:DeutscheBahn);¢ DataManagement(dataprovider:Uniserv);¢ Health&Wellness;

13CorrespondencewiththeOpenDataInstitute.14DataPitchdeliverableD3.4:FirstDataPitchConsultations15https://datapitch.eu/challenges/challenges2017-2/



¢ EmpoweringUsersOnline;¢ LifelongLearning;¢ Living;¢ SmartManufacturing;¢ Tourism;¢ Openinnovation(openchallenge).

ThesesectorswereidentifiedbyDataPitchasareasinwhichsolutionscouldprovidehighimpact;sectorswhichalsocontainedcloseddatasetswhichwerenotrestrictedbytightregulationaswellassectorswithopportunitytoleverageadditionalnetworkeffects(bytargetingtrackswhichareapriorityforlocalandinternationalgovernments).TheseoverlapwithareaswhichtheBigDataValueAssociationPublic-Private-Partnershiphavealso identifiedasareas inwhichdata innovationcanprovidesignificantgains.16

These challenges were created as a result of consultations carried out by Data Pitch. Thecomponentsof theseconsultations includedasurvey,aworkshopand interviewswith identifieddata providers. From these interviews, specific challengeswere created in conjunctionwith theparticipatingdataproviders.

ForCall2,DataPitchidentifiedtheBDVAareaswhichwereeithernotcoveredinCall1,orwereonlypartiallyaddressed.Thisledtoanincreaseinthenumberofchallengesinadditionalsectors.Thefinalchallengeswerewithinthedomainsof:17

¢ Pharmaceuticals;¢ Automotive;¢ Energy;¢ Finance;¢ Telecoms;¢ Privacy&ConsentControl;¢ SmartTransport;¢ PersonalisedEntertainment(dataprovider:AlticeLabs);¢ TextMining&Analytics(dataprovider:Bloomberg);¢ SmartManufacturing(dataprovider:GreinerInternationalPackaging);¢ SustainableFoodSupplyChain(dataprovider:GROW);¢ CustomerNeedsPrediction(dataprovider:KonicaMinolta);¢ Healthcare(dataprovider:JosédeMelloSaúde);¢ MultimodalTransport(dataprovider:MASAI);¢ WeatherandClimateChange(dataprovider:METOffice);¢ OpenChallenge.

16ThekeyverticalsoftheBigDataValueAssociationare:Environmentalandgeospatialdata;Energy;Mobility,transportandlogistics;Manufacturingandproduction;Publicsector;Healthcare;MediaandContent;Finance;Telecoms;Retail;Tourism.17https://datapitch.eu/challenges/challenges-2018/

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AnOpenChallengewasalsoissuedinCall1and2tocaptureanynovelideasthathadnotbeenpre-identifiedfromthisconsultationandpre-loadingphase.

2.4 TheDataPitchaccelerator

The Data Pitch accelerator was a 6-month accelerator programme which provided successfulapplicantswithaccesstofunding,mentorsandadvisors.Projectmonitoringarrangementsinvolvedacombinationofmeetingswithadvisors,workplansanddeliverablesatsetmilestones.

The Data Pitch programme involved a 6-month accelerator programme delivered by Data Pitchpartners Beta-i and the ODI (Open Data Institute). Beta-i also runs several open innovationprogrammesinseveralsectors,includingsmartcities,tourism,fintechandhealthcare.Oneoftheseprogrammes includes the LisbonChallenge, a Portuguese acceleratorwhich has fundedover 70start-ups,which have collectively raised over €75million.18 Other examples of programmes ranincludetheBlueTechAccelerator,TheJourney&FreeElectrons.TheODIhadpreviousexperiencewiththeOpenData Incubator forEurope(ODINE)project;avirtualacceleratorwhich funded57companies.

At the beginning of this accelerator, these start-ups created a workplan and budget for theirprojects. These workplans would serve as a guideline for each start-ups major milestones,deliverables and goals they would aim to achieve. These workplans, which were created innegotiationwithDataPitchandtheirdataproviders,werenotexpectedtobefollowedstrictly;DataPitchacknowledgedthatunexpectedchallengesandopportunitiescouldaffecthowcloselythesestart-upscouldabidebytheseinitialplans.

During the programme, the start-ups had access to a mentor, whom they could meet withthroughouttheaccelerationperiodforguidanceandsupport;over70%ofthesementorsinvolvedinthefirstcohorthavekeptintouchwiththeirallocatedstart-upsaftertheprogrammeended19.These mentors included academics at the University of Southampton, and other professionalsspecialisedintechnology-relatedtopicssuchasdatascienceandAI,aswellasexpertsinbusiness-related fields such asmarketing, sales and consumer behaviour20.Meetingswithmentorswerearrangedbythecompaniesandmentorsthemselvesonanad-hocbasis21.

Alongside thesementors, each start-up was also assigned an advisor. These advisors were thecompany’s first point of contact for concerns, queries and general comments. The aim of theadvisors was to provide support, guidance and help with setting interim goals during theaccelerationperiod.Eachcompanymetwiththeiradvisorroughlyeverytwoweeks22.Additionally,amonthlygroupsessionwouldbearrangedinwhichanadvisorandalltheirmatchedcompanieswouldmeettoidentifysharedproblemsandsolutions.

Atthesebi-weeklymeetings,thesecompaniesagreedwiththeirmentoronnextstepsandcurrentprogress.Furthermore,bi-weeklyprogresswastrackedbytheDataPitchconsortium.

18http://www.lisbon-challenge.com/#program19https://datapitch.eu/news/make-a-difference-become-a-mentor/20https://datapitch.eu/network/mentors/21https://datapitch.eu/wp-content/uploads/2018/08/PUBLIC_DataPitch_.5.1-Data-Pitch_IncubationServices-1.pdf22ibid.



Inadditiontotheregularupdates,theprogrammecontainedthreemilestonetouchpoints.Foreachmilestone,anumberofactivitiesandperformancegoalswereagreedwiththestart-upsaspartoftheinitialworkplanformulation:23

¢ Activities:Whatthecompanywillachieveinthemilestone,e.g.‘Writethecodefordatapre-processingpipelineinPythonandScala’

¢ Keyperformanceindicators(KPIs):Aquantitativeoutcometobeachieved,e.g.‘Coldcallsto50potentialcustomers’

¢ Deliverables: Outputs and evidence of progress towards completing these KPIs, e.g.Businessplans,interviewtranscripts,demos.

Paymentofthegrantwasdistributedovertheaccelerationphaseasfollows:30%ofthegrantwaspaidatthestartoftheaccelerator,30%waspaidafterthe4-monthmilestonereview,and40%waspaidafterthefinalmilestonehadbeenmet.24

2.5 Additionalbenefits&programmeservices

Tohelpstart-upsmaximisetheirimpact,DataPitchprovidedadditionalbenefitswhichwerechosenwith the aim of helping start-ups deal with business and technical challenges. These benefitsinvolvedsoftwareperks,workshopsandnetworkingevents.Thesupportpackagewastailoredtotheneedsofeachstart-up.

DataPitchprovidedseveraladditionalservices.TheseservicesweredefinedandcuratedfromtheDataPitchconsortium’scombinedknowledgefromODINE,themultitudeofinnovationprogrammesran by Beta-i and from the collective experience of the other consortium organisations. Theseservicesincluded:25

¢ Workshops£ 10workshopswereheldpercohort,eithervirtuallyorinperson,anddeliveredtraining

on key themes relevant for the Data Pitch cohorts; themes included, inter alia,marketing,design,marketfitandEUGeneralDataProtectionRegulation(GDPR).

£ Workshopsweredeliveredby representatives fromeachmemberof theDataPitchConsortium,orbyprofessionalsfromexternalorganisations.

¢ Founderstories£ Founder stories refer to a series of sessionswith foundersof established start-ups.

These ‘intimate private sessions’ allowed Data Pitch start-ups to gain a deeperunderstandingandfirst-handexperienceofstartingandscaling-upacompanyinthetechnologyindustry.

¢ Networking£ Start-ups had access to the start-up network of ODI and Beta-i. The aim of this

networkingwastobothincreaseexposureandallowthesestart-upstomeetwithkeystakeholders,suchasinvestors,clientsandpotentialfuturepartners.

23CorrespondencewiththeDataPitchconsortium.24https://datapitch.eu/wp-content/uploads/2018/08/PUBLIC_DataPitch_.5.1-Data-Pitch_IncubationServices-1.pdf25ibid.

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£ Other networking opportunities sharedwith start-ups included various events thatprovidedopportunitiestopitchandtomeetclientsandinvestors.

¢ Perks£ ProvisionofperksanddiscountssuchasAmazonWebHostingcreditsandaccessto

tools such as Hubspot for Start-ups, Segment (analytics platform) & TestArmy(software testing tools). These perks were provided according to the needs of thecompanies.

2.6 Selectionprocess

OverthetwoDataPitchcalls,47start-ups,outofatotalnumberof239applicants,weresuccessfulingainingadmissiontotheprogramme.22ofthedifferentchallengeswerematchedwithstart-ups,withonedataproviderchallengeineachcallleftunmatched.18start-upswereselectedforfundinginCall1andthisincreasedto27inCall2.

ThereweretwoDataPitchcalls.Thefirstcallwasissuedonthe1stJuly2017andthesecondayearlater,onthe2ndJuly2018.BothcallsusedtheF6Splatformtoreceiveandmanageapplicationsfromstart-ups.

Applicants had to be individual companies (no consortia) and qualify as SMEs at the time ofsubmission26.Additionally,theyalsohadtobeestablishedandworkinginanEUmemberstateoranon-EUHorizon2020partnercountry.

Applicants had to submit a short proposal (around 5 pages), supporting documents and basicinformationaboutthecompany.Additionally,applicantsalsohadtoprovidea12-slidepitchdeckanda1-minutevideoexplainingwhyDataPitchshouldfundtheirteams.27

Thecallstooktheformatofalistofchallenges28.Applicantswereinvitedtoaninterviewbasedonthestrengthsoftheirapplications.Threeexperts(asector,businessandtechexpert)werepresentattheseinterviews.Furthermore,dataproviderswerealsopresenttojudge‘their’challenge.TheseexpertsquestionedtheapplicantsovertheirproposalsandreturnedeitheraYesorNoverdict.

Table2 OutcomeofJudgeVerdicts

JudgeVerdicts OutcomeYes3times FundinggrantedYes2timesorNo2times ApplicationreviewedfurtherNo3times Fundingrejected

Thefigurebelowshowstheflowofapplicantsthroughtheselectionstagesforbothcohorts.

26ToqualifyasanSMEundertheECdefinition,afirmmusthaveastaffheadcountoflessthan250andaturnoveroflessthanorequalto50millioneuros.Seehttps://ec.europa.eu/growth/smes/business-friendly-environment/sme-definition_en.27D4.3SummaryofRound2ODI28SeeAnnex1.



Figure4 SelectionofDataPitchparticipants

Note:Thedatainthisdiagramreferstobothcohort1and2companies

Source:LondonEconomicsbasedondataprovidedbyDataPitch

2.6.1 Cohort1:process&outcomes

Data Pitch’s publication strategywas to inform ‘local technology hubs’ of the competition,whowould thendisseminate this information to start-ups andSMEs in thearea.29 These ‘local hubs’includeorganisations suchasaccelerators, incubatorsand community spaces.Additionally,DataPitchscoutedforapplicantsatseveraleventswhichtookplaceinLondon,Lisbon,Berlin,Stockholm,Copenhagen,Cologne&Munich.

Thefirstcallcontained12challenges;5dataproviderchallenges(withadataproviderarrangedbyDataPitch),6sectorchallenges(requiringapplicantstofindadataprovider)and1‘open’challengeallowingparticipantstoprovideaproposalwithoutsectororspecificchallengerestrictions.

142applicationswerereceived,112ofwhichwereeligibleforfunding.These112applicationswerescoredtodeterminethefinalsetof57applicantswhowereinvitedtointerview.ThegeographicalspreadoftheapplicantscanbeseeninFigure1.

29CorrespondencewiththeOpenDataInstitute.

Totalcompanies:239

Eligiblecompanies:182

Ineligiblecompanies:57

Interviewedcompanies:115

Fundedcompanies:47

Companiesnotfunded:68

Rejectedfrominterview:67

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Figure5 MapofDataPitchCall1Applicants

Note:DataPitchinfographicrecreatedbyLEusingmapchart.net

Source:DataPitchdeliverableD.4.2.:SummaryofRound1

Applicationswerejudgedusingthreecriteria:

¢ strengthandnoveltyoftheidea(idea);¢ valuepropositionandpotentialscale(impact);and¢ theteamandbudget(team).

Thesethreecategorieswerescoredoutof30,30and40respectively.Eachapplicationwasreviewedindependently by two reviewers. If the scores for an application differed drastically betweenreviewers,areviewboardwoulddiscussandresolvediscrepancies.30Anapplicationhadtoscoreover60tobeinvitedtointerview.31TheseinterviewstookplaceinLondonandlasted45minutes.Outcomesoftheinterviewsdeterminedwhetherastart-upreceivedfunding.

18start-upsreceivedfunding;13completedsectoralchallengesand5completedadataproviderchallenge.Start-upswerematchtochallengesasfollows:32

¢ Health&wellness[sectorchallenge](4)¢ Smartmanufacturing[sectorchallenge](4)

30CorrespondencewiththeOpenDataInstitute.31DataPitchdeliverableD.4.2.:SummaryofRound132Ibid.



¢ Tourism[sectorchallenge](3)¢ Datamanagement[dataproviderchallenge;dataprovider:Uniserv](2)¢ Dataanalytics[dataproviderchallenge;dataprovider:SpazioDati](2)¢ Transport[dataproviderchallenge;dataprovider:DeutscheBahn](1)¢ Retail[dataproviderchallenge;dataprovider:Sonae](1)¢ Empoweringusersonline[sectorchallenge](1)

2.6.2 Cohort2:process&outcomes

Call 2 was similar to the first but increased the number of challenges to 16 (8 data providerchallenges,7sectorchallengesand1openchallenge).Thiscallfocusedonreachingcountrieswhichwereunder-representedincall1.ThismeantthatDataPitchscoutedateventsincountriessuchasPoland,NorwayandMalta(alongsideeventsinPortugal,Germany,TheNetherlandsandtheUK).ThegeographicalspreadofapplicantscanbeseeninFigure6.

Figure6 MapofDataPitchCall2Applicants

Note:DataPitchinfographicrecreatedbyLEusingmapchart.net.

Source:DatapitchdeliverableD.4.3.:SummaryofRound2

97 applications were received and 70 were eligible. These 70 were reviewed by a team of 8reviewers(eachapplicationbeingreviewedtwice)toarriveatafinallistof58whowereinvitedforaninterview.Thesamecriteriausedincall1(idea,impact&team)wasalsousedincall2.

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29start-upswereselectedforfunding,andmatchedasfollows:33

¢ Smartmanufacturing[dataproviderchallenge;dataprovider:Greiner](5) ¢ Customerneedsprediction[dataproviderchallenge;dataprovider:Konica](3)¢ Openinnovationchallenge(3)¢ Energy[sectorchallenge](3)¢ Personalisedentertainment[dataproviderchallenge;dataprovider:AlticeLabs](2)¢ Weatherandclimatechange[dataproviderchallenge;dataproviderMEToffice](2)¢ Finance[sectorchallenge](2)¢ Privacy&consentcontrol[sectorchallenge](2)¢ Smarttransport[sectorchallenge](2)¢ Textmining&analytics[dataproviderchallenge;dataprovider:Bloomberg](1)¢ Sustainablefoodsupplychain[dataproviderchallenge;dataprovider:GROW](1)¢ Healthcare[dataproviderchallenge;dataprovider:JosédeMelloSaúde](1)¢ Pharmaceuticals[sectorchallenge](1)¢ Automotive[sectorchallenge](1)

Forcall2,agreaternumberofapplicantsreceivedfunding.Additionally,fourapplicantsapplyingtocall2previouslyappliedtocall1;twoofthesefourcompaniesweresuccessfulinreceivingfundingin call 2. This success could be due to new challenges which were more aligned with thesecompanies.Thissuccessmayalsobeattributedtopossiblelearningeffectsoccurringbetweenthetwocalls,orduetothechangesinthepre-selectionphasebetweencall1and2.

In addition to the wider range of challenges for cohort 2, call 2 also allowed applicants to beinterviewed remotely. This ensured that under-resourced, or newly established, start-ups couldensurethatboththeirtechandmanagementpeoplewereattheinterviews.Thiswouldallowforinterviewquestions tobe answered inmoredepth, aswell as allowDataPitch to gain abetterunderstandingofapplicantswhenmakingtheirdecisions.

33DatapitchdeliverableD.4.3.:SummaryofRound2


3|Programmeperformance

3 Programmeperformance

3.1 Performanceobjectives

Asuccessfulprogrammewillshowevidenceofsustainablegrowthandinvestment,increaseinbothsectoralandgeographicalecosystems,andgenerationofdata-drivenusecases.

TheDataPitchconsortiumhasdefinedthreeoverallindicatorsalongwhichtomeasurethesuccessoftheDataPitchproject:34

¢ “Asizablenumberofdata-drivenbusinessesareestablished,andseverallighthousestart-upideasreceivewidepublicattention”;

¢ “Thereisanexpandingecosystemoforganisationscollaboratingtooffertechnologyandmethodologicalsupportfordatainnovationlabs,acrosssectorsandborders”;and,

¢ “Theconceptofdata-drivenbusiness,includingsocialentrepreneurship,iswelldevelopedand receives substantial investment inenterpriseecosystems, investor circles,andotherfunds.”

Therefore,asuccessfulprogrammewillhaveevidenceof:

¢ establishment and expansion of companies which have sustainable growth and arereceivingsubstantialinvestments;

¢ an increase in both sectoral and geographical ecosystems and evidence of cross-border/sectorcollaboration;and,

¢ impactgeneratedbydata-drivenbusinessesandbig-datausecases.

3.2 Sectorsofactivity

Manyoftheparticipatingstart-upsworkedacrosssectors,withtheaimtoenternewsectorsbeingakeymotivator forparticipating inDataPitch.A concentrationofparticipating start-ups canbefoundinthehealth,financialandtransport/mobilitysectors.

Theparticipantscoverawidevarietyofactivitiesandmanyoperateacrosssectors.Moreover,asyoungcompanies,manyhavenotyetreachedastagewhereitispossibletoclearlyidentifycoreactivities or sectors of operation. In fact, entering new sectors has been a key motivator forparticipating inDataPitchforanumberofstart-ups.Asaresult,thisreportdoesnotattempttoassignstart-upstostandardindustryclassifications.Instead,weuseself-reportedtargetmarketsasthemostusefulsectoralindicator.

Table3 Start-upsprimarycustomerforDataPitch-enabledsolution

Clinics UtilitiesandownersoflargeassetsHospitals OffshorewindandenergysectorMedicalstaff HeavyindustryEmergencyservicesandlocalauthorities FlightcompaniesandmountainstationsLocalauthorities BuildingmanagersTransportationandlocalauthorities Retailers

34DataPitchGrantAgreement.SharedwithLondonEconomicsbytheconsortium.

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Highwaymanagers OnlineretailersVehiclefleetmanagersandautomotiveindustry FoodserviceindustryCarmanufacturersandinsurers FoodcompaniesBankandinsurancecompanies TourismFinancialinstitutions Customer-facingbusinessesBankandotherlenders BusinessanalystsBanksandregulators DatascientistsandengineersFinance,insuranceandmanufacturing PayTV/CablecompaniesInvestmentbanks/largecompanies PersonaldatamanagersandconsumersLargecompanies ConsumersBusinesseswithhighcustomervolume Operatorsofindustrialmachinery

Source:Successfulapplicantssurvey

Thehealth,financialandtransport/mobilitysectorsstandoutasbeingthefocusofseveralstart-ups.However,manyofthesolutionsareinherentlycross-sector.Overall,thefocusismostlyB2BratherthanB2C.Thisreflectsthefactthatmanyofthechallengeswereformulatedforthedirectbenefitofthedataproviders,whichmakesforaninherentB2Bfocus.

3.3 Geographicaldistribution

Data Pitch reached start-ups based in 13 countries, with cohort 2 broadening the reach of theprogramme. The United Kingdom represented a large proportion of all participants. The UK,togetherwithSpain,alsoprovidedthehighestnumberofparticipantsinvitedforinterviews.

Start-ups incohort1werebasedacross10countries,whereas start-ups incohort2werebasedacross13countries.Call2reachedadditionalcountriesinEasternEurope(suchasLatviaandSerbia).

UK represents 26% (cohort 1) and 20% (cohort 2) of funded companies. The decrease in theproportionsuggeststhattheattempttoincreaserepresentationacrosstheeligiblecountrieswassuccessful.35

Table4 Geographicalspreadoffundedcompaniesbycohort

Country Numberoffundedapplicantsincohort1 Numberoffundedapplicantsincohort2UK 5 7Germany 3 6Ireland 0 3France 2 2Spain 2 2Greece 1 2Portugal 1 1Denmark 2 1Netherlands 1 1Italy 1 0Romania 0 2Serbia 0 1

35CorrespondencewiththeOpenDataInstitute.



Country Numberoffundedapplicantsincohort1 Numberoffundedapplicantsincohort2Latvia 0 1Total 18 29

Source:DataPitch36

Table5showsthegeographicalspreadofsuccessfulandunsuccessfulapplicants.Notablepointsare:

¢ Applicants from Italyhadan8%successrateofachieving funding.Only1outof the12interviewedweresuccessful.

¢ 13countrieswereunsuccessfulatobtainingfunding.¢ TheUKandSpainhadthehighestnumberofinterviewsbutrelativelylowsuccessratesat

27%and17%respectively.¢ Germany,withthethirdhighestnumberofinterviewsat15,hadthethirdhighestsuccess

rateat60%.This resulted in9,or justunder20%,of the total fundedcompaniesbeingbasedinGermany.

Table5 Geographyofsuccessfulandunsuccessfulapplicants

Country Unsuccessfulfunding(total)

Unsuccessfulafterinterview

Successfulfunding

Proportionoftotalapplicantssuccessful

Totalapplicants

AT 3 2 0 0% 3BE 1 0 0 0% 1CH 1 0 0 0% 1DE 6 5 9 60% 15DK 2 1 3 60% 5EL 2 1 3 60% 5ES 20 10 4 17% 24FI 2 0 0 0% 2FR 6 3 4 40% 10GR 1 1 0 0% 1HR 1 1 0 0% 1HU 2 2 0 0% 2IE 2 0 3 60% 5IL 1 1 0 0% 1IT 11 8 1 8% 12LT 1 1 0 0% 1LV 0 0 1 100% 1NL 1 1 2 67% 3PL 1 1 0 0% 1PT 6 4 2 25% 8RO 0 0 2 100% 2RS 1 1 1 50% 2SE 3 1 0 0% 3

36Thesefigurescanbeaccessedhere:https://datapitch.eu/data-pitch-start-ups/

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Country Unsuccessfulfunding(total)

Unsuccessfulafterinterview

Successfulfunding

Proportionoftotalapplicantssuccessful

Totalapplicants

SK 2 1 0 0% 2SL 2 0 0 0% 2UA 1 1 0 0% 1UK 33 19 12 27% 44N/I[a] 2 2 0 0% 2Total 114 67 47 29% 161

Note:NumberofinterviewedcandidatesisslightlylowerthanDataPitchreportednumberduetosomecandidatesinterviewinginbothcohorts.[a]Countrycouldnotbeidentified.Source:LondonEconomicsanalysis

3.4 Europeancollaboration

24outof47(51%)start-upswerepartneredwithadataproviderlocatedinadifferentcountry.Inthedataproviderchallenge,allbutoneofthe18partnerships(94%)wasaEuropeancollaboration.

AnobjectiveofDataPitchistofostercooperationbetweenfirmsfromdifferentpartsofEurope.Aspartoftheacceleratorprogramme,somestart-upswerepartneredwithdataprovidersfromotherMemberStates.Thetablebelowshowstheextentofcross-bordermatchingwithinDataPitch.

Table6 Countriesofstart-upsmatchedwithdataproviders

DataproviderCountryofDP

DE EL ES LV NL PT RO RS UK Total

AlticeLabs PT 0 0 0 0 1 0 0 0 1 2Bloomberg US 0 0 0 1 0 0 0 0 0 1DeutschBahn DE 0 0 0 0 0 1 0 0 0 1GreinerPackaging AT 1 1 1 0 0 0 0 0 1 4GrowObservatory UK 0 1 0 0 0 0 0 0 0 1JosédeMelloSaúde PT 0 0 0 0 0 0 1 0 0 1KonicaMinolta JP 1 0 0 0 0 0 1 1 0 3MetOffice UK 0 0 1 0 0 0 0 0 1 2Sonae PT 0 0 0 0 0 0 0 0 1 1Uniserv DE 0 0 1 0 1 0 0 0 0 2Total 2 2 3 1 2 1 2 1 4 18

Note:Rednumbersindicatedomesticpartnerships.Source:LondonEconomicsanalysis

Inthedataproviderchallenges,only1(outof18)start-upwasmatchedwithaprovideroperatingin the same country (MetOffice). Thismeant that17 start-upswerematched across EuropeancountriesaspartofDataPitch.DataPitchsucceededinfacilitatinginternationalcollaborationacrossEurope.

Table7 Countriesofstart-upsinthesectorandopenchallengesmatchedwithdataproviders

CountryofDP CH DE DK EL ES FR IE IT UK

Total

CH 0 1 0 0 0 0 0 0 0 1



CountryofDP CH DE DK EL ES FR IE IT UK

Total

DE 0 4 0 0 0 0 0 0 0 4DK 0 0 1 0 0 0 0 0 0 1EL 0 0 0 1 0 0 1 0 0 2ES 0 0 0 0 0 0 0 1 0 1FR 0 1 0 0 0 3 0 0 0 4IE 0 0 1 0 0 0 1 0 1 2IT 0 0 0 0 0 0 0 0 0 0UK 0 0 1 0 0 0 0 0 4 5Total 0 6 3 1 0 3 2 1 5 21

Note:Dataproviderdetailsforthreestart-upscouldnotbelocated,anadditionalstart-updidnothaveapartnereddataprovider.Rednumbersindicatedomesticpartnerships.

Source:LondonEconomicsAnalysis

For start-ups in the non-data provider challenges, 14 enteredData Pitchwith a partnered dataproviderwhichwerelocatedinthesamecountry;7start-upswerematchedacrossinternationalborders.Betweenthedataprovider,sectorandopenchallenges,24start-upswerematchedwithanon-localdataprovider.

3.5 Start-upperformance

During theprogramme,start-upsonaverage increasedsalesby€36,554, received investmentof€82,448 and increased employment by 2. Cohort 1 generated, on average, sales of €134,379,investmentof€71,334andadditionalemploymentof1inthe6monthsfollowingtheprogramme.By theendof theprogramme, theReturnon Investmentwas already23%, increasing to91%6monthsaftertheendoftheprogramme.

Throughouttheprogramme,DataPitchaskedstart-upstoupdatetheirprogressusingthefollowingmetrics:

¢ totalamountofsales;¢ investments;¢ realisedefficiencies;37¢ changeinemployment;and,¢ whethertheapplicantislookingforfurtherfunding.

DataPitch required start-ups to record thesemetrics inbi-weeklyprogress reportsover the six-monthacceleratorperiod.Table8presentsahigh-levelsummaryofthesedifferentmetrics.

Table8 Summaryofstart-upprogressduringtheDataPitchprogramme

Indicator Observations Mean Minimum Maximum TotalSales 47 38,682 0 420,000 1,818,045Investments 47 82,448 0 1,600,000 3,875,060Efficiencies 47 17,168 0 318,000 806,890Employment 47 2 -1 12 114

37EfficienciesrealisedreferstoaloosemetricdesignedtocapturetimesavingthattheDataPitchprogrammeallowedastart-uptopassontotheirclients.Thismonetaryvaluedoesnotrefertomonetaryvaluesavedforthestart-up,butinsteadfortheirclients/customers.

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Note:Valuesareineuros(otherthanEmployment).InvestmentsincludeGrants,BAs,VCsetc.Bi-weeklymonitoringupdatescoverapproximately24weeks.

Source:Bi-weeklymonitoringupdates

During the6-month accelerationphaseof theDataPitchprogramme,on average across bothcohorts, start-ups increased their sales by €36,554, received investments of €82,448, realisedefficienciesof€17,168andincreasedtheiremploymentby2employees.

Acrossthe47start-ups,21(45%)reportedthattheydidnotraiseanyrevenuefromsales;mediansalesamount(includingfirmswithzerosales)was€1,550.75%ofallfirmsreportedsalesof€43,680orlower.Thehighestthreesalesrevenuesraisedwere€420,000,€193,950&€145,625.

The top three sales figures across both cohorts were obtained by companies which had beenestablishedforaroundfouryearspriortotheirparticipationinDataPitch.Conversely,aproportionofstart-upswhichraisedzeroinsaleswereyoung(havingbeenestablishedforDataPitch,orverysoon before). This suggests age and maturity level of the firm may have influenced revenuegenerationduringthisperiod.

Fourofthefivehighestrevenueswereobtainedbystart-upswhowerecompletingadataproviderchallenge(withfourofthetoptenhavingcompletedasectoralchallenge).Ofthe21firmswhichreported zero in revenue, 8were completing a data provider challenge, with the remaining 13competingeitherasectoraloropenchallenge.Therefore,34%ofstart-upscompletingdataproviderchallengesandoverhalf(54%)ofstart-upscompletingsectoraloropenchallengeshadraisedzeroinsalesrevenue.

Figure7 DistributionofsalesforDataPitchparticipantsduringtheacceleratorperiod

Note:Y-axisreferstonumberofstart-ups,totalN=47.


Twenty(43%)start-upsraisedanon-zeroamountofinvestment(otherthanthefundingreceivedfromDataPitch).8start-upsraisedinvestmentupto€45,000andtheremaining12firmsraisedover€45,000.Thehighest three levelsamounted to€1,600,000,€507,000and€460,000.All threeofthesestart-upswerecompletingsectorchallenges,andtwoofthesefirmshadraisedzeroinsales



during the accelerationperiod. The start-upswhich raised the top two investment figureswereestablishedwithinayearofenteringDataPitch(withthethirdestablishedwithintwo).

Forthefirmswhichraisedzeroininvestments,justoverhalf(14outof27)werecompletingDataProviderchallenges.Justunder61%ofallstart-upscompletingdataproviderchallengeshadraisedzero in additional investments; for comparison, 50% of start-ups completing sectoral and openchallengesdidnotreceiveanyadditionalinvestments.

Figure8 DistributionofinvestmentsforDataPitchparticipantsduringtheacceleratorperiod



11(23%)start-upsrealisedefficiencies,savingapositiveamountofmoney(intimesaving)fortheirclients/customers.Thevalueoftheseefficienciesrangedfrom€40atthelowend,toamaximumof€318,000.Thesecondandthirdhighestvaluesare€256,000and€105,000respectively.Thesetopthree figureswereobtainedby firmswhichhadraised€420,000,€1137,460&€142,500 insalesrespectively.

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Figure9 DistributionofrealisedefficienciesforDataPitchparticipantsduringtheacceleratorperiod



38(81%)start-upsexperiencedanetpositivechangeinemployment.10firmsincreasedtheirnetemploymentby1,another10firmsby2and17by3ormore,overtheacceleratorperiod.Onlyonefirmreducedtheiremployeeheadcount(-1).Thehighestchangesinemploymentamountedto12,9and8,respectively.

Ofthesethreefirms,twohadgreatlyexceededtheirfirst-andsecond-yearemploymentforecastfigures,withthehighestincreaseof12raisingtheirtotalemploymentto14(threeawayfromtheirone-yearforecastof17).



Figure10 DistributionofemploymentchangesforDataPitchparticipantsduringtheacceleratorperiod



Twenty-fiveofthe47start-ups(53%)hadsecuredfundingpriortoenteringDataPitch.Thisfundingcamefromsourcessuchasotheracceleratorprogrammes,angelinvestorsandventurecapital.Thethreehighestlevelsofpriorfundingamountto$2.3million,$2millionand$2million.For22start-ups,DataPitchwastheirfirstsourceoffunding.

3.5.1 Cohort1–Postaccelerationperformance

In the sixmonths since completing the programme, start-ups fromCohort 1 have continued toincreasetheirrevenue,investmentandemploymentfigures,asshowninthetablebelow.

Table9 Cohort1post-accelerationperformance(6monthsaftertheprogramme)

Indicator Observations Mean Minimum Maximum TotalSales 17 134,379 0 1,200,000 2,284,438Investments 17 71,334 0 665,000 1,212,679Employment 17 1 -3 6 21

Note:Valuesareineuros(otherthanEmployment).InvestmentsincludeGrants,BAs,VCsetc.

Source:6monthsprogressupdate

Onaverage,eachstart-upattractedsalesofjustunder€135,000andinvestmentstothevalueofjust over €71,000. Total employment has increased by 21,with only three firms experiencing areductioninemployment(respectively-3,-2&-1).6firms(35%)reportednosales,10firms(59%)reportednoinvestmentand4firms(24%)reportednonetchangeinemployment.

At12monthsaftertheendoftheprogramme,cohort1wassurveyedagainontheirprogressinthesixmonthssincethe6monthsprogressupdate.9responseswereobtained.

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Table10 Cohort1postaccelerationperformance(6-12monthsaftertheprogramme)

Indicator Observations Mean Minimum Maximum TotalSales 9 91,529 0 480,000 825,000

Investments[a] 11369,091–737,727 0

1,700,000-5,000,000

4,060,000–8,060,000

Employment[b] 5 1 -2 4 5Note:Valuesareineuros(otherthanEmployment).InvestmentsincludeGrants,BAs,VCsetc.

[a]Onefirmreportedinvestmentsofbetween1and5millioneuros;arangehasbeenprovidedinsummarycalculationstoreflectthis.[b]Twoemploymentchangefiguresaregivenbyfirmsashaving‘increased’.Thecalculationshereexcludetheseobservationsandthereforeunderestimatethetruenetemploymentchange.

Source:12monthsprogressupdate;investmentdetailsfor2start-upsreceivedfromthestart-upsdirectly

Ofthefirmswhichfilledinthissurvey,4reportednosales,6reportednoadditionalinvestmentsand1reportedzeronetchangeinemployment.Theaverageincreaseininvestmentrangesfrom€369,000 to €732,727 whereas the average increase in sales amounts to €91,529. Overall,employmenthasincreasedbyatleast5.

Fourofthesestart-upscontinuedtoobtainrevenueoverbothsix-monthperiods;threestart-upshadnotraisedanysalesacrossbothsix-monthperiods.Onestart-upoutoftheninewhorespondedinbothsurveysreportedareductioninsalesfrom€8,000inthefirstperiod,tozerointhesecond.Thehighestincreaseinsalesisanincreasefrom€200,000to€480,000.

Fiveofthestart-upswhichreported investmentfigures inthisperiodreceivedzero inadditionalinvestmentoverthisperiod.Threestart-upswhichraisedzeroinsalesdidraiseinvestmentineitherperiod(withoneofthesefirmsraising€1.3millioninthesecondperiod).Fourfirmswhichraisedsalesrevenuesdidnotreceiveanyadditionalinvestment.Thismaybeanindicationoftheeffectsofcompanymaturity,asstart-upswhicharepre-revenuemaybefocusedonraisinginvestmentbeforethe release of a commercial product. Additionally, five of the start-upswhich reported in bothperiodsreportedanincreaseinemploymentoverbothperiods,withonlyonestart-upreportinganemploymentloss.

3.5.2 ReturnonInvestmentandadditionalinvestmentsleveragedfromDataPitch

Theperformanceofthestart-upscanbecomparedwiththefinancialresourcesprovidedtotheDataPitchprogramme.Table111belowshowsthecumulativeReturnonInvestment(ROI)andleveragedinvestmentsgeneratedbytheprogramme.

ROIisdefinedassalesgeneratedbystart-upsaspercentageofthetotalmonetaryresourcemadeavailabletoDataPitch, includingbutnotlimitedtothefundingprovidedtostart-ups38.Salesarecumulative.Thatis,salesdefinedfortheROIinthe6monthsfollowingtheprogrammeincludesalesgeneratedduringtheprogramme.Similarly,salesfortheROIinthe6to12monthsfollowingtheprogrammeincludeboththeprecedingperiods.

Leveragedinvestmentsmeasuretheadditionalinvestmentsattractedbythestart-upsandenabledbyDataPitch funding.This isdefinedasadditional investmentattractedsince joiningDataPitchdivided by the total monetary resources available to Data Pitch. As with sales, additionalinvestmentsarecumulative.

38About€7.8millionintotal.



Data on post-acceleration performance is only available for Cohort 1. The ROI and leveragedinvestmentfiguresaccountforthisbyappropriatelyadjustingthetotalDataPitchfundingbasedonthenumberofstart-upsforwhichthereisdata.

Table11 ReturnonInvestmentandleveragedinvestment

Duringtheprogramme 6monthsfollowingtheprogramme


ReturnonInvestment 23% 91% 103%Leveragedinvestment 50% 82% 278%

Note:dataon6and12monthsaftertheprogrammearebasedondatafromCohort1only.Thecolumn“6monthsfollowingtheprogramme”usesdataforallofCohort1,whereasthecolumn“12monthsfollowingtheprogramme”usesdataforasubsetforwhichdataisavailable.ThefiguresaccountforthisbyadjustingtheinvestmentprovidedthroughDataPitchbasedonthenumberofstart-upsforwhichdataisavailable.

Source:Bi-weeklymonitoringupdates,6monthsprogressupdate,12monthsprogressupdate;investmentdetailsfor2start-upsreceivedfromthestart-upsdirectly


Thesefigureshidesomevarietyacrossstart-ups.Thefigurebelowshowsthehistogramofstart-up-specific ROI and leveraged investment generated during the programme39. These numbers arebasedonsalesandadditionalinvestmentdividedby€100,000,themaximuminvestmentavailabletoindividualstart-upsinDataPitch.40

Figure11 ROIandleveragedinvestmentduringtheprogrammeperstart-upa) ReturnonInvestment

39Figuresforstart-up-specificROIandleveragedinvestment6and12monthsfollowingtheprogrammeshowsimilarqualitativepatterns.40Theaveragefundingreceivedbystart-upsissomewhatlowerthan€100,000asnotallstart-upsusedthemaximumavailablefunding.Therefore,thesefiguresshowconservativeestimates.Furthermore,thefiguresdonotincludeadditionalmonetaryresourcesavailabletoDataPitch.Theuseoftheseresourcescannotbeattributedtospecificstart-ups.

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b) Leveragedinvestment

Note:y-axisshowsnumberofstart-upwithineachbin.


For both the ROI and leveraged investment, a small number of start-ups manage to attractconsiderablesalesoradditionalinvestment.Asubstantialproportion,however,hasnoorlowROIand leveraged investment. This follows expectations regarding start-ups and acceleration. SomeacceleratedbusinesseswillbeextremelyprofitableandshowhighROI.Otherstart-upsmaybecomesustainablebutwillnotoutgrowSMEstatus.

3.6 Useofdata

DataPitchenabledstart-ups toaccessdata theywouldotherwisenothavebeenable toaccess.ThroughthedataproviderchallengesDataPitchopenedupdatafromdataprovidersthatwouldnotbeavailableforusebystart-upsotherwise.

Themainobjectivesofstart-upsinDataPitchwastomakepredictionswithoridentifypatternsindata.Themajorityusedmachinelearningtodothis,especiallystart-upsinthesectorchallenges.Start-upsratedtheirsolutionsasmoderatelyuniqueandinnovative.

Start-upsinthedataproviderchallengeshadcloserinteractionswiththeirdataproviderandweremorelikelytohavepivotedtheirideafromtheirinitialproposal.

3.6.1 DatasharedinDataPitch

DataPitchenablesdataaccess

AkeyobjectiveofDataPitchistofacilitatedata-drivenopeninnovationbyenablingaccesstodataforstart-upsandSMEs.DataPitchclearlyachievesthis.AsFigure12shows,themajorityofstart-upswould not have been able to access the same or similar datawithout Data Pitch. This isespeciallystrikingforstart-upsinthedataproviderchallenges.Thefigureshowsthatstart-upsinthe sector challenges would have found it easier to access data outside of the Data Pitchprogramme. These start-ups typically had an already established relationship with their dataprovider,fosteringdataaccess.



Figure12 WouldyoubeabletoaccesssimilardataoutsideDataPitch?%ofrespondents

Q4:WithoutDataPitch,wouldyouhavebeenabletoaccessthesamedata(orequivalentdatathatwouldenableyoutoimplementthesamesolution)?

N=41fortotal;N=18fordataproviderchallenge;N=20forsectorchallenge


Thebarrierstodataaccessvaryperchallengetype.Start-upsinthedataproviderchallengesseeregulationasthegreatestbarriertoaccess.Start-upsinthedataproviderchallengesfurthernotedthattheinabilitytofinddataproviderswouldhavestoppedfromaccessingdata.ThissuggeststhatDataPitchmanagedtoco-operatewiththerightkindofdataproviders,whosedatawouldnormallynotbeaccessible.Businessesinthesectorchallenges,ontheotherhand,aremoreheldbackbythecostofdata,whichmayreflectgreaterexposuretoexistingcommercialdatasets.

Figure13 Barrierstodataaccess;%ofrespondents

Q5:Whatwouldpreventyoufromaccessingthesamedata(orequivalentdatathatwouldenableyoutoimplementthesamesolution)?

N=18fordataproviderchallenge;N=20forsectorchallenge


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UnsuccessfulapplicantsalsoconfirmedthataccesstodatawasanimportantreasonforapplyingtoDataPitch,albeitlessimportantthanthefunding.

Figure14 ReasonsforapplyingtoDataPitch;%ofrespondents(unsuccessfulapplicants)

Q6:WhydidyouapplytoDataPitch?Selectallthatapply.

N=9

Source:Unsuccessfulapplicantssurvey

Oncethedatawasreceived,start-upstypicallyhadfullcontrol41overthedatawhenbuildingthesolution.Onlyonestart-upreportedthattheyhadnothadatleastpartialcontroloverthedata.

Figure15 Controlovertheuseofdata;%ofrespondents

Q14:Howmuchcontroldoyouhaveoverthedatawhenbuildingyoursolution?

N=41


Similarly,datawastypicallystoredunderthecontrolofthestart-up.Start-upsinthedataproviderchallengesweremorelikelytouseacommercialcloudservicepaidforbythemselves.Firmsinthesectorchallenges,ontheotherhand,weremorelikelytostoredatawithintheirowninfrastructure.Thisdifferencemayexistbecausetherelationshipbetweenthestart-upanddataprovidermayhavebeen between data provider and sector challenge. In the latter, there typically was an alreadyestablishedrelationship.Thisrelationshipmayleadtomoretrustofthedataproviderinthestart-up’sinfrastructure.

41Thenotionoffullandpartialcontrolwasnotdefinedinthesurveyquestionnairebut illustratedwithanexample.Fullcontrolwasillustratedas “e.g. full copyofdata freelyavailable tome”andpartial controlwas illustratedas “e.g.data called throughAPIwhenrequired”.



Figure16 Locationofdatastorage;%ofrespondents

Q13:Whereisthedatastored?



CharacteristicsofdatasharedinDataPitch

The data shared through Data Pitch does typically not constitute so-called Big Data, i.e. largevolumesofdatathatrequirededicated,high-endinfrastructurestoprocessandanalyse.Over75%ofsuccessfulstart-upsreceived10GBofdataorlessduringtheprogramme.Similarly,respondentstypicallyreceivedfewerthan100,000individualdataitemsorrecords.

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Figure17 Sizeofdatasetssharedbythedataprovider

No.ofentries/records SizeinGigabyte

Q7:Howlargeisthedatasetthatyouobtainedfromyourmain(partnered)dataproviderforuseinDataPitch?

N=41


Most start-ups received either static data (not being updated) or occasional data (updatedinfrequently). Some received live data or real-time data. The mature commercial solutions –discussedinmoredepthbelow–willmovetowardsusinglivedatainthefuture,althoughfewwillbeusingrealtimedata.

Figure18 Periodicityofdatasharedduringtheprogramme;%ofrespondents

Q15:Whatistheupdatefrequency(periodicity)ofthedatausedinyoursolution?Duringtheaccelerationperiod.N=41


Start-ups believe that both the volume and richness (or granularity) of the data was themostimportantfortheirsolution.Thereisalsosomesupportingevidencethattheabilitytocombinedata



withotherdatasetsisbeneficial.63%ofstart-upsused3ormoredatasetsintheirsolution(56%inthedataproviderchallengesand75%;seeFigure20).

Figure19 Mostimportantcharacteristicsofdata;%ofrespondents

Q6:WhichcharacteristicsofthedatausedinDataPitcharethemostimportantforyoursolution?

N=41


Figure20 Numberofdatasetsusedinthesolution;%ofrespondents

Q3:Howmanydatasetsdoyouuseinyoursolution?Pleaseprovideatotalnumberofopen,closedandself-generateddatasets.

N=18fordataproviderchallenge;N=20forsectorchallenge.


Eventhoughstart-upsnotethatvolumeisimportant,theydonotuseBigDataintheirsolution.Inlinewiththedatasharedthroughtheprogramme,start-upstypicallyuse lessthan10GBintheirsolution.Onaverage,thenumberofentriesintheultimatesolutionislargerthanthedatasharedthroughtheprogrammebutthesizeinGBissomewhatsmaller.Thissuggeststhatnotallattributesofthedatasharedthroughtheprogrammearebeingused.

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Figure21 Sizeofdatasetsusedinthesolution

No.ofentries/records SizeinGigabyte

Q9:Howlargeisthedatasetthatyoursolutionusesintotal?(I.e.anydataset(s)providedforDataPitch+anydataset(s)youcollectedyourselforobtainedfromotherdataprovidersoutsideDataPitch)

N=41a


Moststart-upsusenumericdata.Asubstantialportionusetextorgeospatialdata.Start-upsinthesectorchallengesweremorelikelytogobeyondnumericandtextdata,andalsousedvideoandgraphicaldata.Themaintypesofdatamayexplaintherelativelysmallsizeofthedatasetsusedintheultimatesolutions.Numericandtextdatatendstotakeuplessspacethan,forexample,imagesandvideo.

Figure22 Maintypeofdatausedinsolution;%ofrespondents

Q11:Pleaseindicatethemaintypeofdatausedinyoursolution,selectallthatapply.

N=41




Start-upsinthedataproviderchallengestendtostoredatainfiles,whereasstart-upsinthesectorchallengestendtouserelationaldatabases.Thismayreflectamoread-hocapproachtodatasharingbythedataprovidersintheproviderchallenges.

Figure23 Datastoragetype;%ofrespondents

Q12:Howisthisdatastored?



Overall,thedatasharedinDataPitchappearstobeatthelowerendofthespectrumintermsofcomplexity (data types, volume and update frequency). While this says nothing about thecomplexityofthedataitself intermsofthedifficultyofmakingituseful inabusinessprocess, itshowsthattherequireddataengineeringanddatascienceskillsdidnotneedtoincludeknowledgeaboutdealingwith“bigdata”.

3.6.2 DataPitchsolutions

ThepluralityofsolutionsgeneratedthroughDataPitchusedatatomakepredictions,followedbyseeking to identifypatternsor createauser interface.The first twoare clearMachine Learningapplications.

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Figure24 Mainobjectiveofthesolution;%ofrespondents

Q19:Whatistheprimarytechnicalobjectiveofyoursolution?

N=41


Consequently,themajority,butnotall,start-upsreportusingMachineLearningintheirsolutions.Thereisacleardistinctionbetweenthechallengetypes.Start-upsinthedataproviderchallengestendtouseMachineLearninglessandtendtouseregressionandclusteringalgorithms.Start-upsinthesectorchallengesaremorepronetousedeeplearningalgorithms.Again,thismayreflectthefactthatparticipantsintheproviderchallengeshadlesstimetopreparetheirapproaches,whereasstart-upsinthesectorchallengesmayhaveworkedwiththerelevantdataandmethodsforlongerperiodsoftime.Itmayalsobethecasethatdataprovidersinthedataproviderchallengesweremorecautiousandsoughtmoremainstreamsolutions,asmanyofthesedataprovidersarestillintheearlyphaseofexperimentingwithopeninnovation.Thesedataprovidersmayalsohavebeenmorecautiousastheyhadtoworkwithstart-upsunfamiliartothem.

Figure25 UseofMachineLearning;%ofrespondents

Q20:DoesyoursolutionuseMachineLearning?





Inrelationtothesolution,thesurveyaskedabouttheperceiveduniquenessandinnovativenessofthe solution. Start-ups believe their solution to bemoderately unique. Businesses in the sectorchallengesratetheirsolutionasmoreunique.Thiscouldbearesultofparticipantsintheproviderchallengesworkingonanarrower,moredefined,setofbusinessproblems.

Figure26 Howuniqueisyoursolution?%ofrespondents

Q30:Onascaleto1-10,howuniqueistheproductthatyoursolutionprovidestoyourcustomers?Aretheresimilartypesofproductsoutthere,oristhisaone-of-a-kind?(Ananswerof1signifiestheproductis'notatallunique'andananswerof10signifiestheproductis'completelyunique').



Thesefindingsaremirroredinperceivedinnovativenessofthesolution.Here,start-upsinthesectorchallengestendtoratetheirsolutionmoreinnovative.Notsurprisingly,theperceiveduniquenessandinnovativenessofsolutionsarehighlycorrelated.

Figure27 Howinnovativeisyoursolution?%ofrespondents

Q31:Onascaleof1-5,howinnovativeisyoursolution?(Ananswerof1signifieslowinnovation,ananswerof5signifieshighinnovation).



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Figure28showstheperceiveddifficultyofstart-upstoaccessdata,getitintotherightshape(dataengineering) and building the solution.42 Overall, data access was considered the easiest andbuildingthesolutionwasconsideredthemostdifficult.

Figure28 Perceiveddifficultyofaccess,engineeringandbuilding;%ofrespondentsa) Dataaccess

b) Dataengineering

42Notethatthesefiguresrefertoaccesstoandengineeringofdatasharedwithstart-upsthroughtheDataPitchprogramme.Thesefiguresdonotrefertoageneralabilitytoaccessorengineerdata.



c) Buildingthesolution

Q18:Foreachofthebelow,pleaseratethedifficultyyouhadduringtheaccelerationperiod:(1signifyingeasiest,5signifyinghardest)



Start-upsinthedataproviderchallengesfoundaccessingdatamoredifficultthanstart-upsinsectorchallenges,presumablybecausestart-upsinthesectorchallengeshadapre-establishedrelationshipwiththeirdataprovider.Ontheotherhand,start-upsinthedataproviderchallengeshadaneasiertimeengineeringthedataandbuildingthesolution.Inpartthislikelyreflectsthemethodsadopted(seeFigure25).

Partof theperceiveddifficultiescanbeattributedtothe interactionbetweendataproviderandstart-up.43

AsshowninFigure29,moststart-upsreportthattheyhadatleastsomeinteractionwiththeirdataprovider.Start-ups in thedataproviderchallengesweremoreclosely interlinkedwith theirdataprovider than the start-ups in the sector challenges. The results also show that start-ups thatinteractedmorecloselywiththedataprovidermatchedthroughDataPitchinteractedlesswithdataprovidersoutsideoftheprogramme.

43TheinteractionbetweenSMEanddataproviderisdiscussedinmoredetailbelow.

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Figure29 Interactionbetweenstart-upanddataprovider;%ofrespondents

Q17:HowcloselydidyouinteractwithyourpartneredDataProvider?(1signifies'Nointeraction'and5signifies'Verycloseinteraction')



Start-ups that had closer interactionswith their data provider found accessing data easier. Thiscorrelationisespeciallystrongamongthestart-upsinthedataproviderchallenges.Ontheotherhand,buildingthesolutionwasperceivedasharderbystart-upswithcloserinteractionswiththeirprovider.Possibly,acloseinteractionbetweenstart-upanddataproviderallowedthestart-uptobetterunderstandtheprovider’sbusinessesneed,whichmeantthatpreconceivedideasaboutthesolutionhadtobeabandoned.

Indeed,moststart-upshadtochangetheirinitialproposalatleastsomewhatduringtheprogramme(see Figure 30). Most changes occurred due to business reasons, highlighting that a betterunderstandingofthebusinessneedsmayindeedhavedrivenchangesinthesolution.Thisnotionisfurtherstrengthenedbythefactthatstart-upsthathadacloserinteractionwiththeirdataprovideralsoreportedthattheirultimatesolutionismoredifferentfromtheirinitialproposal.



Figure30 Howdifferentisthesolutionfromtheinitialproposal?%ofrespondents

Q23:HowdifferentisyoursolutionnowfromtheideayouhadatthestartofyourinvolvementinDataPitch?(Ananswerof1signifiesyoursolutionmatchestheinitialproposalexactly;ananswerof10signifiesthatthesolutioniscompletelydifferentfromtheproposal).



3.7 Driversofoutcomes

Start-upsgenerated€599,432insalesandattracted€338,862inadditionalinvestmentperGigabyteshared with them through Data Pitch during the programme. There is, however, no linearrelationshipbetweenthesizeofdatasetsandthevaluegenerated.

There is considerable variation between outcomes for start-ups operating in the same sector.Tentatively, it seems that health start-ups still rely on investment, start-ups working in heavyindustryarematuringtobesales-drivenandstart-upsinthefinancialsectorshowmostemploymentgrowth.

Theuseofmachine learning in the solution impacts the ability to attract investment. Perceivedabilitytoscaleasolutionisalsorelatedtotheability(orwillingness)toattractfunding,withmorescalablesolutionsattractingmoreinvestment.

The programme was delivered efficiently, fostered cooperation between start-ups and dataproviders andenabledmarket entry bymatching start-upswithpotential customers.DataPitchworkedparticularlywellforstart-upswithpre-existingideasorworkingproducts.

Data Pitch tracked outcomes of start-ups during the programme. This section highlights somepotentialdriversoftheseoutcomes.44

44Notethatthedatainthissectionisbasedonthe41start-upsthatcompletedthesuccessfulapplicantssurvey,ratherall47fundedfirms.

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3.7.1 Shareddata

Tounderstandpotentialdriversofoutcomes,ithelpstosplitstart-upsintogroupsbasedoncertaincharacteristics.AnobviousfirstsplitistheabilitytoaccessdataoutsideofDataPitch.

Asnotedabove,amainaimofDataPitchwastoenabledatasharingandthesurveyresultsshowedthatalargeportionofstart-upswouldnothavebeenabletoaccesssimilardatawithoutDataPitch.Accesstodatadoesseemtoinfluencetheabilityofstart-upstoattractadditionalfunding.Firmsthatreportedtheycouldaccessdataattracted,onaverage,€177,059inadditionalfundingwhereasfirmsthatarenotabletoaccessdataattracted€36,044onaverage.Theabilitytoaccessdatamaybeasignofmaturity,explainingwhythisdifferenceexists.

Moreover, the size of datasets matters. On average, start-ups generate €599,432 in sales and€338,862ininvestmentperGBofdatasharedwiththemthroughDataPitch.Thispartlystemsfromthefactthatthemajorityofstart-upsusedarelativelysmallamountofdataintheirsolution.Butitshowsthatmuchvaluecanbegeneratedfromrelativelylittle.

However,theredoesnotseemtobeaconstantorevenuniformlypositiverelationshipbetweenvaluegenerated fromadatasetand its size.WithinDataPitch, it seems that thestart-upsusing“middle-sized”datasets (0.11 to1GB)managed toattractmost salesandadditional investment,whereasstart-upsusingsmallerorlargerdatasetsgeneratedlessvalue.However,therelationshipbetweendatasetvalueandsizeiscomplexandwilldependonspecificcircumstances.

3.7.2 Customers&dataaccess

Dataforall47participantsinDataPitchontheirintendedcustomerbase,theirabilitytoaccessdataand outcomes generated during and after the programme45 were analysed to understand theinteractionbetweencustomersanddataaccess. Individual leveldatacannotbepublishedinthisreportduetothesensitivenatureofthedata,buttheresultsarediscussedbelow.

Asdiscussedinsection3.2,asubstantialshareofstart-upsisactiveinthehealth,transport/mobilityand financial sectors. Other sectors covered include retail, industry with large assets such asmachinery(includingutilities)andthefoodandhospitalityindustries.

Among the three larger sectors, data seems most accessible for the mobility sector. Start-upsworkinginthefinancialandmedicalsectorsweremorelikelytoreportthat–outsideofDataPitch– theywould not have been able to access the same data. This likely reflects the sensitivity offinancialandhealthcaredata(whichistypicallypersonaldata),includingthefactthatdataaccessinthosesectorsisgovernedbysector-specificregulatoryframeworksinadditiontogeneralregulationrelatingtopersonaldata(GDPR).

There is considerable variability in sales, investment and employment changes evenwithin thedifferentsectorscoveredbythestart-upsintheprogramme.Thismakescomparisonacrosssectorschallenging.Nonetheless,anumberoftentativequalitativeconclusionscanbedrawn.

Start-upsactive in thehealthsector typicallystill relyon investment, raisingmore in investmentduring and after Data Pitch than they receive in sales. In contrast, start-ups whose intended

45Outcomedatawasderivedfromthebi-weeklymonitoringupdates,the6monthsprogressupdateandthe12monthsprogressupdate,whereavailable.



customersoperateinheavyindustryandutilitiesarematuringawayfrominvestmentandinsteadshowgoodsalesfigures.

Start-upsactiveintransport/mobilitytendtoattractbothsalesandinvestment,althoughthisdoesnottranslatedirectly intoemploymentgrowth.Start-ups inthefinancesector, incontrast,showmorepromisingemploymentgrowth.Furthermore,thereisamixofstart-upsrelyingininvestmentandstart-upsmaturingtowardssales.

Start-upswithretailandfoodastheirtargetmarketsappearweakestintermsofinvestmentandsales performance. This may be because these are already very competitive markets, and themarginsarelow.Therefore,acompanyneedstobeexceptionaltoattractfundingandsales.

3.7.3 Solutioncharacteristics

Themajorityofstart-upsusedmachinelearningintheirsolution.Theoutcomestrackedduringtheprogrammeprovidesomeevidencethatusingmachinelearninghelpsattractinvestment.

Thereisanimpressivedifferencebetweenstart-upsthatusedmachinelearningandthosethatdidnot.Start-upsthatdidusemachinelearningattracted,onaverage,€126,209ininvestment.Start-ups thatdidnot receive investment,only attractedanaverageof€17,917. Thishints towardsapotential“techbias”amonginvestors.

Theeasewithwhichdatacanbeengineeredintoshapeandtheeasewithwhichasolutioncanbebuiltmay also impact the value generation process.46 As Figure 31 shows, the impacts are verydifferentforboth.Onemayexpectthattheabilitytoattractinvestmentimprovesengineeringorbuilding is (perceived to be) easier.On the other hand, investorsmay appreciate start-ups thatrealisticallyperceive theeaseofworkingwithdata tobeat least somewhatdifficult.Given thatthereisnouniformlypositiveornegativerelationship,bothmaybeatplay.

46Notethatthissectionexcludestheeasewithwhichdatacanbeaccessed.Thesuccessfulapplicantssurveyaskedspecificallyabouttheperceived ease of access, engineering and building the solutionwithin the context of the data used in the Data Pitch programme.Perceived ease of engineering and building solutions are likely to have generalisable elements. Access on the other hand cruciallydependedonco-operationwiththedataprovider.Thismaynotgeneralisetoothersituationswithotherdataproviders.

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Figure31 Averageinvestmentperassessmentofeaseofdataengineeringandbuilding;€

Source:Successfulapplicantssurveyandbi-weeklymonitoringupdates

3.7.4 Scalingpotential

Mostrespondentsbelievedthattheircoresolution47canbescaled.Especiallythestart-upsinthesectorchallengesbelievedinsignificantgrowthfortheirproduct.Start-upswereevenmorepositiveaboutbreakingintonewmarkets,newcustomergroupsornewapplicationareas.Morestart-upsbelievedinsignificantgrowthopportunitiesforsuchnewgroundthanforthecoresolution.

Figure32 Scalabilityofstart-ups’coresolution;%ofrespondents

Q28:Howdoyouseethescalabilityofyoursolutionoverthenextthreeyears?(Ananswerof1signifies'limitedgrowth',ananswerof5signifies'significantgrowth').Coresolutiononly:markets&customersascurrentlyidentified



47ThesolutiondevelopedthroughDataPitchprovidedinanalreadydefinedmarkettoanalreadydefinedcustomerbase.



The (perceived)ability to scalea solutionhasadistinct impacton thecapacityorwillingness toattractfunding.AsshowninFigure33,thegroupthatperceivedonlylimitedgrowthorevenneutralgrowthonlyattracted,onaverage,€7,692ininvestment.Thegroupthatratedtheopportunitiestoscale as reasonablyhigh, attracted€98,231onaverage. Thegroup thatperceivehigh growth inscaling their core solutionattracted€166,537onaverage in additional funding. In all, perceivedabilitytoscaleandattractingfundingarehighlycorrelated.

Figure33 Averageinvestmentbyassessmentofopportunitiestoscale,€

Note:dataonperceivedgrowthisdrawnfromQ28.1inthesuccessfulapplicants’survey.Thegroupingsaredefinedasfollows:

Limitedtoaveragegrowth:options1,2and3;Goodgrowth;option4;Significantgrowth:option5.

Source:Successfulapplicantssurveyandbi-weeklymonitoringupdates

3.7.5 Participants’observationsondriversofoutcomes

Severalfactorswereidentifiedbystart-upsanddataprovidersasmakingasignificantcontributiontothesuccessofDataPitch.

Outreachandrecruitment

Foroptimalimpact,itisimportantthataprogrammelikeDataPitchreachestherightparticipants;organisationsthathavethemotivation,skillsandresourcestoparticipateinopeninnovationandrealisesolutionswithintheaccelerationperiod.

OutreachactivitiesundertakenbyDataPitchweresuccessfulinthattheyactivatedalargenetworkofmultipliersandreachedrelevantstart-upsanddataprovidersacrossEurope.Interviewssuggestthat previous engagementwith the consortium partners and their staffwas important inmanycases,butthegeographicspreadshowsnosignofbias.Theinterviewsalsoconfirmedthatsomestart-upscameacrossDataPitchthroughsimplewebsearch,whichindicatesthattheprogrammewasaccessibletoallinterestedstart-ups.

Thesituationislessclearinrelationtodataproviders.Somedataprovidersfeltbeingmatchedwithseveralstart-upsdilutedtheimpactoftheprogrammeandputsomestrainontheresourcestheycoulddedicatetotheprogramme.Forfutureprogrammes,thismeansthatthereisvalueinmaking

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dataprovidersunderstandtheresourcerequirementsforaprojectlikeDataPitch.Thismayincludebriefingdataprovidersonexpectedcapacityandensuringthatproviderscanprovidethiscapacityforthedurationoftheprogrammebeforecommittingtotheprogramme.Thisinformationexchangecouldbepartofapre-accelerationphase(“phase0”).Furthermore,itmaybehelpfultorecruitmoredataproviders.

Projectset-up

DataPitchsucceededinprovidingsupporttoaverydiverserangeoforganisations;fromsmallidea-stage start-ups to successful high-growth SMEs and, on the data providers’ side, from largecorporatestoresearchconsortia.TheDataPitchpackageseemstohaveworkedparticularlywellforstart-upswithanexistingproductorservicethattheycoulddevelopfurtherduringtheaccelerationperiod,particularlywhereitallowedthemtoaccessanewmarketornewsetofclients.Thereissomeevidencethatearly-stagestart-upsstruggledmoreinthedataproviderchallenges.Inpartthisreflectsfrictionsintheinteractionwiththedataproviders.Earlystagestart-upsarelikelytohavelessexperienceworkingwithlargeorganisationsandcorporations.Thislackofexperiencemayleadtoamismatchofexpectationsbetweentheearly-stagestart-upsanddataproviders.Early-stagestart-upsmay,forinstance,notbeawareofthetimeandeffortrequiredtogetapprovalfordata-sharingwithinlargecorporations.Theymay,therefore,expecttoreceivedatamorequicklythanadataprovidercandeliver.

It is also evident that participants in theprovider challenges had inmanyways a very differentexperiencethanthoseinthesectorchallenge,withcloserinteractionwiththedataproviderandaclearerfocusonopeninnovation.

Somedataprovidersdesiredagreaterfocusonthedataproviderswithaviewtomaximisingthemutual benefits of the accelerationperiod.Most successful examplesof co-operationsbetweendataproviderandstart-upshowedagreatdealofintrinsicmotivationandpriorexperienceintheopeninnovationfieldonthesideofthedataprovider.Inparticular,dataproviderswouldhavelikedmoreinteractionswithotherdataproviders.Oneprovidernoted,forinstance,thatanetworkingeventinvolvingtheparticipatingdataproviderscouldhavebeenusefultoshareexperiencesandbest practices regarding open innovation. It appears that data providers often are not matureenough to make datasets available in a way that allows immediate work on solutions to theirbusinessproblems.Thefrictionsarebothtechnicalandrelatedtothebusinesscases.

Several data providers remarked that Data Pitch did not particularly serve a role as a fullintermediarybetweendataprovidersandstart-upsintermsofironingouttechnicalandbusinesschallenges. In the only casewhere real frictions arose between a start-up and amatched dataprovider, itwas remarked that: “There shouldbe some thinking intowhat todowhen thedataproviderandSMEsincentivesandgoalsarenotstrictlyaligned”.Inthiscase,frictionsarosefromboththestart-upandthedataprovider.Onthesideofthestart-up,thesolutionwasnotagoodfit,and the necessary pivoting was resisted by the start-up. Access to data and data quality wereproblematic.Onthesideofthedataprovider,theteamdedicatedtoDataPitchchangedandwerenotalignedwiththeoriginallyenvisionedplan.However,thestart-upwasveryyoung,startedjustbefore Data Pitch, with perhaps unrealistic expectations and little business experience.Furthermore,thedataprovideralsowasnotexperiencedwithopeninnovation.

Whiletheprogrammedurationisinlinewithotheraccelerators,severaldataprovidersindicatedthatthetimewasinsufficientforthemtorealisevaluefromthesolutions.Theyfeltthat6monthswasinsufficienttocreateaminimalviableproductfromscratch.Somedataprovidersmentionedamismatchbetweenwhatstart-upsthinktheycandoin6monthsandwhattheycanactuallydo.A



coupleofstart-upsremarkedthatmoretimewouldbeneededtobuildmoretechnicallyambitioussolutionsandthatsixmonthswasnotlongenoughtobuildaviableteam,amodelandplatformforthesolution.

Acoupleof start-upsmentioned that the termsof thegrantpaymentswerenot ideal for them,eitherbecausetheywouldhavepreferredmoreregularpaymentsorbecausetheywouldhavelikedmoretimetousethebudget(beyondtheaccelerationphase).

Efficientdelivery

ParticipantswereveryunderstandingoftheneedfortightmonitoringonthepartofDataPitch,inparticular the responsibilities that come with using public funding. Only two start-ups voicedunprompted criticism of the administrative burden (“Some weeks we didn't’ have anything toreport”),whilemanymorewerecomplimentaryabouttheefficiencyoftheirinteractionswithDataPitch (“the programmewas handled perfectly, don’t see any point which could be improved”).Indeed,start-upsconsideredthemonitoring(includingadvisoryandcoaching)arrangementstobeuseful(“Verygoodideatoreportbiweekly–itwasveryproductivetohelpusreflectonwherewestood”; “biweekly check-ins didn’t really exceed 10 minutes”; ”the biweekly meetings in thebeginning were amazing”). Data Pitch was compared very favourably with other accelerators,especially publicly funded ones. The use of tools like FP6 is seen as a plus and shows a goodunderstandingoftheoperatingenvironment.Oneofthestart-upscommentedthatDataPitchwas“moreprofessionally run” than theanotherpublic incubator, inwhich theyalsohad takenpart,whileanothercommentedthatbothweresimilarintermsofadministrativeburden.

Collaborativeopeninnovation

Bringing together data providers interested in open innovation and start-ups with the skills toimplementinnovativedata-enabledsolutionsisinmanywaysthecoreachievementofDataPitch.Thiswasuniversallyrecognisedbyparticipantsintheproviderchallenges(bothdataprovidersandstart-ups).ThereisclearevidencethatDataPitchenabledstart-upstoaccessuniqueandvaluabledatasetstowhichtheywouldnothavehadaccessotherwise.Thisiscorroboratedbyresponsestothe‘successfulapplicants’surveythatclearlyshowsthatespeciallystart-upsinthedataproviderchallengewouldnothavebeenabletoaccessdatasimilartothedatasharedthroughDataPitch.While some start-ups remarked that they would have liked more data, all agreed that thecombinationofrelevantdataandthereal-lifebusinessneedsofthedataproviderhelpedthemtoachievetheiraccelerationgoals48.Bysubsidisingdataaccess,DataPitchfillsagapintheinnovationsupportlandscapethatisnotaddressedbyothermeasures.Forexample,thecostofdatacan’tbeclaimedunderR&Dtaxcredits(Coadec&TheEntrepreneursNetwork,2019).Furthermore,inmanycases, commercial data is not available for sale, even if fundswould be available. Data sharingsolutionslikeDataPitchcanalsoopenupthesedatasets.

Dataprovidersreportedclearandsubstantialbenefits intermsoforganisationalcommitmenttoopeninnovation.Onehighlightedthatdatasharinghasalwaysworkedwellwithintheirownfield,but cross-fertilisationwith other areas is the challenge theywanted to solve.Most of the dataprovidersshowedaclearunderstandingoftheconceptanditsbenefits(“openinnovationmeans

48Boneetal.(2019)findthat“start-upswhichreceiveaccesstopartnersandcustomers,helpinrefiningandtestingtheirbusinessmodel,and/orhelpwithteamformationaremostlikelytothinktheprogrammetheyparticipatedinpositivelyimpactedtheirchanceofsuccess”.Whiletheycautionthatthetypesofsupportperceivedtobeimportantbystart-upsareoftennotthesameasthosethatareassociatedwithstart-upssuccess,theydonotaddresstheuniquedata-sharingfunctionofDataPitchinanyway,whichmakestheirresultsdifficulttoapplytoDataPitch.

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workingtogetherwithstart-upsandacademicstogetinnewideasfromtheoutside,butalsosharingourknowledgetodevelopnewsolutions,approachesandinnovations”;“wewantedtoworkwithstart-ups to figure out the challenges to accessing and processing and working with our data,learninghowtoexplainandsharethisdatawasamotivationforparticipation”).

Severaldataproviderscommentedthattherelationshipsbetweenstart-upsanddataproviders,andtheoverallsuccessofthecollaboration,dependsinnosmallmeasureontheindividualsinvolved.Therefore,theprogrammecanbesusceptibletopersonnelchangesorsetbacks.Severalofthedataprovidersreportedthatthepersonsoriginallydrivingtheirorganisation’sparticipationshadsinceleft,orwereunabletocontributeduringtheprogramme.Collaborationbetweenstart-upsanddataprovidersseemedtobedifficultinthosecases.

Mixofsupport

Participantswerenear-unanimousintheviewthatDataPitchworkedasapackage;theprogrammeprovidedallthe“resourcesnecessarytostructureandruntheproject–couldnothavedonetheprojectwithouteitherthesupport,moneyordata.Wouldnothavebeenabletodothiswithjustthedataset(andnomoney)”.Funding,whichwasusedprimarilytorecruittechnicalstaff,playedakeyenablingroleforallstart-ups.However,forstart-upsinthesectorchallenges,fundingseemstohavebeenmore importantthanfor those intheproviderchallenges.Thesectorchallengesthuslookalotmorelikeatraditionalaccelerator,inwhichfundinganddirectsupportarecrucial.

Other elements of support that were seen as useful include the provision of AWS credits andmentoringandexpertsessions.AWScreditswereseenascrucialbyseveralstart-ups,althoughonewouldhavepreferredthat theoptionalAWSschemewasextendedtootherprovidersofsimilarservices.

Overall mentorship was seen as a useful component of Data Pitch and individual advisors andmentors received praise. However, a few start-ups fed back that the depth of expertise of thementorswasinsufficient,eitherintermsoftechnicalknowledge,domainknowledge,orbusinessexperience.Given thebreadthof sectors, start-upsanddataproviders targetedbyDataPitch,awiderknowledgebaseformentorswouldhavehelped.

Marketaccess

DataPitchhelpedstart-upstoovercomebarrierstoentrytonewmarkets.(“DataPitchactsasevenmorethanashortcut,a‘newdoor’whichwouldn’thaveexistedwithouttheprogramme,anewwayin.”) By overcoming information asymmetries and enabling trusted relationships between dataproviders and start-ups, the programme provides significantly added value compared withtraditionalaccelerators.

Europeandimension

TheabilityofDataPitchtoconnectorganisationsacrossEuropewasseenasanadvantage,especiallyfor businesses outside established start-up hubs. Data Pitch enabled matches between dataprovidersandstart-upsthatwereotherwiseunlikelyeitherbecausetheyare locatedindifferentcountriesorbecauseprovidersandstart-upsarenotawareofeachother’sexistence.Somedataprovidersevennotedthattheyspecificallyparticipated inDataPitchtoworkwithstart-upsthatotherwisetheywouldhavenotbeenawareof.However,someparticipantsremarkedthatdataisoftenlocationspecific,sothattheutilityofthedatamaybereduced.



Onestart-upmentionedhealthcaredatainhishomecountryisespeciallydifficulttoaccess,sothatDataPitchprovidedagreatopportunitytoworkwiththistypeofdata.Thishighlightsanimportantpointthatdataecosystemscontinuetodifferalongmanydimensions,sothatenablingdatasharingacrossbordersisabenefitinitself.

Ontheflipside,thevirtualset-upofDataPitchledtolessclosecontactandreducedthelevelofsupportthedataprovidercouldprovideinsomecases.SomeparticipantsobservedthattheDataPitch’sbasewas“veryLondon-centric”,whereasmostorganisationswerenotbasedinLondon(bothdataprovidersandstart-ups).Theyfeltthattheycouldhavegotmoreoutoftheprogrammeiftherehadbeenmoreinteraction.Thiswasespeciallyrelevantfordataproviders.Start-upsingeneralsawthevirtualaspectasanadvantagethatfitinwiththeirwayofworking(severalhadoperationsinseveralEuropeancountries,inparticularthroughdevelopersworkingremotely).

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3.8 Counterfactualanalysis

Successfulapplicantsseemedtohavebeenabletoattractmorefunding(includingandexcludingthefundingreceivedfromDataPitch)comparedtoapplicantstoDataPitchthatdidnotgetfunded.Furthermore,successfulapplicantsseemedtohavegeneratedmoreemploymentthanunsuccessfulapplicants.However,thereisnoevidenceofstrongerperformanceintermsofrevenuegrowth.Thecomparisonisbasedonasmallsampleof9unsuccessfulapplicants.

TogaugethenetimpactofDataPitch,acomparisonwithunsuccessfulapplicantscanbeuseful.Thisassumesthatstart-upsthatappliedtoDataPitchbutwereunsuccessfularesimilartothesuccessfulapplicants inat leastsomerespects thatarerelevant forbusinesssuccess (e.g. formalqualifyingcriteria,interestinopeninnovation,motivationtoparticipate).

Asacomplementtothesuccessfulapplicants’survey,datawascollectedonunsuccessfulapplicantsthroughasurvey49.Thesurveyreachedintotal9SMEsofwhich8arestillactive.Thelownumberofresponsesfromunsuccessfulapplicantslimitstheinsightsthatcanbeobtained.Forexample,itis possible that more successful businesses had a greater motivation to respond, which wouldintroduceanupwardbiasintotheperformanceofthecounterfactualgroup.

Nonetheless,theperformanceoftheunsuccessfulapplicantscanbecomparedwithperformanceofthesuccessfulones.Theunsuccessfulapplicantssurveyaskedaboutinvestmentattracted,thechangeinrevenuesinceapplyingtoDataPitchandthechangeinemploymentsinceapplying.Thesewerematchedwithdata from thebi-weeklymonitoringupdatesand6monthsprogressupdateprovidedbysuccessfulapplicants.50

Successful applicants received, on average, more external funding (not including the €100,000received through Data Pitch). Some applicants, both successful and unsuccessful, were able toattractsubstantialinvestmentsupwardsof€500,000.

49SeeAnnexA3.550Thebi-weeklymonitoringrequirementscoverapproximatelythe6monthsduringwhichtheDataPitchprogrammewasrun.The6monthsprogressupdatecapturessuccessfulapplicants’progressinthe6monthsfollowingDataPitchandisavailableforcohort1onlyatthetimeofwriting.Whereavailable,thedatafromthebi-weeklymonitoringupdatesandthe6monthsprogressupdateweresummedtoarriveata12-monthprogresssincethestartoftheprogramme.Forcohort2,thedatafromthebi-weeklymonitoringupdateswasmultipliedby2toprovidecomparabledata.

Thedataontheunsuccessfulapplicantsfromthesurveycoversthe12monthssinceapplyingtotheDataPitchprogramme.



Figure34 FundingreceivedsinceapplyingtoDataPitch;%ofrespondents

Forunsuccessfulapplicants:DataderivedfromQ13:Howmuchexternalfundingdidyoureceiveinthefirst12monthsafterapplyingtoDataPitch?N=9

Forsuccessfulapplicants:dataderivedfrombi-weeklymonitoringupdatesand,whereavailable,the6monthsprogressupdate.Fundingreceivedincludes€100,000fundingreceivedthroughDataPitch.

Exactnumberofresponsesinlabels.

Source:Unsuccessfulapplicantssurvey,bi-weeklymonitoringupdatesforcohort1and2,and6monthsprogressupdateforcohort1

Concerning revenue gains, the successful applicants were not necessarily better off than theunsuccessful applicants. A slightly larger share of successful applicants reported no growth inrevenues whereas a larger share of unsuccessful applicants reported increases upwards of€500,000.

However,thedataderivedfromthemonitoringupdatesand6monthsprogressupdatemaynotaccount for the fact that Data Pitch could have fostered relationships between start-ups and apotential client; the data provider. Therefore, revenue effects of Data Pitch may only becomeapparentaftersometime.

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Figure35 IncreaseinrevenuesinceapplyingtoDataPitch;%ofrespondents

Forunsuccessfulapplicants:DataderivedfromQ11:Byhowmuchdidyourannualisedrevenuechangeinthefirst12monthsafterapplyingtoDataPitchcomparedtobeforeapplying?N=9

Forsuccessfulapplicants:dataderivedfrombi-weeklymonitoringupdatesand,whereavailable,the6monthsprogress.Moreprecisely,thisisderivedfromthesumofreportedsalesandefficiencygains.ThisassumesthatthereportedrevenuesinthemonitoringupdatescaptureonlyadditionalrevenuesdirectlyrelatedtothesolutiondevelopedthroughDataPitchandthatreportedefficiencygainscapturetheeffectoftheDataPitchprogrammeonotherrevenuestreams.



Lastly,changesinemploymentcanbecompared.Successfulapplicantsseemedtohavefaredbetterhere.Generally, successful applicantshavegrownmorequicklywith fewer successful applicantsreporting a decrease in employment and a substantial share showing an increase of 6 to 10employees.



Figure36 ChangeinemploymentsinceapplyingtoDataPitch;%ofrespondents

Forunsuccessfulapplicants:DataderivedfromQ10:Byhowmuchhasthenumberofemployeesinyourcompanychangedinthefirst12monthsafterapplyingtoDataPitch?N=9

Forsuccessfulapplicants:dataderivedfrombi-weeklymonitoringupdatesand,whereavailable,the6monthsprogressupdate.



Theincreaseinemploymentforsuccessfulapplicantsisnotsurprising.Theinterviewswithstart-upsclearlyshowedthatDataPitchfundingwasusedmostoftentohirepeople.ThesuccessfulapplicantssurveyfurthershowedthatDataPitchfundingenabledstart-upstoacquireabroadrangeofskills,ofwhichdatascienceandmachinelearning,andsoftwaredevelopmentwereconsideredthemostimportant.

ThechangeinemploymentshowninFigure36formsthebedrockoftheprojectionofemploymentgrowthinthe3yearsfollowingtheprogramme,describedinthenextsection.

3.9 Impactprojections

Employmentisforecastedtogrowby,onaverage,32%peryearuntil2022.Thisimpliesanincreaseofemploymentto717from407atthestartoftheprogramme.ForecastedemploymentgrowthisbetterthanispredictedinascenariowithouttheexistenceofDataPitch.

Annual total revenue generated by start-ups is forecasted to grow by 73% per year until 2022.Annualrevenuesareexpectedtoby459%ofthetotalfundsavailabletotheDataPitchconsortium,includingbutnotlimitedtothefundingreceivedbythestart-upsintheprogramme.

Thissectionpresentsestimatesofpotentialfutureemploymentandrevenuegrowthforthenextthreeyearsbasedonasimulationmodel51.ThedetailsofthemethodologyaredescribedinAnnex2.

51Thistimeframehasbeenchosenbecauseoneoftheinputassumptionsisexplicitlyvalidoverthistimeperiod.

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3.9.1 Employmentgrowth

TheforecastmodelshowsthatDataPitchmayhelpstart-upsgrowmorequickly.Thefiguresbelowshowthepredictednumberoffundedstart-upsthatsurviveperyear,andtheiraverageandtotalemployment.Forecastsstartintheyear2019andgoupto2022.52

TheforecastmodelshowsthatDataPitchhelpsmorestart-upssurvivethefirstthreeyearsaftertheprogramme. Theprojectednumberof survivingbusinesses in the forecastmodel is 43by2022,comparedto36inthecounterfactualmodel.

Figure37 Survivalrateinforecastandcounterfactualmodel

Source:Employmentgrowthsimulation

Similarly, Data Pitch stimulates the average number of employees for surviving start-ups. TheaveragenumberofemployeesishigherintheforecastcomparedtothecounterfactualwithoutDataPitch.WithDataPitch,theaveragestart-upisexpectedtogrowto17employees.Thismayonlybe14withoutDataPitch.

52 Themodel formally treats both cohorts as one, combined groupand starts theprojection at the start of theprogramme for this“combinedcohort”.Forthesakeoftheprojection,weequatethestartofthe“combinedcohort”withthestartofCohort2inSpring2019.



Figure38 Averagenumberofemployeesinforecastandcounterfactualmodel


Giventhis,totalemploymentispredictedtobesubstantiallyhigherwithDataPitchfunding.WithDataPitch,totalemploymentispredictedtoincreaseby407to717employees,oranannualgrowthof32%.WithoutDataPitch,thegrowthratemayonlybe18%oratotalincreaseof202employees.

Figure39 Totalemploymentinforecastandcounterfactualmodel


3.9.2 Revenuegrowth

Thesimulationmodelestimatingemploymentgrowthwasalsoappliedtopotentialrevenuegrowth.Beforediscussingtheresults,importantcaveatsmustbeaddressed.Thegrowthratesintheforecastprojectionareextrapolatedfromrevenuegeneratedduringandshortlyaftertheprogramme.Manystart-upsintheprogrammewerepre-revenueandthereforeshowedsmallornogrowthduringtheprogramme.Thiscarriesthroughintotheprojection,potentiallyunderestimatingthetrueimpactofDataPitch.

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The growth rates in the counterfactual scenario are based on the performance of unsuccessfulapplicants. It isplausiblethatunsuccessfulapplicantsthatmanagedtogeneratestrongergrowthweremorelikelytoanswerthesurvey.Therefore,theassumptionsinthecounterfactualscenariomaybebiasedinfavourofhighergrowth.Furthermore,thecounterfactualscenarioismuchlesscertainasitisbasedonsubstantiallylessdata(9observation).

Thismeansthattheforecastandcounterfactualshouldbecomparedwithcaution.Moreover,theprojectedrevenuegrowthisdriven–inbothscenarios–byexceptionalperformanceofindividualcompanies. These start-ups reported revenue increases ofmore than €1million over one year,which is plausible for tech sector start-ups, but makes predictions of aggregate performancedifficult.

Intheend,themodelassumptionsintheprojectionmodelaregroundedinrealityastheyarebasedon data reported by successful applicants during and after the programme and unsuccessfulapplicantsinasurvey.However,likeanymodel,ithidescomplexityforthesakeoftractabilityandtheexactassumptionsituseswillinfluencetheresults.

Consideringthesecaveats,thefiguresbelowpresentaverageandtotalrevenueprojectionsfortheperiod2019-2022.Theforecastmodelpredictsthataveragerevenuewillgrowfrom€147,723 in2019 to €833,555 in 2022. Combinedwith the success/failure rate of businesses, this implies agrowthoftotalannualrevenueto€35,784,385from€6,896,000.Thisisequivalenttoagrowthrateof73%peryearupuntil2022.Thisgrowthratemaynotbesustainableinthelongrunbutshowsthatadecentproportionofstart-upsfundedbyDataPitchshouldbecomesustainable.

Figure40 Averageannualrevenueinforecastandcounterfactualmodel

Source:Revenuegrowthsimulation

Inthecounterfactualscenario,averagerevenueisexpectedtogrowto€1,873,747andtotalrevenueto€72,374,032.Thisimpliesanannualgrowthoftotalrevenueof119%.Thecounterfactualscenariopredictshighergrowththantheforecast,whichmayshowthatDataPitchhashadlessimpactonrevenue growth than on employment. However, as noted above, the projectionsmay bias thecounterfactualscenarioupandtheforecastscenariodown.Consideringthesebiases,thesimulatedperformance difference between the two scenariosmay bemuch less than shownhere. This isexacerbatedby the fact thatpre-revenue funding for start-ups inEurope iswidelyavailableand



unsuccessful Data Pitch applicants had access to other sources of funding, including otheraccelerators.

Figure41 Totalannualrevenueinforecastandcounterfactualmodel

Source:Revenuegrowthsimulation

Overall, the simulation model provides a reasonable estimate of future impacts of Data Pitchparticipantsintermsofemploymentandrevenuegrowth,althoughthehighannualrevenuegrowthwillnotbesustainableinthelongrun.Smallbusinessgrowthnaturallydeclinesasbusinessesgrowlarger, as shown in Figure 42. Employment is predicted to grow with 32% whereas revenue ispredictedtogrowwith73%peryearupuntil2022.

Figure42 Growthstagesofsmallbusinesses

Source:Churchill&Lewis(1983)

Importantly,thefocusontheDataPitchstart-upsdoesnotconsidertheimpactofDataPitchondataprovidersandontheopeninnovationecosysteminthefuture.Qualitativeevidencestronglypoints to a positive impact on future participation in open innovation by participating data

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providers,whichislikelytomultiplythebenefitoftheprogrammeinarelativelyshortspaceoftime.Forsomedataproviders,openinnovationthroughtheDataPitchprogrammeledtonewservicesandproductstheycanuseintheiroperations.Forothers,theco-operationwithstart-upsledtoanappreciationofthevalueoftheirdataandthevalueofopeninnovation.DataPitchgeneratedanumberofusecasesthatcanbeusedasevidenceforotherpotentialdataproviderstoengagewithopeninnovationand“DataPitch-like”datasharingarrangements.

3.9.3 ReturnonInvestment3yearsafterDataPitch

FollowingSection3.5.2,thepredictedrevenuegeneratedbystart-upsfundedthroughDataPitchcanbesetoffagainstthetotalmonetaryresourcesmadeavailabletoDataPitch.ThetablebelowreproducestheReturnonInvestment(ROI)calculatedinthatsectionandextendsthistoincludetheROIbasedonannualrevenueby2022fromtheforecastprojection.

Notethatinthetable,theROIduringtheprogrammeandrespectively6and12monthsfollowingtheprogrammearecumulativeasdescribedinSection3.5.2.UsingcumulativesalesisappropriateforthecalculationofROIsincethiscapturesallbenefitsthatstemfromtheinitialinvestmentovertime.

TheROIbasedontheprojection,however,isnotcumulativebutbasedonpredictedannualrevenueby2022.Theannualrevenuesintheforecastprojectionhavenotbeendiscounted.Therefore,thecumulativeROIderivedfromtheforecastcanbearbitrarilychangedbyincreasingordecreasingthetimespanoverwhichitiscalculated.ThisisavoidedbyprovidinganROIbasedonannualrevenue.

Table12 ReturnonInvestment;projectedrevenue

Duringtheprogramme 6monthsfollowingtheprogramme


Projectedannualrevenueby2022

23% 91% 103% 459%Note:dataon6and12monthsaftertheprogrammearebasedondatafromCohort1only.ThefiguresaccountforthisbyadjustingtheinvestmentprovidedthroughDataPitchbasedonthenumberofstart-upsforwhichdataisavailable.


Asthetableshows,revenueisexpectedtobe459%ofthetotalresourcesavailabletoDataPitchby2022. This shows that the investment in Data Pitch will likely produce viable businesses withsubstantialrevenues.

3.10 Additionality

Takingintoaccountfactorsthatmaydecreasethenetpositiveimpact(programme‘additionality’)ofDataPitch,weconcludethattheimpactofDataPitchislikelytobestrictlypositive,especiallyonce longer term impacts are taken into account: Data Pitch laid the groundwork for ongoingengagement in open innovation by somemajor European data providers,who appear ready topursue their own initiatives going forward, and acted as a demonstrator for data-driven openinnovation.



Inanyevaluation,aquestionthatneedstobeansweredishowmanyofthebenefitsthatcanbeattributedtotheprogrammewouldhaveoccurredanyway,andtowhatextenttheprojectproducessecondaryconsequences(bothpositiveandnegative)53.Relevantconsiderationsare:

¢ Deadweight:howmuchoftheimpactwouldhavebeenachievedanyway?Giventhegoodavailabilityofearly-stagefundinginEurope(confirmedbythefactthatmanyoftheDataPitchparticipantshadreceivedexternalfundingfromothersourcesbeforetheyenteredtheprogramme),itislikelythathigh-qualitystart-upsliketheonesselectedforDataPitchwould have succeeded in some form anyway. Indeed, the survey of unsuccessfulapplicantssuggeststhatsomeofthestart-upsthatwerenotselectedachievedimpressiverevenuegrowthwithoutthesupportfromDataPitch.However,inrelationtothespecificgoalof fosteringopen innovationthroughdatasharing, theevidence is strongthat theparticipantswouldnothavebeenabletoaccessthedatathatDataPitchenabledthemtoaccess.Moreover, thereare fewprogrammes thatworkwithdataprovidersaswell asstart-ups54, it is unlikely that the benefits for data providers and thewider ecosystembenefitswouldhavebeenachieved. Inthisregard,DataPitchaddressedaclearmarketfailure, namely the frictions that inhibit mutually beneficial data sharing acrossorganisations,whichconstitutesanet(strictlyadditional)benefitoftheprogramme.

¢ Leakage:thereisapossibilitythatsomeofthebenefitofDataPitchwill‘leak’assupportedstart-upsmovetheiractivitiesoverseas.Oneofthestart-upsremarkedinaninterviewthattheywereconsideringapplyingtoYCombinator,whichwouldentailarelocationtotheUnitedStates.However,forthevastmajorityoftheorganisationsinvolvedinDataPitch,thisisunlikely.

¢ Displacement:itispossiblethatDataPitchdisplacedsomeinvestmentbydataproviders.SomeofthedataproviderswereinterestedinopeninnovationbeforecomingintocontactwithDataPitchandmighthavepursuedcomparable initiativesundertheirownsteam.However, it is clear that Data Pitch offered unique advantages in addressing theinformation-basedmarketfailuresthataretypicalforthedataeconomy,therebygettingprojectsoffthegroundthatwouldnothavehappenedotherwise.

¢ Substitution:forboththedataprovidersandthestart-ups,itislikelythatsomedegreeofsubstitution of other inputs for inputs fromData Pitch occurred. Some of the servicesprovidedaspartofDataPitch(legaladvice,marketingsupport,GDPRtraining,etc.)wouldhavebeenavailablefromthird-partyproviders.However,theidentificationofparticipantrequirements and the tailor-made package provided by Data Pitch, based on theconsiderableexperienceoftheconsortiumintheopeninnovationspace,makesitlikelythatsuchthird-partyprovisionwouldhavebeenlessefficientandwouldnothavebeenusedtothesameextent.

¢ Long-termimpactsarelikelytobestrictlypositive.Byactingasademonstratorforopeninnovation and involving new organisations in the practical implementation of openinnovationprojects thatenableddata sharing,DataPitch canbeexpected togenerateindirectandlong-termbenefits.Specifically,DataPitchlaidthegroundworkforongoingengagement in open innovation by somemajor European data providers,who appearreadytopursuetheirowninitiativesgoingforward.

53SeeHMTreasury(2018).54AprominentexampleoutsideDataPitchistheEuropeanDataIncubator(https://edincubator.eu/)

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3.11 Evidenceontheimpactofstart-upincubators&accelerators

Theevidenceontheimpactofstart-upincubatorsandacceleratorsismixed.Thereissomeevidencethat incubatorshelpstart-upsgrow.Furthermore, there is someevidence thatacceleratorshelpwithgenerating,interalia,investment,revenueandcustomerbases.TheexperienceofDataPitchisconsistentwiththegeneralpatternoffindingsintheliterature.

ToputtheperformanceofDataPitchintoperspective,thissectionsummariseskeyfindingsofthewider literature on the impact of incubators and accelerators. The key sources is a study byinnovationagencyNestaonbehalfoftheDepartmentofBusiness,EnergyandIndustrialStrategy(Boneet al., 2019),whichprovides a literature reviewof previous researchon accelerators andincubatorsacrosstheworld.

Establishingtheimpactofincubatorandacceleratorprogrammesischallenging.Firstly,thereisnosingleacceptedsuccessmetricbywhichprogrammesshouldbeevaluated.Moreover,somemetricsthatseemtoberelatedtosuccessmayhaveamorecomplexinterpretation.Oneexampleisthefailurerateofprogrammeparticipants.Highfailureratesmaynotbebad.Non-viablebusinessesshouldbeclosedsoonerratherthanlater.Goodprogrammesmayhelpentrepreneursunderstandthatcertainbusinessesarenotviable,leadingtohighfailureratesthatnonethelesshaveapositiveimpact.ForprogrammeslikeDataPitch,focusingonfailureratesmayriskleavingunviablefirmson“lifesupport”,i.e.moneyinjectionsintostart-upsthatshouldbeallowedtofail.

Secondly, programmes typically select participants that are alreadymore likely to succeed. Thisselectionbiasmeansthatprogrammeparticipantsshoulddobetterthanunsuccessfulapplicants,even if a programme would not provide any services. This selectiveness also creates a signal.Participationinaprogrammemaysignaltopotentialinvestorsthatabusinessisviable,becausethebusiness already passed an “inspection round”. This signal may attract investors, even if theprogrammeitselfdoesnotprovideanyservices.InDataPitch,especiallytheparticipantsinsectorchallenges noticed the potential of signals. Being selected for the programme provided sectorchallengeparticipantswithasignalthattheycouldbetrustedwhichstrengthenedtheirrelationshipwiththeirdataprovider.

Notwithstandingtheseissues,someresearchontheimpactofacceleratorsandincubatorshasbeendone.Theresultsaremixed.

Researchon incubators suggests that these typesofprogrammesmayhelpparticipantsgrow inemployeesize.However,theimpactonsurvivalandfailureratesaremixed.Thesemixedresultsaredifficult to interpretbecause,asnotedabove,bothhighand lowfailure ratescouldbeasignofsuccess.Regarding innovation, little researchhasbeendonebut incubatorsmaynotnecessarilystimulateinnovationasproxiedbyR&Dintensity.

Evidenceonaccelerators isstrongerbutstillmixed.There issomeevidencethatparticipation inacceleratorsmayhelpbusinesses:

¢ attractinvestment;¢ growemploymentandrevenue;¢ reducetimetoacquisition;¢ closeiftheyareunviable;and,¢ gaincustomers.



TheabilitytohelpbusinessesgaincustomersmaybeparticularlyrelevantforDataPitch.TheDataPitch programme did not only provide funding and services, but also matched programmeparticipantswithpotentialcustomers,i.e.dataproviders.

However,thepotentialbenefitsnotedaboverarelyholdforallacceleratorprogrammes.Foreveryacceleratorthatseemstohelpgrowemployment,thereisanotherforwhichnoimpacthasbeenfound.Overall,researchongeneralimpactsofincubatorsandacceleratorshasbeeninconclusive.

Beyond the impact of the programmes themselves, there has been research into the impact ofprogrammedesign.Thereissomeevidencethatsupportservicesthatgobeyondmerefundinghavea positive effect on the programme’s performance. There is moderately strong evidence thatprogrammesthatprovidenetworkingopportunitiesandmentoringschemesaremoresuccessful.This shone through in the Data Pitch programme. Advisors and mentors55 were generally wellreceivedandconsideredusefulbyparticipantsinterviewedforthisassessment.Butasbefore,thereisnosupportservicethatisuniversallyassociatedwithsuccess.

3.12 ComparisonwithODINEandNesta-reviewedaccelerator

DataPitchsharescharacteristicswithotheraccelerators,someofwhichalsofocusondata.

DataPitchcomparesmostnaturallywiththeODINEaccelerator.DataPitchcomparesfavourablywithODINEintermsofestimatedfutureimpactonemployment.

This section provides a high-level comparison between Data Pitch, ODINE (IDC, 2017) and thecorporate accelerator studied by Bone et al. 2019, for simplicity referred to as the ‘Nestaaccelerator’56.

Table13 Comparisonofacceleratorprogrammes

Accelerator FundingMentoringand

advisors

Training,workshopsand/or

webinars(Shared)office

spaceDataPitch ü ü ü ODINE ü ü ü ü Nestaaccelerator ü ü ü

AcceleratorAccessto(investor)

network Peer-networkingAccesstotechnology

Accesstocloudand/orother

servicesDataPitch ü ü ü ü ODINE ü ü ü Nestaaccelerator ü ü ü

55InDataPitch,theroleofadvisorandmentorweredefineddifferently.TheadvisorcamefromwithintheDataPitchconsortium,trackedtheparticipantsthroughouttheprogrammeandcouldprovidegeneraladvice.Mentorswereexternaltotheconsortiumandwerecalledintoprovidemorespecificadvice(e.g.technicalorlegal)attherequestofparticipants.Thereseemstohavebeensomeconfusionabouttheterminologyregardingadvisorsandmentorsamongprogrammeparticipants.56Boneetal.(2019)originatedfromNesta,hencetheacceleratorisindicatedasthe‘Nestaaccelerator’.NotethattheacceleratorwasnotrunbyNestaitself.Thereportdoesnotnametheorganisationbehindtheaccelerator.

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Accelerator

LegalcounselregardingIPanddataprotection

Helpwithcontractual

arrangementsPRand

communication

DataPitch ü ü ü ODINE ü Nestaaccelerator

Accelerator Virtual?Centredaround

data? Duration

DataPitch ü ü 6months ODINE ü ü 6months Nestaaccelerator 4months

Source:DataPitchdocumentation,IDC(2017),Boneetal.(2019)

Basedonthe forecast impactmodel in IDC(2017),companies fundedbyODINEareexpectedtogeneratecumulativerevenuesof€110million intheperiod2016-2020with784 jobsgenerated.Thiscorrespondstoaveragerevenuepercompanyofaround€1millionby2020.ODINEachievesthisbyacceleratingtimetomarket,improvingideasandimprovingteamskills.InascenariowhereODINE would not exist, the funded companies would have generated half as much cumulativerevenueand228fewerjobs.

CompaniesfundedthroughODINEtypicallyusedmultipletypesofOpenData,withaparticularfocusongeospatialandenvironmentaldata.Furthermore,companiesfromcountrieswithmorematureOpenDatamarketsweremorelikelytobesuccessfulintheirapplication.BothpointtowardsthevalueoffosteringanenvironmentconducivetoOpenData.

The corporate accelerator reviewed by Bone et al. (2019) – headquartered in the US but withlocations worldwide – was analysed using state-of-the-art and robust analysis methods57.Participationintheacceleratorisassociatedwithhigherlikelihoodofsurvival,higheremploymentgrowthandmorefundraisingwithinfiveyearsfromtheapplicationstage.Theacceleratorincreasedwebpresence–aproxyforsurvival–by50%,helpedacceleratedbusinessesgrowfrom1-10to11-50employeesandincreasedfundraisingby77.6%58comparedtosimilarbusinessesthatwerenotaccelerated.

ThesefigurescanbecomparedwiththevaluegeneratedbyDataPitchsofar,andtheforecastedvalueby2022.Asshowninsection3.5,bothcohortscombinedgeneratedatotalvalueofnearly€6.5million59 and 114 additional jobs while still in the programme. Including post-accelerationperformancebycohort1(seesection3.5.1),confirmedvaluegeneratedapproximatelyequals€16.6million60 and additional employment of 140. The forecast model of section 3.9 predicts thatemploymentgeneratedbyDataPitch-fundedstart-upswill riseto717,oran increaseof407,by2022andthatannualrevenuewillgrowwith72%peryearuntil2022.

57Moreprecisely,Boneetal.(2019)usedFuzzyRegressionDiscontinuityDesign.58Theaverageapplicanttothisacceleratorraisedabout$150,000.Therefore,thispercentageisequivalenttoaround£117,000.59Sumofsales, investmentsandefficienciesasreported inthebi-weeklymonitoringupdates.Valuegeneratedexcludingefficienciesnearlyequals€5.7million.60Sumofsales,investmentandefficienciesasreportedinthebi-weeklymonitoringupdates,6monthsprogressupdatesand12monthsprogressupdates.Onecohort1participantreportedadditionalinvestmentintherangebetween€1and€5million.Thishasbeenincludedinthiscalculationas€2.5million.Valuegeneratedexcludingefficienciesisapproximately€15.6million.



DataPitchcomparesmostnaturallywiththeODINEaccelerator.DataPitchoutperformsODINEinemploymentgeneration.Asnotedabove,DataPitch-funded start-upsarepredicted togenerateadditionalemploymentequalto407by2022.Overthesametimespan,theODINEprogrammeispredictedtogenerate228additionaljobs(IDC,2017,p.47)61whilefundingmorestart-ups.

61Inparticular,IDC(2017)predictsthatoveratimespanofthreeyears,ODINE-fundedstart-upswillhavegenerated774jobsfromabaseof546.Hence,theincreaseequals228.NotethatIDC(2017)forecastsODINE’simpactforfouryears.

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4 Casestudies

Thesecasestudieswerecreatedfrominterviewswiththechosenstart-ups(andinsomecases,thedataproviders),aswellasdatacollectedaspartoftheDataPitchprogramme.Thesestart-upswerechosen topresentdiverseexamplesof casesofhowDataPitchhaspromoteddata-drivenopeninnovationforcompaniesofdifferentsizes,maturitiesandsectors.

The solutions covered in the case studies range from supply-chain optimisation for industrialmanufacturers,totrafficsoftwarewhichcanpredictsafetyandaccidentriskalongaroute.AswellasgivingaflavourofthebreadthofinnovativesolutionsinstigatedbyDataPitch,thecasestudiesalsoprovideanoverviewofthesupportreceivedandoverallbenefitsrealisedduringtheacceleratorandafterwards,includingthefutureoutlook62.

4.1 OBUU

Website:https://www.obuu.es/

FoundedYear:2015

Number/compositionofstaffonentry:6

Challenge:DP3-2018-Developingapplicationsacrossmanufacturing, logisticsandsupplychainwithGreiner

SolutionTagline:StockWatch:optimisationofsparepartstocksforindustrialMachinery

62Twoofthestart-upsrepresentedinthecasestudies(OBUUandPredina)havegivenpermissiontoreportdetailedperformancedata.High-levelsummariesareprovidedintheothercasestudies.


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4.1.1 Solution63

Aspartofthedatapitchprogramme,OBUUcontinuedtodevelopasoftwaresolutiontheyhadbeenworkingon since 2015 (referred to as ‘StockWatch’). Thissolution is an online, cloud-based platform thatenables users to map out shipping, storage andmanufacturingflows.Thesoftwarethenanalysestheseflowswithinputdatatofindwherefurtherefficienciescanbeattained.

AspartoftheirworkwithGreiner,OBUUutiliseddatafromfiveofGreiner’sproductionplantstodeterminetheriskofsparepartsnotbeingavailablewhenneeded(Stock Risk of Shortage; ROS), the average time thesystemisdownwhenapartisnotavailable(MeanDownTime;MDT),andtheoverallinvestmentinthestockavailable.ThesethreefactorsservedaskeyperformanceindicatorsfordeterminingtheefficiencyofGreiner’ssupplychain.UsingStockWatch,OBUUfoundthatGreinercouldachieveareductionoffixedassetinvestmentof“atleastadouble-digitpercentage”.

StockWatchiscurrentlybeinglicensedbyOBUUonaSaaS(softwareasaservice)basis.Aspartofthe Data Pitch Programme, Greiner obtained a one-year license. OBUU have also licensed thisupdatedsoftwaretofurtherclientsinindustriessuchasenergyandaerospace.

4.1.2 Data

OBUU received 10 megabytes of data from Greiner;OBUUreceivedafullcopyofthisdataandthereforehadfullcontroloverhowthedatawasusedandhandled.Thedata contained information from one of Greiner’sproductionplantson:

¢ Logs and reports of failure cases for Greiner’smanufacturingmachines;

¢ Logisticsorders; ¢ Maintenanceandusagehistory;

63 The charts in these case studies display responses to a survey completedbyData Pitch participants. 41 out of the total 47 firmsresponded,thexaxisreferstotheanswersgivenbyeachfirm.Onedotrepresentsonefirm’sresponsetothisquestion.

NumberofDatasetsused:12

Update frequency of the data:Occasional/Irregular

Levelofcontroloverthedata:Full

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As Greiner were near the beginning of a new digitalisation phase, this data was collected andprocessedforthefirsttimebeforebeingdeliveredtoOBUU.

However, although the dataset contained the full set ofvariables OBUU were looking for, both OBUU and Greinerremarked that the data contained fewer observations thaninitiallypredicted(4,000).Asaresult,OBUUchangedtheirinitialstrategyoftestingonescenariothoroughly,tothecreationand

testingoffourseparateandsmallerscenariosinstead.

4.1.3 Benefitsfromtheprogramme

AccesstoadataproviderwasanimportantpartofOBUU’smotivationforparticipatinginDataPitch.AsOBUUalreadyhadasolutiontheyhadbeendeveloping,theysawDataPitchasanopportunitytoworkwithalargecorporateclientsuchasGreiner.Theystatethatthedatatheyreceivedprobablywouldnot havebeeneasily accessible outsideData Pitch.OBUUhave also commentedonhowpleasedtheywerewiththeir interactionswithGreiner,especiallywithregardstotheactiveroletheirpointofcontactwastaking;anexampleofthisactiverolewastheircontactproposingpossiblesolutionsandroutestheirprojectcouldundergo.

ForOBUU,akeybenefitofDataPitchwasthattheyweregivenaready-madeusecaseofdirectrelevancetoanindustrycustomer.Additionally,thisclosecollaborationwithGreinerenabledOBUUtoenteranew industrywhichwillpotentiallyenable them to sign contractswithother firms inadjacentindustries.

OBUUalsoreceivedjustunder€100,000infundingfromDataPitch.Over80%ofthisfundingwasspentonpayingthesalariesfortheirsevenemployees,withthebulkoftheremainingfundsbeingspentontravellingformeetingswithGreiner,DataPitcheventsandmeetings,andattendingevents.

Size of the data: 0.01GB,4000observations


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OtherresourcesandbenefitsgainedfromDataPitch included access tomentors, webinars &events. Whilst OBUU tried to attend themajorityoftheseevents,theyappreciatedthattheydidnothavetoattendeventswhichwerenot relevant for their current business stage.The provision of AmazonWeb Service creditsfromDataPitchalsoenabledOBUUtohosttheirStockWatch solution for the next couple ofyearswithoutcost.

Atthestartoftheprogramme,OBUUdescribedsuccessfulparticipationwiththefollowinggoals:

¢ To test their algorithms within a mass production environment and acquire an earlyadopteroftheirupdatedsoftware;

¢ Toacquireanewclientanddiversifyintoanewindustry;¢ ToattendconferencesandeventsorganisedbyDataPitch,wheretheywillbeabletomake

newcontactsandshowcasetheirprojectstoagentsfromEuropeanIndustries.

OBUUhaveexpandedtheirsoftwareandtesteditusingGreiner’smanufacturingandlogisticsdata,gainedanumberofnewclientsaswellasattendedeventssuchasMaintenanceNextinRotterdamand the Paris Air Show; they also planned to attend the Packaging Innovations convention inNovember2019.Asaresult,OBUUachievedthegoalstheysetoutatthestartoftheprogrammeandattainedseveralbenefitsasaresult.

4.1.4 AccelerationPerformance

During the accelerator period, OBUU made agrossprofitofalmost€40,000,partofwhichcamefromclosingacontractwithaclientintherailwayindustry. At time of application, OBUU hadaccumulatedalifetimerevenueofapproximately€700,000 and during their six months in DataPitch,theymade€224,230inrevenue.

Additionally, OBUU have received €60,000 ininvestmentalongside theamount received fromData Pitch. This investment took the form offunding for an R&D project into new statisticalalgorithms. At the time of application to DataPitch, OBUU had raised a total of €602,000. Inaddition to this, they increased their employee

countfromsixonentry(hiringonejustbeforetheprojectbegan)tonineonexit.

Revenue(€) GrossProfit(€) Investment RealisedEfficiencies

Employeeincrease

Increaseinpayingusers

224,230.5 39,901.76 60,000 15,000 2(7–>9) 1(3–>4)Note:Thesemetricstrackprogressthroughoutthe6-monthacceleratorperiod.Source:Bi-weeklymonitoringupdatesandOBUUmilestonereviews

ResourcesDataPitchfundingenabledOBUUtoacquire:Softwaredevelopmentskills,Datascience skills, Domain knowledge, Dataanalysisskills&BusinessManagementskills.

Would you have been able to access thesamedatawithoutDataPitch?Yes,alreadyhadconnectionswithdataproviders.

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4.1.5 Sincetheprogramme/Outlook64

OBUU have taken part in several similar programmes such as the Airbus Bizlab, ESA BusinessIncubationCentreandtheRenfeTrenLabstart-upincubator.Movingforward,OBUUarechoosingtoramp-downtheirparticipationinacceleratorsandinsteadfocusongrowingintheindustriestheyarenowestablishedin.Asaresult,theyareplanningtobe‘pickierandmoreselective’.

In line with these plans, OBUU are focused on retainingtheircurrentcustomers,andcreatingrecurrentdemandfortheirservices.TheyplantodothisbylaunchingaFreemiumcampaigntoshowcasetheirproduct,aswellascontinuingthe technical development of their product. Thisdevelopmentincludesimprovementstouserinterfaceandabilitytodealwithahighercomplexityofsolutions.

4.1.6 Insights

OBUUhasbeensuccessfuleventhoughtheirinitialapplicationfortheopeninnovationchallengewas rejected. This suggests that success depends on achieving the right match between dataproviders (or challenges) and start-ups. Additionally, the increase in the number of matchedcompaniesinthesecondcohortmaysuggestthatthematchingprocess(andexperienceofthosematching)hadimproved.Futureprogrammesshouldconsidertherightmixofdataprovidersandstart-ups,andpossiblyincreasethenumberofdataprovidersrelativetothenumberofstart-upstoincreasetheprobabilityofsuccessfulmatches.

64 Inthesurvey,Coresolutionwasdefinedas ‘Markets&customersascurrently identified’;Futureadaptationsofcoresolutionwasdefinedas‘newmarkets/applicationareas/customergroups’.

Effortrequiredforscalability:

Coresolution–Minimal

Futureadaptationsofcoresolution-Medium


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Thedataproviderwasmatchedwith5start-upsandreflectedthatthismayhavebeentoomany.Forlargecompanies(suchasGreiner),workingwithstart-upsonopen-innovationprogrammesmayrepresentanewchallenge.Therefore,theymaynotfullybepreparedtoprovidethesupportneededtohelpmaximisetheimpactwhichthesestart-upscanprovidethroughclosecollaboration.OBUUandGreinerhavebothstatedthattheirrelationshipbenefittedfromtheenthusiasmpresentfrombothparties.Futuresuccessinsimilaropeninnovationprojectsmaybeinfluencedbyahighlevelofeffort,enthusiasmandsupportfromallparticipatingparties.

DataPitchworkedefficientlyinfacilitatingthematchbetweenOBUUandGreinerandwasseenasproviding a useful and comprehensive service, with minimum burden on the start-up. OBUUappreciated that therewasnoobligation to takepart inpartsof theprogramme thatwerenotrelevantforthem.Apartfromdataaccessandcollaborationwiththedataprovider,funding(mostlyforadditionalstaff),cloudspacetohostthesolutionandsomelegaladvicewerethemostusefulelementsofDataPitch.

OpenInnovationprogrammesfunctioninanecosystemoffundingopportunitiesthatincludeotheraccelerators,bothcorporateandpubliclyfunded.CompanieslikeOBUUoftenreceivefundingfromdifferentsources.Attributingstart-upsuccesstoanyoneoftheseisdifficult,andamoreholisticviewshouldconsiderinteractionsbetweendifferenttypesofsupportandtheircumulativeeffect.

The example ofOBUU illustrates the importance of ‘introductions’ for start-ups to enter a newmarketorindustry.Thisreflectsthefactthatchallengesfacedbybusinessareoftensimilaracrosssectors(e.g.supplychainoptimisation),butthatbarrierscontinuetoexistthatlimittheportabilityofsolutions.The‘openness’requiredforopeninnovationtoworkismorethanopennessintermsof data access, it also includes domain,market knowledge, trust and openness to engagewithentitieswhosetrackrecordisindifferentsectors.However,DataPitchalsoclearlyenabledthestart-uptoaccessdatathatwouldhavebeeninaccessibleotherwise.

Theexamplealsoshowstheimportanceofbuy-inonthepartofthedataprovider,bothintermsofaninstitutionalcommitmenttoopeninnovation,andintermsofmotivatedandskilledpersonneldriving thecooperationwith thestart-up.Thedataproviderhadpreviousexperiencewithopeninnovation and R&D cooperation more generally. Although the data provider had previousexperience,theircollaborationwithOBUUprovidedausecasefortheirdatawhichtheyhadnotpreviouslyconsidered.GreinerdidnotthinktheyneededOBUU’sproductatthestartoftheprojectbut ended up using StockWatch as part of their business operations. Data Pitch has thereforeprovidedanexampleastohowadataprovider’sassumptionsregardingtheusefulnessoftheirdatacanbechallengedthroughcollaborationandopeninnovation.

In many ways, the OBUU-Greiner match is an ideal example of the open innovation model asimplementedbyDataPitch,withreallearningonbothsidesandauseableproductattheend.Thestart-upembracedtheopeninnovationmodelexemplifiedbyDataPitchandintendstouseittoenterothermarkets.However, it isnoteworthy thatOBUUhasbeenoperating for anumberofyears, participated in other accelerators and had a working commercial product going into thechallenge.Thismaysuggestthattheywerewellpositionedtoconductaneffectiveandsuccessfulprojectwith theirdataprovider, as theydidnothave to contendwithearly-stage concernsandconstraints(suchasstaffingandbusinessdirection).

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4.2 Bliq

Website:www.bliq.ai

FoundedYear:2018


Challenge:SC7-2018-Innovativesolutionstoimprovemobilityandreducetrafficcongestion

SolutionTagline:Liveparkingmapsfordevelopersinmobility

4.2.1 Solution

Bliq,formerlyknownasAIPARK,createliveparkingmaps. Bliq provide these maps and APIs fordevelopers, who can then develop solutions thatshow downstream users where to find parkingspots.

BeforeDataPitch,parkingspacesprovidedonthesemapswouldhavetobemanuallydrawnandenteredbyBliq.ThesolutiontheydevelopedaspartofDataPitchenabledthemtoautomatethisprocessusingmachinelearning.


4|Casestudies

As an example of the current and potential user base, Bliq’s solution can be built into carentertainmentsystemstoshowthedriverthelocationofavailableparkingspacesnearthem.

4.2.2 Data

Bliqusedover500GBofdataintheconstructionoftheirsolution.Thisdata,whichtheyreceivedfromtheirdataprovider,tooktheformofhigh-qualitysatelliteimages.Bliqnotedthatalthoughthequalityofthisdatawasveryhigh,theupdatefrequencywasrelativelylow,at12–18months. Bliq did not have access to a full copy of thisdata,sotheyonlyhadpartialcontroloverthisdataset.

Bliqwould havebeen able to access this datawithoutDataPitch,but their solutionwouldhavebeenmuch lessmature.Bliqacquiredthisdatathrougharevenue-sharingagreementwiththeirdataprovider,whoprovidedthedatafreeofcharge,asBliqwereinvestigatingausecaseHexagon

hadnotpreviouslyconsidered.


Themajorityof the funding, receivedfromDataPitch,wasspentonstaffingcosts, includingthehiringof2additionalengineers.



Levelofcontroloverthedata:Partial

Size of the data:600GB, 20,200observations

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DataPitchfundingenabledBliqtopursuealineof developmentwhich theywould have beenunable to do otherwise. This funding allowedBliq tomodify their development roadmap toinclude additional functionality. Bliq hopes toobtain additional input data from otherproviders in exchange for access to theirsoftwareinthefuture.

The bi-weekly check-in Bliq had with their advisor also provided Bliq with on-going feedbackregardingactionstheycouldtakeforimprovementandnextsteps.Bliqcommentthatthisaccesstoanadvisor,alongsidetheaccesstothenetworkoftheDataPitchconsortium,hasbeenabenefitforthem. The network has provided an opportunity for Bliq to contact newpotential partners andclients.

4.2.4 AccelerationPerformance65

During the accelerator period, Bliq generatedan increase inrevenue.Additionally, theyalsogainedan increase in investmentand realisedefficiencies. At time of application, Bliq hadalready raised a significant amount ininvestment.

Throughout the accelerator period, theirmonthly userbase increased as well as theiremployee count, with the addition of newengineers.

Bliq also closed a deal with a Japan-basedautomotivesupplierandareconsideringfuturejointproduct-development.

65ApprovalhasnotbeengivenbyBliqtoreportprecisefigures

ResourcesDataPitchfundingenabledBliqtoacquire:Subjectmatter/domainknowledge,Softwaredevelopmentskills,Datascience/machinelearningskills

Would you have been able to access thesamedatawithoutDataPitch?Yes,but thedevelopedsolutionwouldbelessmature.


4|Casestudies

4.2.5 Sincetheprogramme/Outlook

Bliq’snextstepsrevolvearoundmarketpenetration.TheyarefocusingonenteringtheUSAmarket,aswellasexpandingtheirmarketpresenceinmultipleotherEuropeancountries.

Bliqarealsocontinuingworkontheirmobileapps,with the launch of an early access programme inBerlin for theirMobile Parking Vision app. Bliq arealso planning to develop a ride-hailing/taxi drivingapptobuildproprietarydataacquisition,aswellasservingasapotentialsidebusinessmodel.

4.2.6 Insights

Bliq is an example of a start-up forwhich the fundingwas themost important benefit of theirparticipationinDataPitch.ThefundingprovidedforBliqenabledthemtodevelopaspectsoftheirsolutionwhichtheyhadnotpreviouslyplannedtocreate.Thisproduct,whichwouldnothavebeencreated without Data Pitch, would also help with attracting future investment. This access toadditionalinvestmentcanresultinDataPitchhavingalonger-termeffectonbusinessdevelopmentbeyondtheimmediatebenefitsfromtheaccelerator.

Theexamplealsoillustratesthatstart-upscangainaccesstoexternaldataproviderswithouthelpfromthirdparties,inthiscaseusingarevenue-sharingarrangementwithanupstreamdataprovider.BliqalsoprovidesagoodexampleofAI-enabledefficiencygains(replacingalargelymanualprocesswithanautomatedonebasedontheexploitationofarichsetoftrainingdata).


Coresolution–Medium

Future adaptations of core solution -Medium

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4.3 Energeo

Website:www.energeo.co.uk

FoundedYear:2015


Challenge:SC3-2018-Increasingefficientenergycreationanduse

SolutionTagline:EnergeodeliverSustainableIntelligenceforthelowcarboneconomy

4.3.1 Solution

Prior to the programme, Energeo had developed abetaSoftwareasaService(SaaS)platformwhich:

¢ Deliveredintelligencecreatedfromavarietyof geospatial inputs (such as OS maps,LiDAR,satelliteimageryetc.)

¢ Integrated proprietary data to provide theend-user with a data analysis toolkit tosupport the identification of potentialenergyprojects.

As an example, this solution could use satelliteimagerytodeterminewhichrooftopswereidealforsolar panels. This solution was aimed at localauthoritiesandcouncilswhowouldhavethegoalofreducingcarbonemissions.

TheirDataPitchprojectwastocontinuedevelopmentonthisexistingplatformbyintegratingenergydata fromtheirdataprovider.Thisupdatedsolutionwouldbeable todeterminetheeffect that


4|Casestudies

additionalsolarpanelswouldhaveonsubstations(e.g.theeffectthatexcesssolarenergywouldhaveifitwouldhavetoflowbackintothegrid).

4.3.2 Data

Energeousedbothclosedandopendatasourcesaspartof their solution. Theyhadaprevious relationshipwiththeir data provider, who had provided them a datasetpriortoenteringDataPitch.Thisdatasetcontainednearreal-time data for substation/transformers located intheir data provider’s service area. This informationincluded details on transformer temperature, ambientandinsidetemperatures,transformerloadsandcurrents.

EnergeoalsoreceiveddataonhistoricenergyusagefromaLondonUniversity.Thisdatasetwasusedtoanalysetrendsandpatternsofenergyusageovera12monthperiod.Energeohadpartialcontrol

overthesedatasetsasthisdatawouldbeobtainedviaanAPIcallwhenrequired.

Theadditionaldatasetsusedincludeaddressdata,satelliteimageryandenergyperformancecertificates.


At the start of the programme, Energeo had 1full-timeemployee,and2part-timeemployees.They relied a lot on outsourcing andsubcontracting to complete their previouscontracts.With the funding theyreceived fromData Pitch, they managed to hire additionalemployees.Thisenabledthemtobringsoftwaredevelopmentandgeospatialresourcesintotheircompanyonafull-timebasis.


Updatefrequencyofthedata:Live

Levelofcontroloverthedata:Partial

Size of the data provided byWestern power Distribution:100observations,1GB

ResourcesDataPitchfundingenabledEnergeotoacquire:Subjectmatter/domainknowledge,Softwaredevelopmentskills,businessmanagementskills,marketing/salesskills

Would you have been able to access thesame data without Data Pitch? Yes, dataprovider gave access to this data to look atbeforeDataPitch,butDataPitchservedasthecatalysttobringtheseideastofruition.

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TheDataPitchprogrammeenabledEnergeotoprogressalongtheirplanneddirectionmuchmorerapidly.AlargepartofthiswasduetothecreationofanMVP(minimumviableproduct)whichwasmuchmoredevelopedthantheywouldhavebeenabletodowithoutDataPitch.

From their data provider, Energeo received technical supportwith interpreting andutilising theprovidedelectricalandenergy-relateddata (as thiswasoutside theirgeospatialexpertise).TheycommenttheydidnotreceivetoomuchsupportfromDataPitch(oraskedforit),astheyknewwhattheywantedtoachievefromtheprogrammeandsetuponachievingthis.


Duringtheacceleratorperiod,Energeogeneratedanincreaseinrevenue.Theyalsoincreasedtheiremploymentbymorethandoubleoverthecourseoftheacceleratorperiod.


Bytheendoftheprogramme,EnergeohadbeenacceptedintothePwC'Scale'GovTechacceleratorprogramme,whichbeganinOctober2019.TheyhavealsoobtainedacontractwithaBritishcountycouncil toprovidegeospatial intelligenceregarding lowcarbontechpotentialacross thecounty.

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Theyarecompletingthisworkaspartofaconsortium,inwhichEnergeo'selementcouldlastupto3years.

Additionally, Energeowerenamed themost innovative sustainable energyproducts and serviceprovider,andleadingproviderofEVcharginginfrastructuresolutionsintheSMEnews,‘EnergyandPowerAwards2019’.

AdditionalcontractsandbusinessEnergeohavesecuredincludeanorderfromanadditionalBritishcounty council for their solution as well as ongoingdialoguewithpotentialresellersoftheirdataservicesinSouthAfricaandIreland.

Energeo have a long-term ambition to turn thesoftware they created during Data Pitch into asubscription-based platform; they have begun initialresearchintothepossibilityofattainingthis.

4.3.6 Insights

TheEnergeosolutionisoneoftherelativelyfewinDataPitchthatcombinedatafromanumberofdifferentsources tocreateanewsolution (‘recombinant innovation’).Energeoused the fundingreceivedfromDataPitchtoacceleratealongawell-defineddevelopmentpath,enabledmainlybyhiringadditionalfull-timeresources.TechnicalandmarketknowledgesuppliedbyDataPitchwerealso important for the start-up. Data Pitch appears to have functioned like a more traditionalaccelerator. Ithighlightsthe interplaybetweeninnovativestart-ups,differentdataprovidersandthesupportecosystem(includingotheraccelerators).

AlthoughEnergeowasabletoaccessthedatausedwithoutDataPitch,itwastheparticipationintheprogrammewhichwasthecatalyst forEnergeotogenerate impact fromthedata.This isanexampleofhowprogrammessuchasDataPitchcanstillencourageinnovationthroughtheopeningofdata,evenincaseswheredatawasaccessibleforstart-upsbeforehand.



Future adaptations of core solution -High

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Energeo did raise a suggestion regarding the payment structure of the programme. Theycommentedthatforasmall,oryoung,start-up,cashflowiscritical,andthatthepaymentstructurecouldbemodifiedtoreflecttheneedsofthesesmallerSMEs.Specifically,thegapbetweenthefirstandsecondpaymentsmadeitdifficultforEnergeotomanagetheirprojectonthispaymenttimeline.


4|Casestudies

4.4 Predina

Website:https://www.predina.com/

FoundedYear:2016


Challenge: SC5-2017: How can we use data tomakemanufacturing, logistics andmaintenanceprocessesmoreefficientandabletosupportnewmodelsofuseandrepair

Solution Tagline: Guardian, AI technology software that predicts the risk of road accidentsdynamicallyandaccuratelyinrealtime:“Googlemapsforsafety”

4.4.1 Solution

Predina developed two solutions as part of DataPitch,which fed into a final product. The first ofthesesolutionswasanoptimisedalgorithmwhichwasusedtopredicttheriskofroadaccidents.Thesecondof thesesolutionswasadata layerwhichwouldconnect toa satnavproduct; thisproductwould thenbeable toalertdrivers to the riskofroadaccidents.Bothsolutionscametogetherinaweb-basedtoolwhichenableduserstoplantheirroutes, along with the expected safety of theirroute. The collective name for this softwarepackagewas‘Guardian’.

Thesesolutionsweretrainedanddevelopedusingdriver data from their data provider. This driverdata described the actions and characteristics of

truckdriversintheirdataprovider’sdeliveryfleets.

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DuringDataPitch,Predina’stargetedclientbasehadevolvedtotheprovisionofsystemsdirectlytocarmanufacturers,andtheprovisionofsystemsfortheautomotiveaftermarket.Theaimwastousethesesystemsforthepurposesofinsuranceanalyticsandfleettracking.

4.4.2 Data

Predinaobtained2millionobservations,totalling30GBinsize,of data from their data provider. This dataset containedinformationon:

¢ Comprehensive accidents data for all countries(countries which their data provider operate in):driver statement, images, videos of before, duringand after time of accident, affected parties,compensationcostsetc.;

¢ Nearmissdataforallcountries;¢ Comprehensive driver behaviour data: this included profiles for each driver, their

respectivedepots,previousaccidentstheyhaveexperiencedatwork,heavyaccelerationandquickbreakevents,amongstothersimilarindicators;

¢ Real-timedriverlocationtrackingthroughaTomTomAPI;¢ Databaseofschedulesgeneratedeachdayforalldriversbytheclient’sschedulingteam.

Inadditiontothis,Predinaalsousedseveralpublicdatasets(bothopenandpaid).Thesedatasetscontaineddataonweather,policerecords,trafficvolumes,fuelpricesandlocation-specificmappingdata. Predina had full control over the data received from theirdataprovider,aswellasthesepublicdatasetstheyusedfortheirsolution.




Sizeofthedatareceivedfromtheir data provider: 30GB, 2millionobservations


4|Casestudies


The Data Pitch funding was mostly spent onstaffing and salary costs, with the hiring ofadditionalengineers.Theyalsousedthefundingfor Amazon Web Services and Google Cloudcredits,whichwereusedtohosttheirproduct.

FromDataPitch,Predinahavecommentedthatthe ability to network with other start-upfounderswasa‘veryvital’benefitforthem.Theprovision of an environment where Predinacould meet other founders, as well as shareproblemsandsolutionswitheachother,wasasubstantialbenefitoftheprogramme.

Predina’s dataprovider helped shape theproduct and turn their algorithm into the commercialsolution. Predina note that the credibility this partnership provided for them was also hugelybeneficial.Lastly,thisprojecthadalsoprovidedthemausecasefortheirproduct.

ResourcesDataPitchfundingenabledPredinatoacquire:Subjectmatter/domainknowledge,softwaredevelopmentskills,ICTinfrastructure/hardware,OtherITskills,ICTinfrastructure/hardware,marketing/salesskills

Would you have been able to access thesame data without Data Pitch? Yes, had arelationshipwithDataProviderpriortoDataPitch.

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4.4.4 Accelerationperformance

During the accelerator period, Predina also raisedadditional investment and revenue.67 Predina alsoreceivedasizeableinvestmentfromtheirdataproviderattheendoftheacceleratorperiod.


AtthestartofDataPitch,PredinadefinedwhatsuccessfulparticipationinDataPitchlookedlikewiththefollowingachievements:

¢ Changeinuserbehaviourtowardsrisknotification–higheralertnessinhigh-risksituationsandlessaccidents;

¢ AnactualreductioninaccidentsforUK(andGermany);¢ AsignedcontractforbothUKandGermanmarkets;¢ Real-worldtestingofGuardianinGermanyandotherEUcountries;¢ Scalingofprojecttofullcommercialviabilityandmass-market.

In the six months after the programme, Predina havemanaged to show a real-world reduction of 25% inaccidents as a result of the implementation of theirsolution.TheywerealsoselectedfortheGoogleforStart-upscampusresidencyprogrammeandenjoyedadditionalfameintheformofbeingfeaturedinForbes30under30and being selected as Techworld’s ‘hottest machine

learningstart-ups’ in theUK for thesecondyear running.Predinaalso increasedtheiremployeecountby6andreceivedadditionalsales.

Inthesixmonthsfollowingthisperiod(sixtotwelvemonthsaftertheaccelerator),Predinahavecompletedversion2oftheirtechnology.TheyhavealsosecuredapilotwithmajorUKconstructioncompaniesaswellashiringanewCTO,twoadditionalmachinelearningengineersandabusinessdevelopment director. They are currently looking for further funding, both grant and strategicinvestmentcapitalfromautomotivecorporations.PredinaisnowalsolookingtowardslaunchingintheUS.

67PrecisefiguresareredactedatPredina’srequest



Futureadaptationsofcoresolution-Minimum


4|Casestudies

Predina’sproducthasbeensuccessfulbothtechnically(reductioninaccidents)andcommerciallysinceenteringtheDataPitchprogramme.

4.4.6 Insights

Predinaisanotherexampleofastart-upwithpre-existingrelationshipswithexternaldataprovidersthatusedDataPitchmorelikeatraditionalaccelerator,withfundingandnetworkingbeingseenasthekeybenefitsofparticipation.

Theopportunitiesthesestart-upshadtonetworkwithotherstart-upswasseenasavitalcomponentoftheprogrammeforPredina.Theseopportunitiesmaybeawaytoencouragefurthercollaborationtoprovidefurtherimpact.Whendesigningthechallenges,DataPitchstatedthatthesechallengesshould foster discussion and bring together potential solution creators. Future open innovationchallengescouldexaminethisareaasapotentialtrackforimpactgeneration.

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4.5 Radiobotics

Website:https://radiobotics.com/

FoundedYear:2017

Number/compositionofstaffonentry:2.5(2fulltime,1parttime)

Challenge:SC1-2017:Howcanweusedatatohelppeopleimprovetheirhealthandwellnessand/ormakehealthservicesmoreefficientandinclusive

SolutionTagline:AdvancingMusculoskeletalRadiologywithMachineLearning

4.5.1 Solution

ThesolutionRadioboticsdevelopedaspartofDataPitch involved automating X-ray analysis forhospitals. Specifically, this involved creatingmachine learning algorithms that could detectosteoarthritisonx-rayimagesofknees.

Radioboticsaimstoselltheirsolutionbutwillneedto receive regulatory clearance due to theirsolution’s status as healthcare technology. Theirmaingoalistoselltheirsolutiontohospitals,withacurrent focusonEuropean (Denmark,SwedenandUKspecifically)andAmericanclients.


4|Casestudies

4.5.2 Data

Aspartoftheirsolution,Radioboticsreceived20,000X-rayimages.Muchofthisdatawasprovidedbytheirdataprovider, although Radiobotics have also receivedimagesfrombothaDanishhospital,andresearchgroupsbased in the US. Radiobotics received copies of theseimagesandthereforehadfullcontroloverthisdata.

This data was usedto train theirdevelopedalgorithm.Radioboticscomment thatdiversity of images is important to avoid overfitting.Additionally, having multiple data providers ensures acontinuousflowofdata.


The Data Pitch funding was mostly spent onstaffing and salary costs. With this funding,Radioboticshired2newemployees.

ThroughDataPitch,Radioboticshavebeenabletodeveloptheiralgorithmtoastagewheretheycouldpresentittoinvestors.Basedonthis,theymanaged to attract additional funding fromEurostars after theDataPitchprogrammehadended. Radiobotics appreciate that Data Pitch enabled them to obtain funding without priortraction,asotheravenuesoffundingtheyhadresearchedrequiredevidenceofpreviousfunding,oraproductwhichwastobefurtherdeveloped.

RadioboticsstatedthattheyreceivedsupportfromDataPitchintheformofeventsandworkshops.Theprogrammehasallowedthemtobothimprovetheirprojectmanagementskillsandacceleratetheirmarketandfundinganalysisasaresult.

ResourcesDataPitchfundingenabledRadioboticstoacquire:Datascience/machinelearningskills,softwaredevelopmentskills,ICTinfrastructure/hardware,businessmanagementskills,subjectmatter/domainknowledge,marketing/salesskills,otherITskills

NumberofDatasetsused:20,000



Sizeofthedata:5GB,20,000observations

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During the accelerator period, Radioboticsraisedadditionalinvestment.Thistotalamountwas split between an investment deal and agrant from the European Institute ofInnovation&TechnologyHealthconsortium.


Inthesixmonthsaftertheprogramme,Radioboticshaveincreasedtheirstaffbyanadditionaltwoemployees.Theyhavealsowonasubstantialamountofinvestment,includingtheSMEinstrument(H2020)phase1grant,aswellastheaforementionedEurostarsgrant.TheirEurostarsapplicationhadscoredthehighestoutofallpreviousroundsoverthelast10years.

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4|Casestudies

Inthesixmonthsfollowingthis(betweensixandtwelvemonthsaftertheendoftheaccelerator), Radiobotics completed aninvestmentround,obtainingevenfurtherinvestmentasaresult.

4.5.6 Insights

Radiobotics’solutionisaimedatthesensitiveandhighlyregulatedhealthcaresector,whichmeansthat a workable demonstration of the technology is indispensable for gaining traction. ThetechnologydevelopedduringtheaccelerationperiodwithDataPitchservedasasteppingstoneforadditionalfundingandcontactwithinvestors.Radioboticsspecificallyhighlightedtheimportanceofdiversedatasourcesfortheirsolutiontocountertheriskofoverfitting(asolutionthatonlyworksforanarrowsetofcases).


Coresolution–High

Futureadaptationsofcoresolution-High

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4.6 Recogn.ai

Website:https://www.recogn.ai/

FoundedYear:2017

Cohort:1–(2017/2018)


Challenge:DPC5-2017:Thenextgenerationofcustomerdatamanagementsolutions

Solution Tagline: Spin-out from the Technical University of Madrid building AI software foradvancedinformationextractionandintegration

4.6.1 Solution

Recogn.ai’ssolutionusesdeeplearningtomatchrecords across different data sources and tocreate new classifications and categories. Thesolution can identify customers acrosstransactiondatasetsandotherrelatedcustomerdatasets.

Through the Data Pitch programme, Recogn.aiused customer data received from their dataprovider Uniserv, in conjunction with opensourcedatasets,totrainandtesttheirMachineLearningmodels.Attheendoftheprogramme,theirsolutioncouldclassifyrecordsasbelongingto a person or business, further allowingclassificationaccordingtobusinesstype.

Recogn.ai’s solution also included thedevelopmentofaUIandecosystem,referredtoas ‘Recogn.ai Biome’, which allowed for the


4|Casestudies

creationofmodelpipelines.Recogn.aiBiomeprovidesan interfacewhichallowsuserstotrainapredictivemodelandregisterdatasourcesusedfortrainingthroughoutthedevelopmentprocess.Additionally,thisecosystemprovidescomprehensiveversioncontrol,enablingtheusertoreverttoprevious versions, and feedback on model performance through performance metrics such aspredictiveaccuracy.

4.6.2 Data

Aspartoftheirsolution,Recogn.aiuseddataprovidedby their data provider, as well as several opendatasets. Recogn.ai received 10 GB of data fromUniservcontainingover10millionobservations,withinformation on individuals and businesses inGermany, France and the UK. Uniserv works withdatasets provided by their customers. Theircustomersincludebusinessessuchasretailers,banks,financial institutions, insurers and utility providers.Real data relating to businesses were provided toRecogn.aiwhereasasyntheticdatasetwasprovidedfor customer-related data. This customer datasetsharedidenticalstatisticalpropertiestotherealdata

set(whichcouldnotbesharedduetoGDPR).Uniservhavestatedthatthisdatahascommercialvalue,althoughaquantifiablefigurecouldnotbespecified.

The open source datasets used for additional training andtesting were sourced from: Wikidata, DBpedia, NationalLibraries Data, Open Street Maps, Open Addresses.io &Electoral rolls. The teamatRecogn.aihadpriorexperienceofworking with these and similar datasets. These open datasources totalled an additional 10 million observations andbroughtthetotalsizeofthedatausedto20GB.Recogn.aihadfullcontroloverthedatasetsusedintheconstructionoftheirsolution.


Update frequency of the data usedduring the acceleration period:Occasional/Irregular

Update frequencyof thedataused inthecommercialsolution:Live/regular


SizeofthedatareceivedfromData Provider: 10GB,10,000,000observations

Sizeofallotherdatasetsused:10GB,10,000,000observations

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Recogn.aiusedthefundingtheyreceivedfromDataPitch:

¢ topaythesalariesfourstaff;¢ to subcontract a software architectworkingwiththeOntologyEngineeringGroupinMadrid;¢ tohirealegaladvisor;and,

¢ tocovertravelexpenses.

AsRecogn.aihadonlyformedasacompanynearthestartoftheproject,thisfundingwasvitaltoallowRecogn.aitoformateamabletodeveloptheproductthroughits initialphases.This initialfundinghasenabledthecompanytosuccessfullyacceleratethroughtheproject,aswellasprogressthecompanytoitscurrentposition.

Support provided by Uniserv provided alongside their data proved to be a major benefit. Thisincludedtechnicalinformationthatfacilitatedworkingwiththedataaswellasdetailedbackgroundinformationontheirbusinessobjectivesandclientneeds.

Recogn.aiwasalsogivenguidanceonGDPRcompliance.Recogn.aihighlightedtheusefulnessoftheGDPRworkshopDataPitchhadprovidedforthem.

MostimportantresourcesDataPitchfundingenabled Recogn.ai to acquire: Softwaredevelopment skills; Data science skills; ICTinfrastructure,hardwareandskills.

Couldyouhaveobtainedthisdata(fromtheDataProvider)withoutDataPitch?No


4|Casestudies


Prior toDataPitch,Recogn.aihad receivedinvestment, but no revenues. During theacceleratorperiod,Recogn.aibothincreasedtheir revenues and headcount. Recogn.aialso closed four contracts during the DataPitchprogramme.Thesewere:

¢ A one-year pilot with Correos(SpanishPostOffice);

¢ A 3-month project with theMinistry of Culture and Sport(Spain);

¢ A3-monthpilotwithEvoBanco;¢ A six-monthprojectwith Zaragoza

CityGovernment.


In the sixmonths afterData Pitch, Recogn.ai had secured five new clientswithwhom they arerunningpilotprojects.Theseincludelegalfirmsandinsurancecompanies.Additionally,Recogn.aihadincreasedtheiremploymentby1,withtheadditionofanewleaddatascientist.

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TheyhavealsobeencontinuingtheirworktowardsattainingapartnershipwithUniserv.TheywerealsoselectedfortheAirbusBizlabAcceleratorprogramme.Theirtotalsalesalsoincreasedinthissix-monthperiod.

Within 12 months after the end of Data Pitch, Recogn.ai had totalled an additional sales,investmentsandrealisedefficiencies.

ThroughRecogn.ai’scontinuedworkontheirpartnershipwithUniserv,theyhavealsosignedacontracttodevelopaproofofconceptforoneofUniserv’smajorclients,DZBank. Recogn.ai also successfully completed the AirbusBizlabAccelerationprogrammeandarecurrentlyfocusingon reaching new clients through a new marketingstrategy,aswellascontinuingwithproductdevelopmentandtheircurrentprojects.

4.6.6 Insights

Recogn.ai was a very new company when it joined Data Pitch and another example of a veryproductive relationship between data provider and start-up. In addition to the matched dataproviderandthetraditionalincubator/acceleratorrolesofDataPitch,helpreceivedinbuildingtheteamandlegaladvicewerehighlyappreciatedbythestart-up.

For a new company, being able to work with a large company can be a rare opportunity. InRecogn.ai’scase,theirpartnershipwithUniservhasnotonlyledtofurtheropportunitiesanddealsoutside of this partnership but has also provided additionalwork from a clientwithinUniserv’snetwork.Therefore,theirparticipationinDataPitchhashadacompoundingeffectontheirbusinessdevelopment;Recogn.aihasgonefromapre-revenuestart-up,toacompanyworkingwithbanks,governmentcouncilsandlargeinternationalcompanies.Wheninnovationprogrammescanconnecttheselargerpartnerstostart-ups(whomtheselargercompaniesmaynotbeawareof),abeneficialpartnershipforbothsidescanbeachieved.



Futureadaptationsofcoresolution-High


5|Conclusions&recommendations

5 Conclusions&recommendations

5.1 Conclusions

5.1.1 Generalsuccessfactorsofaccelerators70

Ingeneral,theevidenceonthesuccessofacceleratorsismixedwithmanyacceleratorsprovidingdifferenttypesofoutcomesfortheirparticipants.However,Boneetal.(2019)show–inasurveyofpreviousparticipantsinacceleratorprogrammes–thatstart-upsperceiveddirectfundingtobethemostusefulsupportreceivedaspartoftheirincubatororacceleratorprogramme.Thiswasfollowedbyaccesstoofficespace,labspaceandtechnicalequipment.

Boneetal.(2019)reportstrongerevidencefortheimpactofparticulartypesofsupport.Whilemosttypesofsupporthaveasignificantpositiveassociationwithatleastoneofanumberofoutcomemeasures (development stage,numberofpatentapplications,R&Dexpenditureand investmentraised,etc.),thestrongestevidenceforpositiveimpactrelatesto:

¢ accesstoinvestors;¢ accesstopeers;¢ helpwithteamformation;¢ directfundingfromtheprogramme;¢ pressormediaexposure;¢ mentoringfromanindustryexpert;¢ helpwithmeasuringsocialimpact;or,¢ mentoringfromaventurecapitalist(VC)/angel.

Inaddition,somesupporttypes(e.g.accesstopeersandcoaching/personaldevelopment)appeartoactdirectlyonimprovingstart-upoutcomes.Othersseemtoworkthroughchanginghowstart-upsapproachraisingfinance,planstrategically,developandrecruitstaff,andpartnerwithexternalorganisations.DataPitchshares thekeysuccess factorswithotheracceleratorsand is thereforelikelytoproducesimilarpositivebaseline impacts, inadditiontobenefitsthatarespecifictotheuniquedatasharingaspectsofDataPitch.

5.1.2 DataPitchsuccessfactors

DataPitchhashadsubstantialpositiveimpactsonparticipants,bothstart-upsanddataproviders.DataPitchenableddata-driven innovationthatwouldnototherwisehavetakenplaceandhadapositive impact on the commercial performance of the participating start-ups as measured byincreasesinemployment,revenuesandexternalfunding.Inaddition,DataPitchsucceededinlayingthegroundworkforasustainableopeninnovationecosysteminEurope,byprovidingaplatformfordataproviderstotryoutopeninnovationinalowrisksetting(facilitatingmatchingandinteractionwithstart-upsandprovidingexpertiseandpracticalsupport).

Start-upsanddataprovidersidentifiedanumberoffactorsthatcontributedtotheimpactofDataPitch.

70SeeBoneetal.(2019).

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Outreach and recruitment of start-ups managed to reach a large, pan-European network ofbusinesses.Onthesideofdataproviders,thesituationislessclear.DataPitchmayhavebenefitedfromagreaternumberofdataprovidersofferingchallenges,andaclearercommunicationbetweenDataPitchand thedataproviders regarding requiredcapacity for theprogramme.A longerandmorethoroughpre-accelerationphase(“phase0”)maybehelpful inthefuturetoensurethatasufficientnumberofdataproviders isavailable intheproject,andtoensurethatdataprovidersunderstandtherequirementsof theprogramme.Furthermore,agreaternumberofproviders infutureprojectsmaybehelpfulinlimitingthenumberofstart-upsmatchedtoonedataproviderandensuringthatdataproviderscandedicatesufficientresourcestothesolutionsdevelopedthroughopeninnovation.

DataPitchsucceededinprovidingsupporttoaverydiverserangeoforganisations.Theprogrammeworkedparticularlywellforstart-upswithanexistingproductorservicethatcouldbedevelopedfurther. Some data providers desiredmore attention for the data provider. Data providers aretypically new to open data innovation and are therefore not always mature enough to makedatasetsavailableinwaysthatallowforimmediatework.Alongerandmorethorough“phase0”mayalsohelpinthisrespect.Beyondrecruitment,thispre-accelerationphasecanbeusedtonotonlyrecruitbutalsoonboardandpreparedataproviderstofostercooperationwithstart-ups.

Regardingdelivery,participantsnearlyunanimouslyagreedthatadministrativeburdenwasminimaland,overall,thedeliveryoftheprogrammewascomparedfavourablywithotheraccelerators.DataPitchwasespeciallycomparedfavourablytootherpubliclyfundedaccelerators.

Bothstart-upsanddataprovidersrecognisedthatDataPitchmanagedtobringtogetherstart-upswithskillanddataprovidersinterestedinopeninnovation.Assuch,DataPitchaddressedagapintheinnovationsupportlandscape.Dataproviderssawclearbenefitsfromtheprogrammeintermsoffosteringorganisationalcommitmentstoopeninnovation,whilenotingthatthesuccessoftheopeninnovationmodelrestsontheindividualsinvolved.ThematchinginherenttoDataPitchalsoallowedstart-upstoenterintonewmarkets.

The EuropeandimensionofData Pitchwas seen as an advantage, especially by start-ups basedoutsideof thetraditionalstart-uphubs.This internationaldimensionallowedstart-upstoobtaindatawhich otherwisewould have been out of reach. On the flip side, the virtual set-up of theprogrammeallowedforlessclosecontactbetweenstart-upanddataprovider.Thedataprovidersespeciallywouldhavelikedcloserinteractionwiththeirmatchesstart-ups.

Moreover,“anecdotally,incubatorsandacceleratorsoftenserveas‘focalpoints’foranecosystem,providingnotonlyadegreeofdeliberatecoordinationbutalsoageographicfocuswhichincreasesthechanceofserendipitous interactions“(Boneetal.,2019,p.60).ThesetupofDataPitchmayproduce less of these ‘serendipitous interactions’. However, the geographic spread of theparticipants(andtheconsortiumpartners)mayalsoinfactwidenthescopeforpossibleinteractionsoutsideoftraditionalstart-uphubs.Itisthereforenotclearwhetherafocus,non-virtualset-upwilldeliverbetterresults.



5.1.3 ImpactofDataPitch

Impactonstart-ups71

During the Data Pitch programme, firms increased their sales by a mean of €36,554, receivedinvestmentsofameanof€82,448,realisedefficienciesof€17,168andincreasedtheiremploymentbyanaverageof2employees.Onaverage,start-upsgenerated€599,432insalesand€338,862ininvestmentperGBofdatasharedwiththemthroughDataPitch.


Table14 ReturnonInvestmentandleveragedinvestment;realisedandprojectedfigures

Duringtheprogramme



Projectedannualfigureby2022

ReturnonInvestment 23% 91% 103% 459%

Leveragedinvestment 50% 82% 278% N/A

Note:dataon6and12monthsaftertheprogrammearebasedondatafromCohort1only.ThefiguresaccountforthisbyadjustingtheinvestmentprovidedthroughDataPitchbasedonthenumberofstart-upsforwhichdataisavailable.


Themajorityofstart-upswouldnothavebeenabletoaccessthesameorsimilardatawithoutDataPitch.,inparticularstart-upsworkinginthefinancialandmedicalsectors.Accesstodatadoesseemtoinfluencetheabilityofstart-upstoattractadditionalfunding.Start-upsthatcouldaccessdataoutsideofDataPitchattracted,onaverage,€141,000moreinadditionalfundingthanstart-upsthatwouldnotbeabletoaccessdata.Start-upstypicallyhadfullcontroloverthedatawhenbuildingthesolution.Similarly,datawastypicallystoredunderthecontrolofthestart-ups.

Themajorityofstart-upsusedmachinelearningintheirsolution.Theoutcomestrackedduringtheprogrammeprovidesomeevidencethatusingmachinelearninghelpsattractinvestment.Thereisanimpressivedifferencebetweenstart-upsthatusemachinelearningandthosethatdonot.Start-upsthatdidusemachinelearningattracted,onaverage,€108,000moreininvestmentthanthatdidnotusemachinelearning.

Regardinggrowthopportunities,start-upsinthesectorchallengeshadhighergrowthexpectationsfortheirproduct.This(perceived)abilitytoscaleasolutionhasadistinctimpactontheabilitytoattractfunding;witheasierperceivedabilitytoscalebeingassociatedwithincreasesinadditionalfundingreceived.

Successfulapplicantsreceived,onaverage,moreexternalfundingthanunsuccessfulapplicants(notincluding the €100,000 received through Data Pitch). Some applicants, both successful andunsuccessful,wereabletoattractsubstantialinvestmentsupwardsof€500,000.

71Notethatimpactsmightnothavebeenfullydisclosedbytheparticipatingstart-ups(ormaterialised)bytheendoftheprogramme.Sothattheimpactmaybeunderstatedsomewhat.

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Projectingperformanceoffundedstart-upsintothefuture,aforecastmodelpredictsthataveragerevenue for start-upswill grow from€147,723 in2019 to€833,555 in2022.Combinedwith thesuccess/failurerateofbusinesses,thisimpliesagrowthoftotalannualrevenueto€35,784,385from€6,896,000.Thisisequivalenttoagrowthof73%peryearupuntil2022.

Impactondataproviders

Theimpactondataprovidershasbeenreportedmostlyintermsofpromotingtheopeninnovationapproachwithintheproviderorganisations.Allintervieweessawthemselvesparticipatinginopeninnovation in the future, several felt they had absorbed sufficient knowledge to do so withoutexternalhelp.Severalremarkedespeciallythattheywerenowfurtheralongintheirjourneytomaketheirdatamoreaccessibleandunderstandableforthirdparties.Somedataprovidersmentionedthatmorecontactwithotherdataproviderswouldhavebeenuseful.CreatinganetworkofdataprovidersthatcontinuesaftertheaccelerationphasemightbeausefulextensionofaprogrammelikeDataPitch.

AlthoughDataProviderswentintoDataPitchwithabusinesschallenge,theysawDataPitchasalearningopportunityregardingOpenInnovationanddatasharing.Assuch,measuringquantitativeimpactswerenotprioritisedandgenerallynotavailable.However,onedataproviderestimatedthatthesolutiondevelopedwiththeirdatainDataPitchreducedthecostofaparticularbusinessprocessby35%.

5.1.4 Longertermimpacts

DataPitch is likely toproduceanumberof impacts in the longerterm.These includethe futuresuccessesoftheparticipatingstart-ups:Boneetal.(2019)statethat“acceleratorsandincubatorsaffectstart-upsinnumerousways.Outcomesofsuchinteractionsmanifestthemselvessometimesimmediately and sometimes only a considerable time after a start-up has graduated from aprogramme”.Oneeffectthatispivotalforlong-termsuccessbutlikelytooearlytoevaluateistheeffectonfirmsurvival.Ithasbeenfoundthatstart-upsthatgraduatedfromacceleratorprogrammeshaveapproximately23%highersurvivalratethanothernewbusinesses(Regmietal.,2015).

Ontopofthis,thereistheimpactondataprovidersandthewideropeninnovationecosystemthatDataPitchishelpingtoinitialise:

¢ ThemainbenefitofDataPitchfordataproviderswasthelearningexperiencefromtheprogramme.Itisthereforelikelythatmoretangibleimpactsasaresultofparticipatingindata-drivenopeninnovationwillonlyemergeovertime.

¢ Ecosystem effects in relation to accelerators, and specifically in relation to openinnovation,requireevaluationoverthelongerterm.ThiswouldincludeboththeeffectofDataPitchonthewiderstart-upsupportecosysteminEuropeandthesynergiesbetweenDataPitchandotherformsofsupportavailabletostart-ups.

5.2 Recommendations

¢ Sectorchallengesandproviderchallengesseemtohaveworkeddifferently.Forexample,start-ups in the data providers challenges and the sector challenges differed in themethodologiesused(sectorchallengestart-upsweremorelikelytousemachinelearning),thetypeofdataused(sectorchallengestart-upsusedvideodata,whereasdataproviderchallengestart-upsdidnot)andthewaydatawasstored(dataproviderchallengestart-upsmostlyfiles,whereassectorchallengestart-upsmostlyusedrelationaldatabases)as



showninsection3.6.Amoreexclusivefocusonmatchedchallengesislikelytoproducegreaterbenefitsandprovidesabettertestbedforthehypothesisthatreducingfrictionsinhibitingdatasharingfacilitatescanunlockdata-drivenopeninnovation.Theexperiencefor participants in the sector challenges more closely resembled that of a standardaccelerator,wherethestart-upisabletosourcedatafromexternalpartiesindependently.

¢ More resources to prepare and connect data providersmaymaximise the programmeimpact.Thiscouldinvolvea“Phase0”withaselectionprocessandan‘acceleration’periodfocusedondataproviderstopreparethemforworkingwithstart-upsontheirchallenges.Thelong-termbenefitsfordata-drivenopeninnovationcouldbecementedbythecreationofanetworkofdataprovidersthatpersistsaftertheendoftheprogramme.

¢ Despite the comprehensive support provided to the start-ups byData Pitch, themoremature start-ups seem to have performed better. A clearer focus on start-ups with‘acceleration-stage’maturity(provenabilitytodeliveranMVP)mayenhancetheoverallimpact.Partlythiscouldreflectthefactthatmoreexperiencedstart-upsarebetterableto select appropriate challenges both based on their technical viability, but alsostrategically, in terms of the applicability of an existing solution to a new market orindustry. In this regard,a stricter separationbetween ‘incubation-stage’ start-ups,whomaynotbeabletoproduceaworkingprototypebytheendoftheprogramme,andthemorematurestart-upsmaybecontemplated,soastoprovideeachtypeofstart-upwiththeoptimalsupportpackage,withmorestrategicadvicebeingmoreappropriateforthemorematurestart-ups.

¢ ThereissomeevidencethattheimpactofDataPitchwasstrongerinsectorswithhigherbarriers to data sharing (such as healthcare and finance, which data subjects see asparticularlysensitive).Forexample,asdiscussedinsection3.7.2,start-upsinhealthcareandfinanceweremorelikelytonotethat,withoutDataPitch,theywouldnotbeabletoaccess data. An ex-ante focus on such sectorsmay increase benefits. The selection ofsectorsshouldtakeintoaccounttheirspecificbarriersto,andenablersofdatasharing.For example, data-drivenopen innovation in the finance sector is supportedby strongregulatoryaction(OpenBanking,PSD2).

¢ Theprogrammedesignandsetupshouldfacilitaterobustevaluationoftheprogrammeitself.Thisincludeshavingaclearevaluationstrategywithwell-definedsuccessmetricsforthe programme, and comprehensive baseline data collection. Making long-term datasharingobligatory forparticipantsmaybeconsidered.Whichdata is shared long-term,which may be a couple of years, depends on the success metric targeted by theprogramme,butmayincluderevenuesandemploymentfiguresofparticipants.

¢ More broadly, investment in further pilot projects is needed to develop the openinnovationmodelandtofindoutwhatworks.Parameterssuchastheselectionofstart-upsandtypeofsupportgivenshouldbecomparativelyanalysed.Anopportunityexistswith using other European incubators, such as the European Data Incubator. OtherEuropean programmes may be used to experiment with, for instance, on-boardingprocessesinsimilar,butnotidentical,circumstances.

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References

References

Bone,J.Allen,O.&Haley,C.(2017).BusinessIncubatorsandAccelerators:TheNationalPicture.BEISResearch Paper Number 7. Retrieved from:https://www.gov.uk/government/publications/business-incubators-and-accelerators-the-national-picture

Bone, J.,Gonzalez-Uribe, J.,Haley,C.&Lahr,H. (2019).The ImpactofBusinessAcceleratorsandIncubators in the UK. BEIS Research Paper Number 2019/009. Retrieved from:https://www.gov.uk/government/publications/the-impact-of-business-accelerators-and-incubators-in-the-uk

Chesbrough,H.&Bogers,M.(2014).ExplicatingOpenInnovation:ClarifyinganEmergingParadigmforUnderstandingInnovation. InH.Chesbrough,W.Vanhaverbeke&J.West(Eds.),NewFrontiersinOpenInnovation.Oxford:OxfordUniversityPress,pp.3-28.

Churchill,N.C.& Lewis,V.L. (1983).The Five Stagesof Small BusinessGrowth.HarvardBusinessReview.Retrievedfrom:https://hbr.org/1983/05/the-five-stages-of-small-business-growth

Clarysse, B.,Wright,M. & Van Hove, J. (2015).A Look Inside Accelerators: Building Businesses.Retrievedfrom:https://www.nesta.org.uk/report/a-look-inside-accelerators/

Coadec & The Entrepreneurs Network (2019). The Start-up Manifesto. Retrieved from:https://static1.squarespace.com/static/58ed40453a04116f46e8d99b/t/5de921a956a00a7f2764220f/1575559602818/The+Start-up+Manifesto.pdf

Ctrl-Shift (2018). Data Mobility: The personal data portability growth opportunity for the UKeconomy.ReportfortheDepartmentforDigital,Culture,Media&Sport.Retrievedfrom:https://www.ctrl-shift.co.uk/reports/DCMS_Ctrl-Shift_Data_mobility_report_full.pdf

IDC (2017). Impact Assessment of ODINE Programme. Retrieved from:https://opendataincubator.eu/wp-content/uploads/2016/01/ODINE-Final-report-by-IDC.pdf

HMTreasury(2018).TheGreenBook:CentralGovernmentGuidanceonAppraisalandEvaluation.Retrieved from: https://www.gov.uk/government/publications/the-green-book-appraisal-and-evaluation-in-central-governent

Miller,P.&Bound,K.(2011).TheStart-upFactories:Theriseofacceleratorprogrammestosupportnew technology ventures. Nesta Discussion Paper. Retrieved from:https://media.nesta.org.uk/documents/the_start-up_factories_0.pdf

OECD (2005). Glossary of Statistical Terms: Innovation. Retrieved from:https://stats.oecd.org/glossary/detail.asp?ID=6865

Puri,M., Zarutskie, R. (2012).On the Life CycleDynamics of Venture-Capital- andNon-Venture-Capital-FinancedFirm.TheJournalofFinance,67(6),2247-2293;TableVI

Regmi,K.,Ahmed,S.A.&Quin,M.(2015).DataDriveAnalysisofStart-upAccelerators.UniversalJournal of Industrial and Business Management, 3(2), pp. 54-57. DOI:10.13189/ujibm.2015.030203


Indexoftables&figures


Tables

Table1 ReturnonInvestmentandleveragedinvestment;realisedandprojectedfigures 4

Table2 OutcomeofJudgeVerdicts 20

Table3 Start-upsprimarycustomerforDataPitch-enabledsolution 25

Table4 Geographicalspreadoffundedcompaniesbycohort 26

Table5 Geographyofsuccessfulandunsuccessfulapplicants 27

Country 27

Table6 Countriesofstart-upsmatchedwithdataproviders 28

Table7 Countriesofstart-upsinthesectorandopenchallengesmatchedwithdataproviders 28

Table8 Summaryofstart-upprogressduringtheDataPitchprogramme 29

Table9 Cohort1post-accelerationperformance(6monthsaftertheprogramme) 33

Table10 Cohort1postaccelerationperformance(6-12monthsaftertheprogramme) 34

Table11 ReturnonInvestmentandleveragedinvestment 35

Table12 ReturnonInvestment;projectedrevenue 66

Table13 Comparisonofacceleratorprogrammes 69

Table14 ReturnonInvestmentandleveragedinvestment;realisedandprojectedfigures 103

Table15 Call1challenges(July2017) 110

Table16 Call2challenges(July2018) 110

Table17 Baselineemploymentgrowthratesintheforecastmodel 114

Table18 Baselineemploymentgrowthratesinthecounterfactualmodel 114

Table19 Scalingfactorsfor2021and2022growthrates 115

Table20 Start-updeathratesintheforecastmodel 115

Table21 Start-updeathrateinthecounterfactualmodel 116

Table22 Baselinerevenuegrowthintheforecastmodel 117

Table23 Baselinerevenuegrowthintheforecastmodel 117

Table24 Primarydatacollectionprogramme(October–November2019) 118

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Figures

Figure1 TheOpenInnovationmodel 10

Figure2 TheDataPitchapproachtoopeninnovation 11

Figure3 Definingcharacteristicsofincubatorsandaccelerators 14

Figure4 SelectionofDataPitchparticipants 21

Figure5 MapofDataPitchCall1Applicants 22

Figure6 MapofDataPitchCall2Applicants 23

Figure7 DistributionofsalesforDataPitchparticipantsduringtheacceleratorperiod 30

Figure8 DistributionofinvestmentsforDataPitchparticipantsduringtheacceleratorperiod 31

Figure9 DistributionofrealisedefficienciesforDataPitchparticipantsduringtheacceleratorperiod 32

Figure10 DistributionofemploymentchangesforDataPitchparticipantsduringtheacceleratorperiod 33

Figure11 ROIandleveragedinvestmentduringtheprogrammeperstart-up 35

Figure12 WouldyoubeabletoaccesssimilardataoutsideDataPitch?%ofrespondents 37

Figure13 Barrierstodataaccess;%ofrespondents 37

Figure14 ReasonsforapplyingtoDataPitch;%ofrespondents(unsuccessfulapplicants) 38

Figure15 Controlovertheuseofdata;%ofrespondents 38

Figure16 Locationofdatastorage;%ofrespondents 39

Figure17 Sizeofdatasetssharedbythedataprovider 40

Figure18 Periodicityofdatasharedduringtheprogramme;%ofrespondents 40

Figure19 Mostimportantcharacteristicsofdata;%ofrespondents 41

Figure20 Numberofdatasetsusedinthesolution;%ofrespondents 41

Figure21 Sizeofdatasetsusedinthesolution 42

Figure22 Maintypeofdatausedinsolution;%ofrespondents 42

Figure23 Datastoragetype;%ofrespondents 43

Figure24 Mainobjectiveofthesolution;%ofrespondents 44

Figure25 UseofMachineLearning;%ofrespondents 44

Figure26 Howuniqueisyoursolution?%ofrespondents 45

Figure27 Howinnovativeisyoursolution?%ofrespondents 45



Figure28 Perceiveddifficultyofaccess,engineeringandbuilding;%ofrespondents 46

Figure29 Interactionbetweenstart-upanddataprovider;%ofrespondents 48

Figure30 Howdifferentisthesolutionfromtheinitialproposal?%ofrespondents 49

Figure31 Averageinvestmentperassessmentofeaseofdataengineeringandbuilding;€ 52

Figure32 Scalabilityofstart-ups’coresolution;%ofrespondents 52

Figure33 Averageinvestmentbyassessmentofopportunitiestoscale,€ 53

Figure34 FundingreceivedsinceapplyingtoDataPitch;%ofrespondents 59

Figure35 IncreaseinrevenuesinceapplyingtoDataPitch;%ofrespondents 60

Figure36 ChangeinemploymentsinceapplyingtoDataPitch;%ofrespondents 61

Figure37 Survivalrateinforecastandcounterfactualmodel 62

Figure38 Averagenumberofemployeesinforecastandcounterfactualmodel 63

Figure39 Totalemploymentinforecastandcounterfactualmodel 63

Figure40 Averageannualrevenueinforecastandcounterfactualmodel 64

Figure41 Totalannualrevenueinforecastandcounterfactualmodel 65

Figure42 Growthstagesofsmallbusinesses 65

Figure43 ProbabilityofbeingselectedfortheDataPitchProgramme 137

Figure44 Probabilityofobtaininganinterviewintheapplicationprocess 138

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Annex1|ListofDataPitchchallenges

Annex1 ListofDataPitchchallenges

Table15 Call1challenges(July2017)

ChallengeIdentifier Sector Challenge Dataprovider

DPC1-2017 RETAIL Future-proofretailsupplychains SonaeCenterServicosII,S.A.

DPC2-2017 SPORTS&RECREATION

Howcanweusedatatoimprovevisibilityandaccesstophysicalactivities?

IminLimited

DPC3-2017 DATAANALYTICS Empoweringsalesandmarketingdecisionsthroughcompanyknowledgegraphs SpazioDati

DPC4-2017 TRANSPORTChangingpublictransportforthebetter DeutscheBahnAG

DPC5-2017DATAMANAGEMENT

Thenextgenerationofcustomerdatamanagementsolutions UniServeGmbH

SC1-2017 HEALTH&WELLNESS

Howcanweusedatatohelppeopleimprovetheirhealthandwellnessand/ormakehealthservicesmoreefficientandinclusive?

N/A

SC2–2017 EMPOWERINGUSERSONLINE

HowcanweusedatatomaketheWebmoretrustworthyandimprovepersonalsafetyandsecurityonline?

N/A

SC3–2017 LIFELONGLEARNING

Howcanweusedatatoensurethatwehaveandcanfurtherdeveloptheskillsweneedinthefuture?

N/A

SC4–2017 LIVINGHowcanweusedatatoimprovelivingstandardsandlifestyle,andcreatenewaccommodationoptionsinEurope?

N/A

SC5–2017SMARTMANUFACTURING

Howcanweusedatatomakemanufacturing,logisticsandmaintenanceprocessesmoreefficientandabletosupportnewmodelsofuseandrepair?

N/A

SC6-2017 TOURISMTransformingtourism:aggregatedtravelservicesandintelligentpersonalassistants

N/A

OIC-2017 OPENINNOVATION

Harnessingthefullpowerofdata-driveninnovation

N/A

Note: ▬ = Provider challenges Source:DataPitchdeliverableD4.2.:Summaryofround1

Table16 Call2challenges(July2018)

Challengeidentifier

Sector Challenge Dataprovider

DPC1-2018 Personalisedentertainment

Developingthenextgenerationofmultidimensionalrecommendations AlticeLabsSA

DPC2-2018 Textminingandanalytics

Automatedansweringofsubjectivequestionsonenvironmentalandsocialgovernance

Bloomberg

DPC3-2018Smartmanufacturing

Developingapplicationsacrossmanufacturing,logisticsandsupplychain

GreinerInternationalPackaging

DPC4-2018 Sustainablefoodsupplychain

Creatingfarm-to-marketlinkages GROW


Annex1|ListofDataPitchchallenges

Challengeidentifier

Sector Challenge Dataprovider

DPC5-2018 Customerneedspredictions

Creatingadaptivewaystoanticipatecustomerrequirements KonicaMinolta

DPC6-2018 HealthcareCreatingoutcome-basedhealthcareofferings JosedeMelloSaude

DPC7-2018Multimodaltransport SeamlesstravelservicesacrossEurope MASAI

DPC8-2018 Weatherandclimatechange

Creatingsocialandeconomicvaluebyreducingtheimpactofclimatechange

METOffice

SC1-2018 PharmaceuticalsDevelopinginnovativeapproachesandprocessesacrossthepharmaceuticalindustry

N/A

SC2-2018 AutomotiveMaximisingthepositiveimpactofautonomousconnected,electrifiedandsharedvehicles

N/A

SC3-2018 EnergyIncreasingefficientenergycreationanduse

N/A

SC4-2018 Finance Overcomingthedatachallengesinthefinancialsector

N/A

SC5-2018 Telecoms Supporting5Greadinessanddelivertomorrow’stelecomsindustry

N/A

SC6-2018 Privacyandconsentcontrol

Creatingproductsandservicestoensureindividualprivacyandcontrol

N/A

SC7-2018 SmarttransportInnovativesolutionstoimprovemobilityandreducetrafficcongestion

N/A

OC1-2018 OpenchallengeHarnessingthefullpowerofdata-driveninnovation

N/A

Note: ▬ = Provider challenges Source:DataPitchdeliverableD4.3.:Summaryofround2

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Annex2|Impactforecastmethodology

Annex2 Impactforecastmethodology

A2.1 Employmentgrowth

A2.1.1 Modelandassumptions

Employmentgrowthwasestimatedusingasimulation-basedapproachwithrandommodelinputs.Thatis,themodeldefinedacalculationofemploymentgrowthbasedonasetofinputassumptions.The calculation drew these inputs at random from a pre-defined distribution. This exercisewasrepeatedintotal10,000times,drawingdifferentrandominputsateveryrepetition,andtheaverageoverthese10,000simulationsprovidesthefinalestimateofgrowth.

Thesamemodelwasusedtoestimateaforecastedgrowthmodelandacounterfactualmodel.ThecounterfactualmodelprovidesanestimateofemploymentgrowthhadDataPitchnotexisted.Theforecast and counterfactual models differed only in the input assumptions used. The corefunctionalityofthemodelwasthesame.

Themodelcombineddatafrombothcohortsintoonesinglecohortandstartedtheprojectionatthestartofthishypothetical,combinedcohort.Forthesakeofreporting,thestartofthecombinedcohortisequatedwiththestartofcohort2.Therefore,baselinefiguresrefertotheyear2019,andthesubsequentyearsrefertorespectively2020,2021and2022.

A2.1.2 Baselineassumptions

The model firstly required a baseline from which to evaluate growth in employment. Theassumptionsforthisbaselinewerethesamefortheforecastandthecounterfactualmodel,andwere:

¢ thereare47SMEsatthestartoftheDataPitchprogramme;¢ theyhaveonaverage772employeesatthestart;and,¢ theyhave310employeesintotalatthestart.

The average and total number of employees for the 47 SMEs was derived from the statedemploymentofsuccessfulapplicantsduringtheapplicationstage.

A2.1.3 Corefunctionalityofthemodel

Themodelcalculatedthefollowingthreeoutcomesfortheyears2020,2021and2022:

¢ averageemploymentperstart-up;¢ numberofstart-upsstillinbusiness;and,¢ totalemploymentforallstart-upsstillinbusiness.

Thiswascalculatedasfollows:

72Notethattheunroundedaverageis6.58,whichexplainswhytotalemploymentdoesnotequal47 ∗ 7.



For2020(thefirstyearaftertheprogramme):

¢ average employment was calculated as 7 ∗ (1 + 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑔𝑟𝑜𝑤𝑡ℎ𝑟𝑎𝑡𝑒)wherethebaselinegrowthratewasarandomlydrawninput(seebelow);

¢ thenumberofstart-upsinexistenceascalculatedas47 ∗ (1 − 𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑑𝑒𝑎𝑡ℎ𝑟𝑎𝑡𝑒)wherethestart-updeathratewasalsorandomlydrawn;and,

¢ totalemploymentwascalculatedas𝑎𝑣𝑒𝑟𝑎𝑔𝑒𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡=>=> ∗ 𝑒𝑥𝑖𝑠𝑡𝑖𝑛𝑔𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑠=>=>.

For2021:

¢ average employment was calculated as 𝑎𝑣𝑒𝑟𝑎𝑔𝑒𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡=>=> ∗ (1 +2021𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑔𝑟𝑜𝑤𝑡ℎ𝑟𝑎𝑡𝑒) where the 2021 growth rate was a function of thebaselinegrowthandascalingfactor(seebelow);

¢ the number of start-ups was calculated as 𝑒𝑥𝑖𝑠𝑡𝑖𝑛𝑔𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑠=>=> ∗ (1 −𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑑𝑒𝑎𝑡ℎ𝑟𝑎𝑡𝑒);and,

¢ totalemploymentwascalculatedas𝑎𝑣𝑒𝑟𝑎𝑔𝑒𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡=>=B ∗ 𝑒𝑥𝑖𝑠𝑡𝑖𝑛𝑔𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑠=>=B.

Finally,for2022:

¢ average employment was calculated as 𝑎𝑣𝑒𝑟𝑎𝑔𝑒𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡=>=B ∗ (1 +2022𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑔𝑟𝑜𝑤𝑡ℎ𝑟𝑎𝑡𝑒)wherethegrowthratewasdefinedalongsidethe2021growthrate;

¢ the number of start-ups was calculated as 𝑒𝑥𝑖𝑠𝑡𝑖𝑛𝑔𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑠=>=B ∗ (1 −𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑑𝑒𝑎𝑡ℎ𝑟𝑎𝑡𝑒);and,

¢ totalemploymentiscalculatedas𝑎𝑣𝑒𝑟𝑎𝑔𝑒𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡=>== ∗ 𝑒𝑥𝑖𝑠𝑡𝑖𝑛𝑔𝑠𝑡𝑎𝑟𝑡𝑢𝑝𝑠=>==.

Note that number of employee andbusinesses cannot be fractional. Bothwere rounded to thenearestintegerduringthecalculations.

A2.1.4 Baselineemploymentgrowthrate

Theinputsusedforthebaselineemploymentgrowthrateintheforecastmodelwerederivedfromthebi-weeklymonitoringupdatesand6monthprogressupdate.Wherebothwereavailable,thedatafromtheupdatesweresummedtoderive12-monthprogresssincethestartoftheprogramme.Wherethe6monthsupdatewasnotavailable(cohort2),thebi-weeklyupdatesweremultipliedby2toderiveanannualisedfigure.

Thereportedchangesinemploymentweremappedtosixcategories73.Thesecategoriesthemselvesweremapped into implied baseline growth rates. The probability distributionwithwhich thesebaselinegrowthswereappliedinthesimulations,wasderivedfromtheproportionofstart-upsineachofthesixcategories.Theassumptiononbaselineemploymentgrowthintheforecastmodelwasthereforeasfollows:

73Changeinemploymentwascategorisedintoadiscretenumberofcategoriestoensurethatthesamemodelcanbeusedforboththeforecastandcounterfactualscenario.

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Table17 Baselineemploymentgrowthratesintheforecastmodel

Category Impliedbaselinegrowth[a] ProbabilityMorethan15employeesmoreinfirstyear 214% 2%Between11and15employeesmoreinfirstyear 186% 2%Between6and10employeesmoreinfirstyear 114% 32%Between1and5employeesmoreinfirstyear 43% 40%Nochangeinemploymentinfirstyear 0% 19%Between1and5employeesfewerinfirstyear -43% 4%

[a]Impliedgrowthrateshavebeencalculatedasfollows:

Morethan15: 15 7 ∗ 100% ≈ 214%

Between11and15:(13 7) ∗ 100% ≈ 186%

Between6and10: 8 7 ∗ 100% ≈ 114%

Between1and5: 3 7 ∗ 100% ≈ 43%

Nochange:0%

Between1and5decrease: −3 7 ∗ 100% ≈ −43%

Exceptfor“Morethan15”and“Nochange”,thegrowthratesarebasedonthemid-pointofeachcategory.Notethattheaverageemploymentwas7atthestartoftheprogramme.

Source:Bi-weeklymonitoringupdatesand6monthsprogressupdate

Fortheacounterfactualscenario,theinputsinthemodelwerebasedonthe‘unsuccessfulapplicantssurvey’.Thissurveygaugedchangeinemploymentin5categorieswhichweresimilarlymappedtoimpliedbaselinegrowthrates.Theprobabilitydistributionwithwhichthesebaselinegrowthswereapplied in the simulations, was derived from the proportion of start-ups in each of the fivecategories.Theassumptionforthecounterfactualmodelwasthereforeasfollows:

Table18 Baselineemploymentgrowthratesinthecounterfactualmodel

Category Impliedbaselinegrowth[a] ProbabilityMorethan10employeesmoreinfirstyear 143% 22%Between6and10employeesmoreinfirstyear 114% 0%Between1and5employeesmoreinfirstyear 43% 56%Nochangeinemploymentinfirstyear 0% 11%Between1and5employeesfewerinfirstyear -43% 11%


Morethan10: 10 7 ∗ 100% ≈ 143%

Between6and10: 8 7 ∗ 100% ≈ 114%

Between1and5: 3 7 ∗ 100% ≈ 43%

Nochange:0%

Between1and5decrease: −3 7 ∗ 100% ≈ −43%

Exceptfor“Morethan15”and“Nochange”,thegrowthratesarebasedonthemid-pointofeachcategory.Notethattheaverageemploymentwas7atthestartoftheprogramme.


A2.1.5 Growthratesfor2021and2022

Theemploymentgrowthratesin2021and2022weredefinedasafunctionofthebaselinegrowthrateand theassessmentof scalabilityof solutions reported in the ‘successfulapplicants survey’.Moreprecisely, successfulapplicantswereaskedtorate theeasewithwhichtheircoresolutioncouldbescaledinthethreeyearsfollowingtheprogrammefrom1(limitedgrowth)to5(significantgrowth).Itistobeexpectedthatstart-upsthatforeseesignificantgrowthwillgrowfasterthanstart-upsthatonlyforeseelimitedgrowth.



Theemploymentgrowthratesin2021and2022werethusdefinedas:

¢ 2021:𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒𝑔𝑟𝑜𝑤𝑡ℎ𝑟𝑎𝑡𝑒 ∗ 𝑠𝑐𝑎𝑙𝑖𝑛𝑔𝑓𝑎𝑐𝑡𝑜𝑟=>=B¢ 2022:𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒𝑔𝑟𝑜𝑤𝑡ℎ𝑟𝑎𝑡𝑒 ∗ 𝑠𝑐𝑎𝑙𝑖𝑛𝑔𝑓𝑎𝑐𝑡𝑜𝑟=>==

Thescalingfactorsfor2021and2022weredefinedas:

¢ 𝑠𝑐𝑎𝑙𝑖𝑛𝑔𝑓𝑎𝑐𝑡𝑜𝑟=>=B =B

=∗(LMNOPQPRSQSTU)

¢ 𝑠𝑐𝑎𝑙𝑖𝑛𝑔𝑓𝑎𝑐𝑡𝑜𝑟=>== =B

V∗(LMNOPQPRSQSTU)

where𝑠𝑐𝑎𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦isanumberbetween1and5,where1indicateslimitedgrowthpotentialand5indicates significant growth potential. The probability distribution over 𝑠𝑐𝑎𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 was derivedfromresponsesinthesuccessfulapplicantssurvey.Thesameassumptionwasusedintheforecastandcounterfactualmodel.

Theassumptiononthescalingfactors(andbyextensionthe2021and2022growthrates)werethus:

Table19 Scalingfactorsfor2021and2022growthrates

Scalability Scalingfactor:Year2 Scalingfactor:Year3 Probability1(limitedgrowth) 0.10 0.07 5%2 0.13 0.08 5%3 0.17 0.11 22%4 0.25 0.17 32%5(significantgrowth) 0.5 0.33 37%


Notethatthescaling factorsalwaysdecreasedthe(absolutevalueof)baselinegrowth.Thiswasimplementedbecausethebaselinegrowthratescouldbesubstantialandinsomecaseswereevenabove100%.Suchhighgrowthsratesarenotsustainableineventherelativelyshortrunofthreeyears.Assuch,growthrateswerediminishedovertimetogenerateamorerealisticassumption.Itshouldbenotedthatinrealityemploymentgrowthratesmayshowmoreerraticpatternsdependingonspecificcompanyperformance.

Furthermore, thedecline ingrowthwas largerbetween2020and2021 thanbetween2021and2022.Thisaccounted for the fact that lowergrowthrateshave lessscope for furtherdecline. Ineffect,ifemploymentgrowthis50%,itcanreasonablybeexpectedtodecreasemorethanifthegrowthrateis25%.Byconstruction,employmentgrowthrateswerelowerin2021thanin2020.

A2.1.6 Start-updeathrate

Thelastinputintothemodelwasthestart-updeathrate.Deathrateswerecollectedfromavarietyofsources.Eachdeathratewasusedwithequalprobabilityinthesimulationanddifferedbetweentheforecastandcounterfactualmodel.

Fortheforecastmodel,thefollowingassumptionwasused:

Table20 Start-updeathratesintheforecastmodel

Deathrate ProbabilityDeathrateofallcompanies;Eurostat 8% 25%

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Deathrate ProbabilityDeathrateofcompaniesbetween5-9employees;Eurostat[a] 2.48% 25%Deathrateofcompanieswith10ormoreemployees;Eurostat[b] 1.29% 25%Currentstateamongsuccessfulapplicants 0% 25%

[a]Theaveragenumberofemployeesatthestartoftheprogrammewas7

[b]Inmostcases,thesizeofSMEwillexceedby2020.

Source:Eurostat,2017(bd_9bd_sz_cl_r2)

Theaveragedeathrateusedinthemodelwas2.9%.ThiscanbecomparedwiththedeathrateofVC-fundedcompaniesasreportedbyPuri&Zarutskie(2012).Theyreportafailurerateof4.9%oneyearafteraVC-fundedfirmismatchedwithaventurecapitalist.Theassumeddeathrateforthesimulationmaythereforebesomewhatonthelowside.However,itshouldbenotedthattheprofileofVC-fundedfirmsdoesnotnecessarilyreflecttheprofileofSMEsfundedthroughDataPitch.

Forthecounterfactualmodel,thefollowingassumptionwasused:

Table21 Start-updeathrateinthecounterfactualmodel

Deathrate ProbabilityDeathrateasestablishedbyaninternetsweepofunsuccessfulapplicants 13% 25%

Deathrateasreportedintheunsuccessfulapplicantssurvey 11% 25%Deathrateofallcompanies;Eurostat 8% 25%Deathrateofcompaniesbetween5-9employees;Eurostat[a] 2.48% 25%

[a]Theaveragenumberofemployeesatthestartoftheprogrammewas7

Source:Eurostat,2017(bd_9bd_sz_cl_r2);Unsuccessfulapplicantssurvey

Theaveragedeathrateusedwas8.62%.ComparedwithPuri&Zarutski(2012),thisisagainonthelowerside.Theauthorsreportafailurerateof9.5%inthefirstyearaftermatchingwithaventurecapitalist.

A2.2 Revenuegrowth

A2.2.1 Changesinassumptionsrelativetoemploymentgrowthmodel

Most of the assumptions used in the employment growth simulation were also applied to therevenue growth model. Furthermore, the core functionality was not changed. The followingassumptionwerechanged.

In thebaseline assumption, the average revenue at the start of theData Pitchprogrammewasassumedtobe€146,723.40,whichwastheaveragerevenuereportedbysuccessfulapplicantsattheapplicationstage.

Theassumptionforbaselinerevenuegrowthintheforecastmodelwasagainbasedondatafrommonitoring updates and the 6 months progress updates. Where available, revenues (includingefficiencygains;seenotestoFigure35)weresummedacrossbothupdates.Forcohort2,revenuesweremultipliedby2toderiveannualisedfigures.Revenueswerecategorisedintofivegroups.Thesegroupsweremappedintoimpliedbaselinegrowthrates.Theassumptionforbaselinegrowthintheforecastmodelwasasfollows:



Table22 Baselinerevenuegrowthintheforecastmodel

Category Impliedbaselinegrowth[a] ProbabilityRevenueincreasebymorethan€1million 682% 4%Revenueincreasebybetween€500,001and€1million 511% 2%Revenueincreasebybetween€100,001and€500,000 170% 30%Revenueincreasebybetween€1and€100,000 34% 28%Nochange 0% 36%


Morethan€1million: 1,000,000 146,723.40 ∗ 100% ≈ 682%

Between€500,001and€1million:(750,000 146,723.40) ∗ 100% ≈ 511%

Between€100,001and€500,000: 250,000 146,723.40 ∗ 100% ≈ 170%

Between€1and€100,000: 50,000 146,723.40 ∗ 100% ≈ 34%

Nochange:0%

Exceptfor“Morethan€1million”and“Nochange”,thegrowthratesarebasedonthemid-pointofeachcategory


Forthecounterfactualmodel,theinputswerederivedfromtheunsuccessfulapplicantssurveyusingthe samecategories as the forecastmodel. Theassumption in the counterfactualmodelwasasfollows:

Table23 Baselinerevenuegrowthintheforecastmodel

Category Impliedbaselinegrowth[a] ProbabilityRevenueincreasebymorethan€1million 682% 11%Revenueincreasebybetween€500,001and€1million 511% 11%Revenueincreasebybetween€100,001and€500,000 170% 22%Revenueincreasebybetween€1and€100,000 34% 22%Nochange 0% 33%


Morethan€1million: 1,000,000 146,723.40 ∗ 100% ≈ 682%

Between€500,001and€1million:(750,000 146,723.40) ∗ 100% ≈ 511%

Between€100,001and€500,000: 250,000 146,723.40 ∗ 100% ≈ 170%

Between€1and€100,000: 50,000 146,723.40 ∗ 100% ≈ 34%

Nochange:0%

Exceptfor“Morethan€1million”and“Nochange”,thegrowthratesarebasedonthemid-pointofeachcategory


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Annex3|Datacollection

Annex3 Datacollection

A3.1 Summary

LEconductedinterviewswithDataPitchparticipants(dataprovidersandstart-ups)andranonlinesurveys of participating start-ups and unsuccessful applicants. A total of 23 interviews wereconducted.Theonlinesurveyselicitedacombined50complete responses.FormalconsultationswerealsoheldwithDataPitchstafffromtheODIandtheUniversityofSouthampton.

Table24 Primarydatacollectionprogramme(October–November2019)

Interviews(face-to-face,phone) OnlinesurveyresponsesStart-ups:17Cohort1:5Cohort2:12

DataPitchparticipants:41

Dataproviders:6Cohort1:1Cohort2:5

Unsuccessfulapplicants:9

The following sections reproduce the interview guides and survey instruments used in the datacollectionprogramme.



A3.2 DataPitchevaluation–Start-uptopicguide

Background

¢ BackgroundtotheSME/solution(didtheideaprecedeparticipation?)¢ HowdidtheybecomeawareofDataPitch¢ MotivationforparticipatinginDataPitch(andprovidervs.sectorchallenge)¢ PotentialalternativestoDataPitch(otherforms/sourcesoffunding)

Detailsofthedataprovided

¢ Data type, characteristics (volume, periodicity, granularity, source(s), personal vs non-personal,etc.)

¢ BenefitsofaccessingthisdatathroughDataPitch£ Didn’tknowthisdataexisted£ Couldn’tlocateaproviderofthisdata£ Tooexpensive£ Technicalbarriers£ Legal/regulatorybarriers

Detailsofthesolution

¢ Description¢ Marketpositioning,userbase¢ Useoftechnology(AI)¢ Importanceofdatainthesolution

£ Quantity£ Diversity£ Innovation

¢ Roleofopendata/openinnovationinthesolution

Useofthefundingreceived

¢ Whatarethefundsusedfor(skills,hardware,etc.)

Othersupportreceivedfromthedataprovider(s)&DataPitch

¢ AnyotherresourcesprovidedtotheSMEaspartofDataPitchbythedataprovider¢ Anyother resourcesprovided to theSMEaspartofDataPitchbyDataPitch (e.g. legal

documentation)

Benefits

¢ DirectbenefitstotheSME¢ Benefitstootherparties¢ Plansforfutureuseofthesolution

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¢ Futureparticipationinopeninnovation

Reflectionsontheprogramme

¢ Applicationprocess¢ Selection/matchingprocess¢ Monitoringarrangements¢ Outcomesvsexpectations,¢ Lessonslearned¢ Comments&suggestions

Other

¢ Didyoureceive/fillintheonlinesurvey?Anyissueswiththeonlinesurvey?



A3.3 DataPitchevaluation–Dataprovidertopicguide

Background

¢ Backgroundtothechallenge/innovationsought¢ MotivationforparticipatinginDataPitch¢ PotentialalternativestoDataPitch(in-housedevelopment,ordinaryprocurement)

Detailsofthedataprovided

¢ Data type, characteristics (volume, periodicity, granularity, source(s), personal vs non-personal,etc.)

¢ Valueofdata(anyinternalvaluationofthedataprovided)

Othersupportprovided

¢ AnyotherresourcesprovidedtotheSMEaspartofDataPitch

Benefitsforthedataprovider

¢ Directbenefitstotheprovider¢ Benefitstootherparties(SMEs,thirdparties)¢ Plansforfutureuseofthesolution¢ Futureparticipationinopeninnovation

Reflectionsontheprogramme

¢ InteractionswiththeDataPitchconsortium¢ Selection/matchingprocess¢ Outcomesvsexpectations,¢ PerformanceoftheSME¢ Lessonslearned

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A3.4 DataPitchevaluation–successfulparticipantssurvey

London Economics [https://londoneconomics.co.uk] has been commissioned by the Data Pitchconsortium to carry out an independent assessment of the Data Pitch programme. This surveycollectsinformationonyourinteractionwithDataPitchandthecharacteristicsandfeaturesofyoursolution. No personal information is being collected in this survey. The survey will takeapproximately15minutestocomplete.

If you have any questions, please contact Moritz Godel, T +44 (0)20 3701 7708,[email protected]

Q1.Nameofyourbusiness:*

Q2.HowcloselydidyouinteractwithDataProvidersotherthanyourpartneredDataProvider?(1signifies'Nointeraction'and5signifies'Verycloseinteraction')

Q3.Howmanydatasetsdoyouuseinyoursolution?Pleaseprovideatotalnumberofopen,closedand self-generated datasets. (Dataset refers to sets of data that share the samefeatures/characteristicsandwhichyourbusinesseitherreceivesfromadataproviderorcollectsitself).*

Q4. WithoutDataPitch,wouldyouhavebeenabletoaccessthesamedata(orequivalentdatathatwouldenableyoutoimplementthesamesolution)?*

Yes

No

Q5.Whatwouldpreventyoufromaccessingthesamedata(orequivalentdatathatwouldenableyoutoimplementthesamesolution)?*

Ididn’tknowthisdataexisted

Icouldn’tlocateaproviderofthisdata

Tooexpensive



Technicalbarriers

Legal/regulatorybarriers

Other(pleasespecify):

Q6.WhichcharacteristicsofthedatausedinDataPitcharethemostimportantforyoursolution?*

Volumeofdata(enablingmoreprecisepredictions,greatercoverage,etc.)

Richness/granularity of data (enabling higher quality solution, more relevantrecommendations,etc.)

Complementaritywithotherdatasets(‘missingpieceofthepuzzle’)


Q7. Howlargeisthedatasetthatyouobtainedfromyourmain(partnered)dataproviderforuseinDataPitch?*

Number ofentries/observations/records

*

SizeinGigabytes *

Q8. What is(are) theprimaryunit(s) of observation (e.g. customers, card transactions, patentfilings,imagesetc.)?*

Q9.Howlargeisthedatasetthatyoursolutionusesintotal?(I.e.anydataset(s)providedforDataPitch+anydataset(s)youcollectedyourselforobtainedfromotherdataprovidersoutsideDataPitch)*

Number ofentries/observations/records

*

SizeinGigabytes *

Q10.Whatis(are)theprimaryunit(s)ofobservation(e.g.customers,cardtransactions,patentfilings,imagesetc.)?*

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Q11.Pleaseindicatethemaintypeofdatausedinyoursolution,selectallthatapply.*

Numeric

Text

Video

Audio

Geospatial

Image/graphics


Q12.Howisthisdatastored?*

Semanticdatabases(i.e.RDFtriples)

Documentorienteddatabases

Relationaldatabases

Files


Q13.Whereisthedatastored?*

DataProvider'sinfrastructure

Commercialcloudpaidforbythedataprovider

Yourowninfrastructure

CommercialCloudpaidforbyyourbusiness


Q14.Howmuchcontroldoyouhaveoverthedatawhenbuildingyoursolution?*

Fullcontrol(e.g.fullcopyofdatafreelyavailabletome)



Partialcontrol(e.g.datacalledthroughAPIwhenrequired)

Nocontrol(e.g.IsendmyalgorithmstotheDataproviderandgettheresults)


Q15.Whatistheupdatefrequency(periodicity)ofthedatausedinyoursolution?*

Staticdata(notupdated)

Occasionaldata(irregularlyupdated)

Live data(regularlyupdated)

Real time data(regularlyupdated withhighfrequency)

Notapplicable

Duringtheaccelerationperiod Mature commercialsolution

Q16.Whatresources/capabilitiesdidDataPitchfundingenableyoutoacquire(rankinorderofimportancewith1beingmostimportant;answerN/AifDataPitchfundingwasnotusedforaparticularcategory)?*

Ranking

Subject matter/domainknowledge

Businessmanagementskills

Softwaredevelopmentskills

Data science/machine learningskills

OtherITskills

ICTinfrastructure/hardware

Marketing/salesskills

Pleasespecifyanyotherimportantresources/capabilitiesandtheirrelativeranks

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Q17.HowcloselydidyouinteractwithyourpartneredDataProvider?(1signifies'Nointeraction'and5signifies'Verycloseinteraction')

Q18.Foreachofthebelow,pleaseratethedifficultyyouhadduringtheaccelerationperiod:(1signifyingeasiest,5signifyinghardest)

DataAccess

DataEngineering

Buildingthesolution

Q19. Whatistheprimarytechnicalobjectiveofyoursolution?*

Combineandcorrelatedifferentdatasets

Identifypatterns

Makepredictions

Enhancedataquality

Filterinformation

Visualiseinformation

Createauserinterfacefordataaccess

Q20.DoesyoursolutionuseMachineLearning?

Yes

No

Q21.Whichmethodsareusedinyoursolution?Pleaseselectallthatapply.*

Regressionalgorithms(e.g.linearregression,logisticregression)

Instance-basedalgorithms(e.g.k-NN,SVM)

Decisiontreealgorithms(e.g.CART)

Bayesianalgorithms(e.g.naïveBayes,BayesianNetwork)



Clusteringalgorithms(e.g.k-means,hierarchicalclustering)

Associationrulelearningalgorithms(Apriori,ECLAT)

Artificialneuralnetworkalgorithms(e.g.MLP)

Deeplearningalgorithms(e.g.CNN,RNN,DBN)

Reinforcementlearningalgorithms(e.g.Q-Learning)

Ensemblealgorithms(e.g.RandomForest,GBM)


Q22.Howwouldyoucategoriseyoursolution?DescriptiveAnalytics,whichusedataaggregationanddataminingtoprovide insight intothepastandfuture:"Whathashappened?"PredictiveAnalytics,whichuse statisticalmodels and forecasts techniques tounderstand the futureandanswer: "What could happen?" Prescriptive Analytics, which use optimisation and simulationalgorithmstoadviseonpossibleoutcomesandanswer:"Whatshouldwedo?"*

Descriptive

Predictive

Prescriptive


Q23.HowdifferentisyoursolutionnowfromtheideayouhadatthestartofyourinvolvementinData Pitch? (An answerof 1 signifies your solutionmatches the initial proposal exactly; ananswerof10signifiesthatthesolutioniscompletelydifferentfromtheproposal).

Q24. WhyisyoursolutiondifferentfromtheideayouhadatthestartofyourinvolvementinDataPitch?*

Technical reasons (original solutionwas too difficult to implement during the accelerationperiod)

Businessreasons(changestothesolutionresultinginabetter/moremarketableproduct)

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Wouldprefernottosay


Q25.Whodoyouenvisageastheprimarycustomersforyoursolution?*

Q26.Whatdistributionmodeldoyouenvisageforyoursolution?*

SaaS(SoftwareasaService)

On-premisedeployment

Ad-hocmodel(e.g.consultancywithengagementonacase-by-casebasis)

Wouldprefernottosay


Q27. AreyouconsideringreleasingyoursolutionasOpenSource?*

Yes,fully

Yes,partsofit

No

Wouldprefernottosay

Q28.Howdoyouseethescalabilityofyoursolutionoverthenextthreeyears?(Ananswerof1signifies'limitedgrowth',ananswerof5signifies'significantgrowth').

Coresolution(markets&customersascurrentlyidentified)

Futureadaptationsofthecoresolution(newmarkets/applicationareas/customergroups)

Q29.Howmucheffortdoyouforeseethisscalabilityrequiring?



Minimum effort (e.g.only additional hostingspace andcomputationalpower)

Medium effort (e.g.minimal softwarechanges)

High effort (e.g. majorsoftwarechanges)

Coresolution Future adaptations ofthecoresolution

Q30.Onascaleto1-10,howuniqueistheproductthatyoursolutionprovidestoyourcustomers?Aretheresimilartypesofproductsoutthere,oristhisaone-of-a-kind?(Ananswerof1signifiestheproductis'notatallunique'andananswerof10signifiestheproductis'completelyunique').

Q31. Onascaleof1-5,howinnovativeisyoursolution?(Ananswerof1signifieslowinnovation,ananswerof5signifieshighinnovation).

Q30.Anyothercommentsorremarks?

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A3.5 DataPitchevaluation–unsuccessfulapplicantssurvey

Inthesummerof2017and/or2018,youappliedtotheDataPitchprogramme.LondonEconomicshasbeencommissionedbytheDataPitchConsortiumtocarryoutanindependentassessmentofthisprogramme.

Thissurveywillcollect informationonyour interactionwiththeDataPitchprogrammeandyourcompany’sperformancesinceapplyingtotheprogramme.Thesurveywilltakeapproximately15minutestocomplete.

If you have any questions, please contact Moritz Godel, T +44 (0)20 3701 7708,[email protected].

Q1.Whatisthenameofyourcompany?*

Q2.WhendidyourcompanyapplytoDataPitch?*

Summer2017

Summer2018

Both

Q3. IsthecompanywithwhichyouappliedtoDataPitchstillactive?*

Yes

No

Prefernottosay

Q4.Approximately,howmanypeoplecurrentlyworkatyourcompany?*

1

2-5

6-10

11-50

51-250

>250



Q5.Approximately,whatisyourcurrentannualisedrevenue?*

€0(pre-revenue)

€1-€100,000

€100,001-€500,000

€500,001-€1million

€1million-€2million





>€50million

[Only forthosewhomentionedapplyingtoDataPitchtwice]YoumentionedthatyouappliedtoDataPitchtwice.In answering the following questions, please think of the last time you applied to Data Pitch.

Q6.WhydidyouapplytoDataPitch?Selectallthatapply.*

Funding

Accesstodata


Q7. Howeasywasittocompleteandsubmityourapplication?*

1 2 3 4 5

Veryeasy Verydifficult

Q8. Howtransparentwasthedecision-makingprocessaroundyourapplication?*

1 2 3 4 5 Verytransparent

Veryopaque

Q9. DoyouhaveanyothercommentsontheapplicationprocessforDataPitch?

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Q10. Byhowmuchhasthenumberofemployeesinyourcompanychangedinthefirst12monthsafterapplyingtoDataPitch?*

Increasedbymorethan10employees

Increasedby6to10employees

Increasedby1to5employees

Nochange

Decreasedby1to5employees

Decreasedby6to10employees

Decreasedbymorethan10employees

Q11. Byhowmuchdidyourannualisedrevenuechangeinthefirst12monthsafterapplyingtoDataPitchcomparedtobeforeapplying?*

Increasedbymorethan€1million

Increasedbybetween€500,001and€1million

Increasedbybetween€100,001and€500,000

Increasedbybetween€1and€100,000

Nochange

Decreasedbybetween€1and€100,000

Decreasedbybetween€100,001and€500,000

Decreasedbybetween€500,001and€1million

Decreasedbymorethan€1million

Q12.Generally,howchallengingisaccesstoexternaldatasourcesforyourcompany?*

1 2 3 4 5

Veryeasy Verychallenging

Q13. Howmuchexternalfundingdidyoureceive inthefirst12monthsafterapplyingtoDataPitch?*



WereceivednoexternalfundingafterapplyingtoDataPitch

Lessthan€10,000

Between€10,000and€50,000

Between€50,000and€100,000

Between€100,000and€250,000

Between€250,000and€500,000

Morethan€500,000

Q14. Whattypeoffundingdidyoureceive?Selectallthatapply.*

Seedinvestment

Angelinvestment

SeriesAroundinvestment

SeriesBroundinvestment

SeriesCroundinvestment

Mezzanineinvestment


Q15. Wheredidthefundingoriginatefrom?Selectallthatapply.*

Fromwithinthecountrywearebasedin

FromwithintheEU,butnotthecountrywearebasedin(includingEUinstitutions)

FromoutsideoftheEU

Q16. Doyouhaveanyothercommentsorremarks?

Q17. Canwecontactyouwithfurtherquestions?*

Yes

No

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Ifyouagreethatwecancontactyou,wewillneedtocollectsomepersonalinformation.Ourfullprivacypolicyisavailablehere.

Q18.Pleaseprovideyournameandcontactdetails.

Name:

E-mailaddress:

Telephonenumber:

Any othercontactdetails:


Annex4|Noteonquantitativeevaluation

Annex4 Noteonquantitativeevaluation

As part of the impact assessment, London Economics investigated the possibility of providingquantitative estimatesof theeffect ofDataPitchon theparticipating companies and thewidereconomy.Followingour initialproposal,weexploredtheuseofaFuzzyRegressionDiscontinuityDesign(FRDD)toprovideimpactestimates74.

A4.1 TheFuzzyRegressionDiscontinuityDesign

TheimpactoftheDataPitchprogrammecanbeassessedbycomparingtheoutcomesforcompaniesthattookpartintheprogramme(successfulapplicants)withthoseofsimilarcompaniesthatdidnottakepart.Inanidealworld,onewouldtakeacollectionofcompanies,randomlydividethemintotwogroupsandadministertheprogrammetooneofthegroupsandnottheother.Becauseoftherandomisation,thetwogroupswouldbecomparableandthecomparisonbetweenthetwogroupsprovidesanestimateoftheprogramme’simpact.

However,inDataPitchcompaniesarenotrandomlyassignedtotakepart.Thereisanapplicationphase that (non-randomly) selects successful applicants. However, the selection of successfulapplicantswasbasedonanobservablemeasure;allapplicationswerescoredandselectedforaninterviewifthescoreexceededadefinedcut-offvalue75.Ifthisscorewastheonlydeterminantofbeingselected,andifthecut-offhadbeenappliedrigidly,thena(sharp)RegressionDiscontinuityDesign(RDD)couldhavebeenapplied.

RDDworksontheassumptionthatapplicationswithsimilarscoresshouldbeofasimilarquality,andthereforeshouldbeequallylikelytosucceedunderthesamecircumstances.Ifthisistrue,thenwe can compare successful applicants that scored just above the cut-off with unsuccessfulapplicantsscoringjustbelowthecut-off.Thesetwogroupsofcompaniesshouldbecomparableinquality,butonegrouptookpartintheprogrammeandtheotherdidnot.

Ineffect,RDDcreatesquasi-experimentalconditionswhereoneiscomparingtwogroupsthatdifferonly in the fact thatonegroup is “treated” and theother is not. Thedifferencebetween thesegroups,asinanexperiment,providesanestimateoftheeffectoftheprogramme.

InDataPitch,thescorewasnottheonlydeterminantofwhetheranapplicantwouldbesuccessful.Theapplicationphasewas followedbyan interviewphase that further screenedoutapplicants.Furthermore, the cut-off was not rigidly applied, to ensure the best use of the programme’savailable budget. A sharpRegressionDiscontinuityDesign is therefore not possible, but a fuzzyRegressionDiscontinuityDesign(FRDD)maystillbepossible.

TheFRDDdoesnotdependonarigidlyappliedscoreandcanbeusedaslongastheprobabilityofbeing selected for theprogrammesignificantly increasesat the cut-offpoint. If that is the case,econometric techniquesexist thatcanaccount forapplications thatpassed thecut-offbutwereunsuccessfulintheend.

74FRDDisastate-of-the-artmethodthathasbeenusedinsimilarsettings,notablybyBoneetal.(2019).75Cohort1applicationswerescoredoutof100withthecut-offat60.Cohort2applicantswerescoredoutof5withthecut-offat3.

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A4.2 CanFRDDbeappliedtoDataPitch?

A4.2.1 Evidenceofanexploitablediscontinuityinapplicationscores

TodeterminewhetheritispossibletoimplementFRDDfortheDataPitchevaluation,wefirstlookedatwhether there is indeedadiscontinuityat thecut-off in theprobabilityofbeingselected, i.e.whethertheprobabilityofselection“jumps”atthispoint.76

ThegraphbelowshowstheaverageprobabilityofbeingselectedforDataPitchplottedagainsttheapplicationscore77.Theredlineshowsalinearregressionthroughthedots,allowingthelinetobedifferentoneithersideofthecut-offvalue.Thegraphclearlyshowsthat:

1) thereisapositiverelationshipbetweentheapplicationscoreandtheultimateprobabilityofbeingselectedfortheDataPitchprogramme,and:

2) thereisaclearincreaseintheprobabilityofbeingselectedatthecut-offvalueof6078,asshownbythediscontinuityintheregressionline.

Furthertestingshowsthattheprobabilityofbeingselectedfortheprogrammeincreasedbyabout29percentagepointsatthecut-offvalue.This jumpishighlystatisticallysignificant(p<0.01). Inprinciple,therefore,FRDDcouldbeapplied.

76Normally,onewouldalsocheckforthepossibilitythatscoresweremanipulatedtofavourcertaincompaniesthatshouldnotbeselectedbasedonthescoringcriteria.Thiswouldunderminetheassumptionthatapplicationswithsimilarscoresareofsimilarquality.DataPitch’sreviewprocessaccountedforpotentialconflictofinterestsbyensuringthatapplicationswerereviewedbyareviewerwithoutarelationwiththereviewedcompany.Furthermore, thereviewerswerenotdisclosedtotheapplicants.Therefore, therewasno incentive forreviewerstomanipulatescores.77Thedotsrepresenttheprobabilityofbeingselectedperbinoftwo.Inotherwords,itshowstheprobabilityforapplicationswithscores(whereavailable)0-2,2-4,4-6,etc.78Thegraphshowsgroupeddataforbothcohorts.Theaveragescoresforcohort2weremultipliedby20togetascoreoutof100withthecut-offat60.



Figure43 ProbabilityofbeingselectedfortheDataPitchProgramme

Notes:Basedon165applications.Thescoresoftwoapplicationswerenotknownattimeofwriting.Cohortsaregrouped.

Source:DataPitchmaterials

Asimilargraphcanbeusedtoshowthatthecut-offvalueof60toobtainaninterviewwasnotrigidlyapplied.ThegraphbelowshowsasimilargraphasFigure43but,insteadofshowingtheprobabilityofbeingselectedfortheprogramme,itshowstheprobabilityofobtaininganinterview.

Thegraphshowsthattheprobabilityofobtaininganinterviewdidjumpbymorethan50percentagepointsatthecut-offof60.However,somecompanieswithascorelowerthan60wereinvitedforaninterview,andsomecompanieswithascorehigherthan60werenot.

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Figure44 Probabilityofobtaininganinterviewintheapplicationprocess

Notes:Basedon165applications.Thescoresoftwoapplicationswerenotknownattimeofwriting.Cohortsaregrouped.

Source:DataPitchmaterials

A4.2.2 Availabilityofoutcomemeasures

Having established that a quasi-experimental impact assessment is feasible in principle, weconsideredsuitableoutcomemeasuresfortheanalysis.Suitableoutcomesmeasureshavetosatisfytwoconditions:

1) theyhavetocapturechangeovertime;and,2) theyhavetobeavailableforbothsuccessfulandunsuccessfulapplicants.

Examples could be survival of companies, increases in employment or funding attracted bycompaniessinceapplyingtoDataPitch.Ourconclusionatthisstageisthatthenecessarydataisnotavailable:

¢ Survival rates: Theendof theDataPitchprogramme is too recent,and theprogrammedurationtooshort,forsurvivalratestobeusefulasoutcomemeasure:£ Noimpactonsurvivalratescanbeobservedforcohort2yet.£ Most unsuccessful applicants still exist.Of all unsuccessful applicants,we identified

nineasinactivewithafurther4forwhichthestatuscouldnotbediscerned.¢ Employment:Whileemploymentdataisavailableforthesuccessfulapplicants,thisdata

could not be obtained for unsuccessful applicants.We investigated the use of externalsourceforemploymentdataforunsuccessfulapplicants(OrbisandCrunchbase),butheredataisscarce.Wecouldonlylocateemploymentfiguresforaround30%ofallapplicants.



¢ Funding: Similarly, funding data for unsuccessful applicants (Crunchbase) is sparse andincomplete.

Weconsideredotheroutcomemeasuressuchasadvancementintechnologyreadinessandbusinessdevelopmentstage,butwerenotabletoovercomethedatalimitations

SomersetHouse,NewWing,Strand,London,WC2R1LA,[email protected]@LondonEconomics

+44(0)2037017700

data pitch evaluation · 3.12 comparison with odine and nesta-reviewed accelerator 69 4 case...

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