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Big Data at Vectren

Tom Vargo, Vectren

Thursday, June 16, 2016

2016SoutheastU/li/esDay:ReimaginetheDigitalU/lity

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Session Agenda

•  Introduc+ontoVectren• QuickHistoryonBigData•  FindingValuewithCorporateAnaly+cs• Organiza+onalDesign&NextSteps

Thankyouforhavingmetoday

3 Introduction - Vectren at a glance

•  HQEvansville,IN

•  +1MMgascustomers

•  157kelectriccustomers

•  Opera/ngCenters:48

•  BargainingUnitContracts:5

•  ServiceTechnicians:250

•  Distribu/onFieldTechnicians:500

•  Engineers&BackOfficeSupport:150

Non-Regulated businesses: §  Miller Pipeline §  Minnesota Limited §  Energy Systems Group (ESG)

750MobileFieldworkers

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2012-2013–Emergingtrend

2014–TopicalinNews,U/li/es(esp.smartgrid)–Vectrendoesaproofofconcept

2015–“BigData”iscool,showsupinDilbert,HouseofCards(season4,ep.5-8)

2016–EstablishCorporateAnaly/csFunc/on

Quick History: Big Data Timeline

2016–Setitup.2017–2018–Makeitpayforitself!

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Buzzwordtodescribetheabilitytoanalyzemassiveamountsofdatanoteasilyanalyzedusingtradi/onalsoawaretools.WhatarethehallmarksofBigData?

•  Processesstructured(e.g.Oracle)&unstructureddata(socialmedia)

•  Transcendsdata“silos”usingmassivestorage–availablereal+me

•  Predic@veanaly@cs:correla+ons->rela+onships->paUerns->causa+on

•  Complexdataunderstandableusingadvancedvisualiza+ontools

•  Enablesnewlearning,newtheories–datareservoir,availabletoData

Scien+sts

"Afull90percentofallthedataintheworldhasbeengeneratedoverthelasttwoyears.“–ScienceDaily

Big Data Defined

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Thisismathema@cs,i.e.whataDataScien@ststudies:

IfIcaniden/fytheX’s(causa/on):𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑆𝑎𝑡𝑖𝑠𝑓𝑎𝑐𝑡𝑖𝑜𝑛="Y" = f(𝒙𝟏, 𝒙𝟐, 𝒙𝟑,…,𝑥𝑛)

ThenImaybeabletoinfluenceoutcomes(predic/on)

Y= 𝜷𝟏𝑥1+𝜷𝟐𝑥2+𝜷𝟑𝑥3+…+𝜷𝒏𝑥𝑛+𝛆

So:Canweusepredic@veanaly@cstobeIerunderstandourcustomersandincreasesa@sfac@on?

Dataleadstoconversa/ons,whichleadstosharedunderstanding,whichdrivesac/on.

Correlation > Causation > Prediction > Prescription > Value

7“Tech Stack” – Current State

Viryanet

Banner

Information Bus (Data Grand Central)

Vectren.com

Oracle

Esri GIS

Power On

Maximo

Disaster Recovery (Data TiVo )

Weinvestedinamessagehub,or“middleware”,in2004andhavegrownittoover500connec+onpointstoourcri+calsystems.

Business Process Management (end to end overlay)

Dataisthe“milkbetweenthecornflakes”

8Big Data “Tech Stack” Cost Drivers

Cloudera Hadoop Cluster

Viryanet

Banner

Information Bus (Data Grand Central)

Vectren.com

Business Process Management (end to end overlay)

Oracle

Esri GIS

Power On

Maximo

Transaction Journaling (Data TiVo )

Real time “Predictive Analytics” triggering Value generating “Prescriptive Analytics”

“Social Media”

feed

Analytics Laboratory Machine Learning

BigDataisallabout3things:Value,ValueandValue

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§  20% of the timeline was installation, skills development

§  70% of the time was data loading

§  Data Prep and cleanup is a large part of the job, and, we found data

“gaps”

§  Producing the actual deliverables , i.e. using the tools, was done in 3

weeks

§  “Answers” can be found, but it requires discipline and time

POC Outcomes: Key Learning

ThiswaslikeanIndyracecar,wedid3lapsat40mph(andwehadfunandreallywantedtoopenitup…)

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Big Data POC Results

Results•  Abletoconnectsurveydatatootherdatatables•  Successfullystoredmul+plesourcesinHadoop•  MasterDataMgmt(MDM)tooltocreate“goldcopy”ofcustomer

Implica/ons•  Plabormfordeepanaly+csoncustomersurveystoiden+fydriversofsa+sfac+on•  Couldexpandsurveytoallcontacttypes,regardlessofcontactmethod(currentlywedoGasLeaks,Outages&NewCustomer)

•  “Cleanse”data,beUertotalviewofthecustomer

PlannedDeliverable:CombinedCustomerSurveydatawith…

Volume

Veracity

Visibilityintothedatawehadnothadbefore

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Results•  BuiltworkingprototypeCSRscreenwithnewinforma+on•  Insight1–FoundVectrenonTwiUer/Facebook,abletoiden+fycustomer“behind”theirscreenname

•  Insight2–“Inmemory”drama+callyreduced+meforcustomerlookups–saveshandle+meImplica/ons•  Plabormforpersonaliza+on,i.e.likeOpower’s“momentsthatmaUer”•  Abilitytoenhancecustomerexperienceacrossallchannels•  Abilitytoleveragesocialmedia

•  Canopera+onalizenewlearning•  Contactchannelcontainment(serveatlowestcostusingmethodpreferred)

PlannedDeliverable:“Omni-view”oftheCustomer,“Fastdata”andSocialMedia

ValueBig Data POC Results

Accesstodatawehadnothadbefore

12CSR Screen – working prototype

Predic/onWindow

60daywindowofac/vity

Op@onsforValuewehadnothadbefore

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Results•  DeeperanalysisofCustomer•  Scratchedthesurfaceofiden+fyingdriversofsa+sfac+on

Implica/ons•  Allows“directedcuriosity”,abilitytotesthypotheses

PlannedDeliverable:Predic/veAnaly/cs

Nowlet’sgettoPredic/on,butbeforewestart…..

Big Data POC Results

So,visibility,accessandop@ons,whataboutpredic@on?

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Aren’tcustomerswhorespondtoourContactSurveyinfluencedbythesizeoftheiru+litybills?

NewInsights:FirstSteps

15ContactSurvey:NotSo!Sizeofbillunrelatedtooverallsa/sfac/onscore

Whenconcentric,nosta/s/caldifference

“Hmmm,that’sinteres@ng”

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CustomerswhorespondtoourPercep+onsurveysareinfluenced

bythesizeoftheirbills

NewInsights:FirstSteps

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Percep/onSurvey:True!Sizeofbillsignificantlyrelatedtosa/sfac/onscores

Whennotconcentric,thereisasta/s/caldifference


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Warning:Thefollowinggraphsarerated“M”,forMaybe

Nospecificconclusionsorrecommendedac+onsatthispoint.

19Canwedigdeeper…?

§  We want to develop a PREDICTIVE MODEL, that can estimate a customer’s response on overall satisfaction given a set of values for predictor variables.

§  But what are the right predictors?

§  How do we know?

§  Do we have the right data? In sufficient volume?

§  There are a host of techniques for developing predictive models, selecting the right one and properly applying it is a competency unto itself.

Before we try to build a predictive model, let’s continue searching for predictor candidates. ..

20“Chi-Square”technique:Significantrela/onshipbetweenoverallsa/sfac/onandagebracket:p=.02<alpha=.05


21Chi-Squaredforoverallsa/sfac/onversusincomebracket:notsignificantatp=.91>alpha=.05,sofornow,notaleadtofollow…


22DirectedCuriosity:Howdoesoverallsa/sfac/onrespondtosmartphoneownership?Moresothanincomeorage!(p=.004<alpha=.05)


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Justforfun,quickdetour

24Whatifwenormalizeagetoourdemographic?

0.6% 0.5% 2.0%

Satgoesdownby:


25Ifwenormalizeandprojectsmartphoneownershipwhathappens?

Whatcouldwedoaboutthatnow?•  UseHadoop+FastDatatosurveymorecustomersacrossallchannels

•  DigdeeperonSmartPhoneusers,holdfocusgroupstoget“inside”theirheads

•  Con+nuetoimprovethecustomerexperience

•  Onepossibility:developpersonalizedoutboundmessagesonenergyuse,ata@methatishelpfultothecustomer

N–0.2% O–0.3% S–0.9%For2015:

N–1.1% O–1.3%For2020: S–3.8%


26Sowhat…… 55%?

1millionads 300clicks 3applica+ons 1approval0.0001%ofads

resultinapprovedcustomer

Isn’tthatasgoodasacointoss?

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Isitthatinteres/ng?

StrategicRabbitHoles

28BigDataPoten/al–Isitthatinteres/ng?Poten:alareastoapplyBigData

Benefits:

•  Elimina/ngspecificexcep/onprocessing

•  Improvementsinexcep/onhandlingshouldbenefitcustomersa/sfac/on

•  Improvedcustomerservicetechniques,outboundmessaging,richerinfoonCSRscreens,etc.

•  ShouldreceivefavorablereviewbyregulatorsValue-$500kofsustained

O&MsavingsReadytoexecute

Benefits:

Iden/fied3usestoreducecosts:

•  Earlydetec/onofcurbing,reducedtruckrolls

•  Detec/ngdiversion,lostrevenueandinves/ga/onexpense

•  Improvedmetertracking

ValueTBD–es/matedrangeof$300k-$500k

O&Msavings

Benefits:

Iden/fiedmul/pleuses:

•  Assetriskanalysis(leaks,facili/esdamage)

•  AnalysisofTOUdatafromAMI

•  Engineering&Constr.costmanagement

•  Workmgmtforecas/ng

•  Routeop/miza/on

ValueTBD–es/matedrangeof:$200k-$400kO&Mand

$500k-$2Mcapital

Benefits:

Iden/fiedmul/pleuses:

•  Healthoutcomeanalysis

•  Auditcon/nuousmonitoring

ValueTBD–est.rangeof:$100k-$300kO&M

BigDataisallabout3things:Value,ValueandValue

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Monthly frequency

Monthly hrs impact Annualized High Level business analysis Advanced Business Rules and automation opportunity

% Reduction of manual

handling Monthly time

savings Annual time

savings 10 25 300 Manual effort is expended researching why

a bill appears to be abnormal. Because of the nature of VIPs, they are special handled to research root causes and factors that affected the situation. Anecdotally, 90% of the time a plausible / reasonable explanation is identified, hwoever many factors need to be analyzed.

With Spotfire, Complex Business Rules , Fast Data and Hadoop:Analyze low bill for estimate/actual history.Compare estimate to multiple years same month and to peer meters.Analyze weather during billing period against norm.If all tests pass, no issue with the meter so auto-insert a bill message that explains the exception: Bill was (high or low). Read was actual. Weather was cooler than normal. Energy usage was xx. This is first actual , previous were estimates. Explain customers with similar homes also experienced reduced bill due to weather. IF - energy reduction wasn't same as peer meters, add a conservation message.

90% 22.5 270

20 480 5760 A process will improve estimation to prevent over stated consumption which causes the negative and cancel-rebill to be performed. This process will be on an account by account basis outside of Banner.

With Spotfire, Complex Business Rules , Fast Data and Hadoop:Analyze consumption history and factor in weather data to produce a more accurate proration to be used in lieu of the current Banner methodology to avoid processing to avoid the cancel rebill for customer experience.

25% 120 1440

21 630 7560 Considerable amount of time is spent researching to determine whether a meter has become non-registering, due to lack of valid appliance information or good record of premise occupancy. Customer contact must be made to verify if usage is occurring

Analyze historical consumption patterns for multiple yearsAnalyze weather during billing period against normIdentify if meter age correlates to other confirmed non-registering meter types for accurate identification

50% 315 3780

21 336 4032 Current resources and systems do not allow an effective and efficient method of identifying slowing meters. This equates to lost revenue due to accurate usage not registering and eventually the meter no longer captures usage.

Analyze historical consumption patterns for multiple yearsAnalyze weather during billing period against normIdentify if meter age correlates to other confirmed non-registering meter types for accurate identification

50% 168 2016

21 336 4032 Current resources do not allow for trending and analysis of off-cycle reads that may be obtained once AMR is fully implemented

Analyze off-cycle read that is obtained to past month on-cycle usage and historical usage during same time framAnalyze weather during billing period against norm to look for meter issuesAnalyze meter based on age and model to look for trends with meter issues

50% 168 2016

Total FTE Hours 21684 Net FTE Hours Savings 9522

approximate FTEs 10.425 $476,100

Ini/alRevenueMgmtUseCases:$500kO&MHardSavingsiden/fied

30OtherPoten/alUses-$1.7MO&Miden/fied,page1of2: Use Case Opportunity High Level business analysis Impact Description Benefit Description Benefit Value

Customer / BillingCurbing Detection Combine Meter Reader data with Billing usage data

and identify irregularities.

Evaluate current usage to historical usage, weather and peer meters. Identify any out of range conditions. Compare meter reader history to identify patterns of abnormality.

Causes high/low investigations.

Time spent with vendor to prove out the issue

Saved time and effort from high/low investigations.

Reduced re-read truck rolls.

Improved customer sat

$100

Meter Tracking Link Banner to Oracle Financials and Meter Inventory System (MITS). Real time tracking and reconciliation of meters being, purchased, installed, and setup in Banner.

Talk to Jon on this : look at all business and reconcile to all meters to ensure every one is billing

Minimize revenue loss from meter not in Banner

$100

Diversion Identification link the LUG data and Gas Operations with billing and ESRI then we could potentially identify diversion quickly and pinpoint within a certain geographic radius

Minimize lost revenue $100

Personalized Messages like OPower Specific messages on:Actions to reducing energy consumption.Products/services to reduce energy.Early warning on high bills.Peak days warning.Outages

Increase customer satisfactionReduced subscription costs for Opower Messaging

$100

Analyze Mass Accepted Low Bills Currently low usage bills are mass accepted (confirm with Rob) Analyze low bills for patterns and explanations and operationalize personalized messages to customers as bill inserts of text messages explaining why for ones that follow standard pattern (weather, estimates/actual reconciliation, etc).

Causes extensive manual review and response.

Save FTE manual labor review time.

Reduce bill complaint calls to contact center.

Reduce Regulatory complaints and negative social media events.

Increase customer satisfaction.

$476

Optimize Marketing Campaign Effectiveness

Use predictive analytics to optimize adoption rates for customer targeted programs such conservation, budget bill, etc.

TBD

Energy DeliveryEngineering

Analyze estimates across engineers and to actuals to determine if it is standard approach and what is estimated matches final as built

Improved design$

$200

Facilities Damage Analyze historical damage cases to identify any patterns that are preventable.

Reduced damage claims$

$200

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Use Case Opportunity High Level business analysis Impact Description Benefit Description Benefit ValueEnergy Delivery Leaks Analyze leak history and look for patterns that may

provide early warning or shift priorities for replacement Safety TBD

Truck rolls Analyze various truck rolls and look for patterns to validate cause and avoid expense of the roll

$truck roll = $60

$100

Work assignment / Routes Analyze assigned work routes, break in work and re-routing for efficiency improvements

mom budget increased over 6 years 14m - 16.5m

$2.5M labor if we can get route efficiency back to 2009 baseline

$100

Work Forecasting & Planning Load years of work history and labor type/costs and create simulation models that allow you to manage timing, labor pool etc to reduce costs

$ reduced labor costs by optimizing internal/contractor crew mix, shifting work to take advantage of time of year, optimizing use of large asset (e.g. backhoe)

$200

Safety accident prevention:Personal injuriesMVAs

Combine Maximo, PowerOn, Viraynet, Personnel records, and safety records to identify patterns of injuries. Is it years of service? Time of year?

property and insurance costs, health care, days away with restricted duty or lost time

$50

Electric reliability study where outages have been , asset perfromance (lightning strikes, arrestor failure, etc etc )

TBD

DIMP Risk and safety mgmt systemADMS

Human ResourcesHealth, Wellness and Retirement programs

Refer to Sep 2015 HR Magazine article on Big Data. Suggests using health and wellness data and campaigns to study outcomes and adjutsments to programs. Some areas they were researching: Where can employees get top-level medical care at thebest prices? Which workers are at risk for becomingseriously ill? They also studied retirement planning and employee investments.

$50

TOTAL POTENTIAL SAVINGS $1,776

OtherPoten/alUses-$1.7MO&Miden/fied,page2of2:

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BigData–Organiza/on–2016

CIO,VPofIT

Director,StrategicTechnology

Sr.DataScien/stCorporateAnaly/cs

Laboratory

LeadSystemsAnalystCorpAnaly/cs-Tech

SystemsAnalystCorpAnaly/cs-Tech

Organiza/on

•  CentralizedinITbutavailabletoallbusinessunits

•  Laboratoryisresearchcenter•  TechnologygroupworkswithLaboratory

toenableresearch,andtoimplement“opera+onalized”changes

Manager,CorporateAnaly/cs

Technology

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BigData–MatureOrganiza/on-2018

CIO,VPofIT

Director,StrategicTechnology

Manager,CorporateAnaly+csTechnology

Manager,CorporateAnaly+cs

DataScience

DataScien/stCustomerOpera/ons

DataScien/stEnergyOpera/ons

DataAnalystBigDataLab

DataScien/stFinancial,Audit,Risk

DataAnalystBigDataLab

LeadSystemsAnalystCorpAnaly+cs-Tech

SystemsAnalystCorpAnaly+cs-Tech



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APPENDIX

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BigDataApplied–Non-U/lity–EarlyAdopters

•  CMEGroup:Aggrega+onandanalysisofreal-+memarketdatatoautomateprocesses,modifyalgorithms,andtokeepupwithbusiness(trader)demand.

•  Proctor&Gamble:Leverageslargevolumesofdatatoanalyzeperformancetothecountry,territory,productline,andstorelevel.ThisprovidesinsightintotheleversP&Gcanpull,suchaspricing,adver+sing,andproductmixtoop+mizesales.

•  Macy’s:Customerbigdatatounderstandbehaviorandawtudes,productplacement,cross-sellingandupsellforOMNIChannelmarke/ng.

•  GM:Largevolumesofmaterialscosts,salesvolumeandgrowthtrendstodeterminetobrandprofitabilitybycountry

•  AT&T–increasedfreecashflowby1/3byreconcilingvarioussystemsaccoun+ngdataandmethods.

•  PlanetFitness–equipmentusageandrota+onschedulebasedonfloorloca+on

•  Healthcare–clinicalpathways,pa+entcasemanagement

•  CDC–contagionimpactanalysis

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BigDataApplied–U/lityPeers

•  Centerpoint–totalviewofthecustomer,masterdatamanagement

•  St.LouisSewage–infrastructuremoderniza+onandregulatoryratecase

•  BCHydro:Analyzeddatafromover300Energysystemstoimproveoutagemanagement,restora+onandcustomerno+fica+on.

•  ERCOT:Studiedwholesalemarketdatatoimproveopera+ngefficienciesbyusingmorerapidanddetailedpricingandscheduling(i.e.,beUerpricesignalsforloca+nggenera+onandtransmission).

•  PJM:Analyzesbigdatafromweathermodelsandenergydistribu+onacrossthe13NortheasternStatestopredictandcontrolnecessaryenergydistribu+onandrou+ng.

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ThanksforHavingMe!

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