Big Data in Malaysia: Emerging Sector Profle2014Big Data in Malaysia: Emerging Sector ProfleiiBig Data in Malaysia: Emerging Sector Profile 2014Contact us at sandra@bigdatamalaysia.org, tirath@bigdatamalaysia.orghttp://survey.bigdatamalaysia.orgAttributionExcerpted material from this report can be cited in media coverage and institutional publications. Text excerpts should be attributed to Sandra Hanchard and Tirath Ramdas. Graphs should be attributed in a source line to: Big Data Malaysia 2014ThisworkislicensedunderaCreativeCommonsAttribution-NonCommercial-NoDerivatives4.0 International License.Big Data in Malaysia: Emerging Sector ProfleiiiForeword by Multimedia Development Corporation (MDeC) BigDataAnalyticsisacrucialsectoringrowingMalaysiasdigital economy. Multimedia Development Corporation (MDeC) is committed to building a vibrant and dynamic Big Data Analytics industry as a key componentofICTservicesinMalaysia.TheMalaysianICTServices sub-sectorhashugepotentialgrowth,withaprojectedshareof 48%1 in the nations digital economy in 2020. These ICT services sub-sectorsincludebigdataanalytics,cloud,mobility,socialmediaand telecommunications.ICTservicesencompasssoftwaredevelopment and IT services.MDeCsstrategicintentisforMalaysiatobeahubforBigData Analytics services, both as a producer and consumer by 2020. Our goal is to catalyse the adoption and usage of Big Data Analytics across all private and public sectors. This reflects the Digital Malaysia initiative 1IDC and MDeC AnalysisivBig Data in Malaysia: Emerging Sector ProflevBig Data in Malaysia: Emerging Sector Proflewhich MDeC is driving, to advance the country towards a developed digital economy by 2020. Digital Malaysia aims to create an ecosystem thatpromotesthepervasiveuseofdigitaltechnologyinallaspects of the economy to connect communities globally and interact in real time.ThiswillultimatelyresultinincreasedGrossNationalIncome (GNI), enhanced productivity and improved standards of living.To game change Malaysias digital landscape, last year MDeC announced theDigitalMalaysia354(DM354)Roadmap,whichencompassesa plan that addresses three ICT areas: access, adoption and usage across five key subsectors: ICT Services, eCommerce, ICT Manufacturing, ICT TradeandICTContentandMedia,targetingfourimportantDigital Malaysiacommunities.ThesecommunitiesaretheB40group(the lowerpercentoftheMalaysianpopulationintermsofhousehold income),digitalentrepreneurs,smalltomidsizedenterprises(SMEs) andyouth,wherebyelementsofDigitalMalaysiasinitiativeswillbe proactively embedded in selected programmes and initiatives.TheDM354initiativewillrolloutwiththeICTservicessectorwhich willseethegrowthofBDAadoption.Withtheparticipationofthe governmentandprivatesectors,BDAadoptionwillnotonlyreduce costsandraiseproductivityandcompetitivenessbutalsoprovidea comprehensive and holistic approach that is needed to grow the Big Data ecosystem in Malaysia. The DM354 roadmap was approved by the Prime Minister, Najib Razak last November at the 25th MSC Malaysia Implementation Council Meeting. This council meeting approved the development of Malaysias Big Data framework, relevant government pilot projects and funding for the private sector.MDeC and Big Data Malaysia organisations share common goals such as improving Big Data literacies in Malaysia and in helping to connect supplywithdemandthroughnetworkingopportunities.MDeChas worked with Big Data Malaysia during the Big Data Week festivals in Kuala Lumpur for the anchor events, Big Data: Strategy, Practice, and Research in 2013 and the forthcoming Big Data: Endless Possibilities in 2014. Last year we welcomed diverse participants from engineers to executives, across all organisational levels and are looking forward to anevengreaterresponsethisyear.WearedelightedtosupportBig DataMalaysiassurvey2014report,BigDatainMalaysia:Emerging SectorProfile.Wecommendthegoalofthereportinraisingthe profile of Big Data initiatives in Malaysia and identifying areas of need and opportunities for growth.AchievingDigitalMalaysias2020aspirationalgoalsisachallenge forthewholenation.Issuesofdatamanagementandgovernance, talentdevelopment,funding,opendata,policiesandregulations, andchangemanagementwillcontinuetoshapeMDeCsagendain supporting the Big Data Analytics industry in Malaysia. We welcome the sharing of knowledge, successes and learnings in supporting each other through this journey.Chief Executive OfficerDatuk Badlisham GhazaliviBig Data in Malaysia: Emerging Sector ProfleviiBig Data in Malaysia: Emerging Sector ProfleTable of ContentsForeword by Multimedia Development Corporation (MDeC) ivAcknowledgementsviiiIntroduction1Who should read this report2Key Findings3Seven broad recommendations 5Survey6About the respondents6Enabling organisations8End uses10Skills12Capabilities16Data sources18Profiles21Overview21Heislyc Loh, CoRate24End uses and data sources24Malaysian environment and skills recruitment25Tom Hogg, Effective Measure26Measuring online audiences27Regulatory and Malaysian business environment27Siim Saarlo, STATSIT30Practices30Infrastructure31Data environments 31Robin Woo, Western Digital Corporation32Scope and context32Data governance33Resourcing and Malaysian environment33Ian Phoon, Malaysian bank36Data integrity and prediction36Tools of the trade37Industry benchmarking38Conclusion39About the authors40Sandra Hanchard40Tirath Ramdas40About Big Data Malaysia41AcknowledgementsThefirstportionofthisreportisbasedonasurveyconductedin October 2013. To encourage survey questionnaire completions, lucky draw prizes were offered. We would like to thank the sponsors of these lucky draw prizes: Olygen (organisers of the Big Data World Show conference series)Merlien Institute (organisers of the Market Research in the Mobile World conference series)Revolution AnalyticsWewouldalsoliketoacknowledgeourquestionnairetestersfor theirinvaluablefeedbackpriortothelaunchofoursurvey.Our thanks go to Diana Jayne Gonzales (Big Data Philippines), John Berns (BigDataSingapore),AndeGregson(BigDataWeek),MarkSmalley (BrainControl),DanielWalters(ExperianMarketingServices),Jasper Lim(MerlienInstitute),PakMeiYuet(MultimediaDevelopment Corporation), Luke Lai (Olygen), Sivaramakrishnan Narayanan (Qubole), AndrewLooHongChuang(TaylorsUniversity),JoethiSahadevan (TaylorsUniversity),SzilardBarany(Teradata),GopiKurup(Telekom Malaysia Research & Development), Syahrizal Salleh (Telekom Malaysia Research&Development),MargieOng(ThoughtsInGear),ReiLim (Transtel Technology) and Annie Hibberd (VLT).Forassistancewithdisseminationofoursurveyquestionnaireto theirnetworksandotherformsofsupport,wewouldliketothank theMultimediaDevelopmentCorporation(MDeC),theAssociation of Banks in Malaysia (ABM), Big Data Singapore, Big Data Philippines, andEmTechSingapore.Wewouldliketothankallrespondentsfor contributingtheirprecioustimesothatwecouldhavethedata needed to conduct the analyses. Furthermore, we would like to thank respondents who shared with us quotable insights, including Jin Chuan Tai (ChrysaSys Consulting) and Greg Whalen (Pivotal) and others who preferredtoremainanonymous.Thesecondportionofthisreport features profiles of Big Data thought leaders in Malaysia. We are very gratefultohavebeenabletointerviewHeislycLoh(CoRate),Tom Hogg (Effective Measure), Siim Saarlo (STATSIT), Robin Woo (Western Digital Corporation) and Ian Phoon (from a Malaysian bank) for their time. A note of thanks also to Raju Chellam (Dell Enterprise Solutions) and Elena Tan (JobStreet.com) for participating on a panel dedicated to this report during Big Data Week 2014. For assistance with editing this report and providing feedback on our survey questionnaire, we owe a huge debt of gratitude to Heoh Chin-Fah(StorageNetworkingIndustryAssociationMalaysia).Finally,we thank everyone who has contributed to the development of Big Data Malaysia.Wevalueallcontributions;simplyattendingameetupor postingsomethinginterestingtoourFacebookwallgeneratesvalue for the group.Sincerely,Sandra & TirathviiiBig Data in Malaysia: Emerging Sector ProfleIntroductionBigDatahasbecomepartofeverydayorganisationalparlance. Increasingly,thisawarenessisbeingtransformedintopractice. Supportforharnessingdatatoamplifycapabilitiesandachieve organisational objectives at practitioner and senior management levelsisbecomingaligned.AreportbyForrester2onBigData adoption in Asia Pacific in 2013-2014 observed a trend across all industries of using more types of data, from more sources, to enable timelierbetter-informedinsights.SimilarlyinMalaysia,thereis growing cultural acceptance that Big Data can and should enhance decision-makingprocesses,althoughpathwaysforadoptionare notuniformlyunderstood.Nevertheless,wearenowobserving atransitionphasefromcuriosityandenthusiasmtobuy-inand actionacrossstartup,corporateandgovernmentorganisations. This groundswell of interest fuels the basis of Big Data Malaysia, anetworkinggroupforprofessionalswithinterestinallthings BigData,includingNoSQL,Hadoop,datascience,visualisation, businessusecases,datagovernance,opendata,andmore.Our community has welcomed participation from stakeholders ranging fromcomputerscientiststodatajournalists,reflectingabroad societalinterestinBigData.Ourmissionistoencouragehigh caliber knowledge sharing and to provide a space for professionals withdifferentintereststocollaborate.Thisreportgrewoutofa need to understand in more detail the various networks that Big Data Malaysia helps to connect. In order to support the Big Data ecosystem that we see emerging, we identified critical questions that required further investigation, in particular: What are the opportunities and barriers to Big Data activity inMalaysia? Who is merely interested, versus who is actually committed? What is the current and future capacity for Big Data talent? Where are the critical gaps in training and skills? What are the soft inhibitors, including data access, regulationand perception?There are two parts to this report. The first includes results from aquestionnaire,whilethelatterfeaturesinterviewsconducted in-person or via email. In collaboration with various partners, we devised and distributed a questionnaire online across our networks and collected responses during October 2013. In our final sample, we collected responses from 108 individuals over 90 organisations. As our report will show, these viewpoints represent a diversity of organisational stakeholders and industries in the Big Data space. Wefollowedupwithinterviewsofhigh-profilerespondentsfor richer insight. 2Page 2, Big Data Adoption Trends in Asia Pacific: 2013 to 2014, John Brand and Michael Barnes, Forrester, 2013.1Big Data in Malaysia: Emerging Sector ProfleWho should read this reportTheobjectiveofthisreportistoraisetheprofileofBigDatain Malaysia. We conducted this research because we are passionate aboutthepotentialforBigDatatotransformtheMalaysian economy,providedtherightconditionsprevailtoallowthe ecosystem to thrive. If you share this broad view, then this report may be for you. Although anyone with an interest in Big Data will find something of value in this report, it was designed especially with the following perspectives in mind: You are researching how to facilitate engagement with BigData initiatives across various organisation levels. You are preparing to jumpstart your organisations Big Datainitiatives, and are trying to anticipate internal and externalchallenges. You are considering what use-cases you may target for yourown Big Data Proof of Concept (POC) projects and are tryingto identify what resources you will need.Thisreportshinesaspotlightontheperspectivesofsenior managementandpractitioners(e.g.analystsandengineers), acrossproducersandconsumersintheBigDataecosystem. ThisisthefirsttimewehaveruntheEmergingSectorProfile surveyfocusedontheMalaysianmarket.Futuregoalsinclude longitudinalandcross-marketanalysistoprovidemarket measures and benchmarks. Our scope is limited to presenting a grass roots profile of Big Data in Malaysia based on a thriving, networked community; serving as a foundation to inform readers of this report who may then go on to formulate recommendations that are relevant to their organisation.2Big Data in Malaysia: Emerging Sector ProfleKey FindingsAbout the respondents Interest in Big Data is emerging from both a consumptionand production perspective. Slightly more than 50 percentof respondents claimed to be in an industry vertical otherthan Information and Communications Technology (ICT),which indicates interest across the broader Malaysianeconomy beyond any specific sub-sector.Enabling organisations Thereiscomplacencyinhowmanagersviewtheabilityoftheirorganisationstoleveragedata.ThreeoutoffivemanagerssaideithertheyAgreeorStronglyagreethattheirorganisationiseffectivelyderivingtangiblebenefitsfromtheirorganisationaldataassets. Anticipated spend for Big Data in 2014 was bullish. Nearly athird of managers said that they would be increasing their spend on Big Data in 2014 compared to 2013 by more than 25 percent. HighoutsourcingintentamongstmanagersinICTindustriessuggeststechnicalfragmentationinBigDataindustries.ThereareopportunitiesforboutiquefirmsinMalaysia to meet specialist technical needs for global markets.End uses Customer-centric uses of Big Data dominates prioritisation.ThissuggeststhatinterestinBigDataisprimarilydrivenbythepursuitoftop-linerevenueandbusinessgrowth,ratherthanbottom-linefactorssuchasefficiency. Managers need to align the perception of the value of BigData for both internal and external goals towards a data-centric organisation.This means recognising both customer-centric and operational end-uses of Big Data.Skills DifferencesindesiredskillsacquisitionbetweenICTandnon-ICT firms are likely to produce a sustainable Big Dataecosystem.HoweversomeorganisationsmayparticipateintheBigDataecosystemasbothproducersandconsumers,especially organisations in the ICT and marketing services sectors. There is a need to promote the value of fundamental applied math and statistics skills. These may be overlooked due to thehype surrounding techniques perceived as more sophisticated.3Big Data in Malaysia: Emerging Sector Profle Specialiseddataanalysis,modeling,andsimulationarethemostcommonlydesiredskill.Intermediateandadvancedrespondentsratedthefollowingfiveskillsasthemost desirable: Big and Distributed Data (eg. Hadoop, MapReduce) Algorithms (eg. computational complexity, CS theory) MachineLearning(eg.decisiontrees,neuralnets,SVM,clustering) Back-End Programming (eg. JAVA/Rails/Objective C) Visualisation (eg. statistical graphics, mapping, web-baseddataviz)Capabilities Uncoveringpatternsinreal-timeisastronglydesiredcapability.Realtimeinsightsfromreal-timedatastreamsandUncoveringpatterns(e.g.segments,correlations)frommulti-structureddatasetsconsistentlyplacedinthetopthreeintermsofpriority. Data visualisation is the number one priority for managers. Data visualisation is a highly effective way to reveal patternsandtrendsthatcomputersystemsmaynotyetbeabletodetect in a fully-automated fashion.Data sources Mostorganisationshaveasinglesourceofdataratherthan combining internal and external sources. There are asegment of users that would benefit from external consultation in getting started with the basic goal of identifying data sources. OpendatafromgovernmentsourcesisneededtosupporttheBigDataecosystem.TwooutoffiverespondentssaidthattheyeitherAgreeorStronglyagreethathavingaccesstosomegovernmentdatadoesorwillcreatevaluableBigDataopportunitiesforthem. There is a general weariness by respondents of bureaucracy and red tape in achieving their Big Data projects. About one in five respondents said they believe their local legal and regulatory environmenttobeahindrancetotheirBigDatainitiatives. Publiccloudvendorsneedtoidentifyandaddressconcernsinhibitingadoption.Thisincludesprivacy,resourcecost,andperceptionsspecifictoMalaysianprofessionals.Onlyoneinfourrespondentswaswillingtouploadinternaldatatoapubliccloudservice.4Big Data in Malaysia: Emerging Sector ProfleProfiles TherearepocketsofmaturityintheMalaysianBigDataecosystem.Thisincludestheuseofsophisticatedtoolsandtechniquestowardsniche,highvalueusecases.Areasofexpandinginterestweprofiledincludedmarketresearch(e.g.brandmonitoring,demographicprofiling,anddetectionofpurchasingintent),manufacturing(e.g.rootcauseanalysis),finance(e.g.riskmanagement),andmanyothers(includingideasinwebapplications). Malaysians share the global trend of increasing concern todo with data privacy. The Personal Data Protection Act 2010(PDPA) has had an awareness-raising effect.Seven broad recommendations Fosteradata-centricorganisationalculture,where organisationalprocesses,decisions,andobjectivesare supported by leveraging a variety of data resources. Equipyourorganisationtomanageandmakedecisionsbasedonreal-timedata,inordertoremaincompetitive. Cultivatemulti-disciplinarydatascienceteams,consistingofspecialistswithadvancedskillsinmathematicsandstatistics,domainexperts,andothers. Combine data from internal sources within your organisationtogetherwithexternalsources(includingpublicandproprietarythird-partysources)tospurinnovation. Createadatadictionaryofdataassetsrelevanttoyourorganisation,tominimisefrictionfordatascienceprojectsacrossyourorganisation. Large organisations, and especially government, should make concertedeffortstoreleasedatatothepublicinmachine-consumableformtounlockvaluehiddenwithintheir data assets. ThePDPAshouldbefrequentlyreviewed,andrevisedif necessary,toprotectindividualswhilesimultaneously encouraging data projects.5Big Data in Malaysia: Emerging Sector ProfleSurveyAbout the respondentsSubstantialinterestinBigDatafromaconsumption perspective as well as a production perspective was indicated byourrespondentpool.Slightlymorethan50percentof respondentsclaimedtobeinanindustryverticalotherthan InformationandCommunicationsTechnology(ICT).Whilethe ICT sector dominated our respondent pool, interest was indicated by other major sectors such as marketing services, professional/scientific/technical services, educational services, and media and classifieds. Figure 1: What industry vertical is your organisation primarily in?n=108Source: Big Data malaysiaThe survey captured a broad range of professional perspectives at all levels of the organisation. We deliberately did not want tolimitourviewtoexecutivesorpurchasingdecisionmakers because our goal was to highlight the state of the overall Big Data ecosystem. Consequently, the respondent pool reflects a balance between decision makers and a class of respondents that we call practitioners, who have a hands on role with Big Data.Overall,24percentofrespondentswereC-levelexecutives (acombinationofCEOorequivalent,CIO/CTOorequivalent, andotherC-level),afurther24percentofrespondentswere 6Big Data in Malaysia: Emerging Sector Proflesenior/middlemanagement,and40percentofrespondents werepractitioners(comprisingsoftwareengineers,analysts, datascientists,technicalconsultants,aswellasscientistsand academics).Itisencouragingthatwealsoreceivedsome responsesfromfull-timestudents,indicatingthatawarenessof Big Data has reached into local universities and colleges.Figure 2: Job title that is the closest fit to your current role: n=108Other = 5Source: Big Data MalaysiaTherespondentprofileintermsoforganisationsizewas alsoreasonablybalanced.Forrespondentswhofellintothe practitionerorsenior/middlemanagerrolecategories,the organisations they represented ranged in size between very small organisations (including organisations with less than 5 employees as well as the self-employed) and large organisations (with over 1000employees).However,ourC-levelrespondentpoolwas heavilyskewedtowardssmallandmediumsizedorganisations, withalmost90percentofC-levelrespondentsbeingwith organisations of less than 75 employees.CEO, or equivalentCIO/CTO, or equivalentC-level OtherSenior/middle managerSoftware engineerAnalystData scientistAcademic or ScientistTechnical consultant (e.g. pre-sales)Full time student14%6%4%24%16%11%6%5%4%7%7Big Data in Malaysia: Emerging Sector ProfleEnabling organisationsBigDataapproachesarebecominginfusedintoorganisational culturesinMalaysia,reflectedbyresourcesbeingdedicated toBigDataprojects.Weaskedrespondentswithmanagerial responsibilitiestoprovideatemperatureoftheirBigData readinessandresourcingintentforBigDatain2014.Overall, wefoundthataspirationswerehighamongstmanagers,but actual allocation to personnel was conservative. The results were nevertheless encouraging in managers matching enthusiasm for Big Data with resource commitment.Therewassomecomplacencyinhowrespondentsviewed the ability of their organisations to leverage data. Three out of five managers said either they Agree or Strongly agree that theirorganisationiseffectivelyderivingtangiblebenefitsfrom theirorganisationaldataassets.Justunderathirdofmanagers were neutral, while just over one in ten managers said they either Disagree or Strongly disagree. However, organisations need to ensuretheyarenotover-estimatingtheircapacitytoleverage theirdata.AdiscussionwithGregWhalen,PivotalsChiefData Scientist, Asia Pacific and Japan, revealed that many organisations do not have a data dictionary. Data scientists use this inventory of enterprise data to identify new correlations that provide return on investment through creative use of existing information. While somemanagersmightbelievetheyarealreadyusingtheirdata assets effectively, newer approaches in data mining, exploratory visualisation and comparison across external data sets may shed new light on unknown opportunities. Anticipated spend for Big Data in 2014 was bullish. Nearly a third of managers said that they would be increasing their spend on Big Data in 2014 compared to 2013 by more than 25 percent. This large increase in investment suggests a small base of comparison, supportingargumentsthatweareexperiencingaturningpoint in Big Data in Malaysia from awareness to implementation. Only 9percentofmanagerssaidtheywereincreasingtheirBigData budget by less than 5 percent. The remaining bulk of managers (41percent)saidtheywereincreasingtheirbudgetfrom5to 25percent.Therewasasimilarspreadinspendingintentions betweenmanagersinICTandnon-ICTindustries.Intermsof increasingheadcountspecificallyaroundBigDatainitiatives, mostrespondentssaidtheyexpectedtogrowtheirstaffinthe 4-10 range, with few anticipating growth of more than 20 staff in the coming year. Create a data dictionary of data assets relevant to your organisation, to minimise friction for data science projects across your organisation.8Big Data in Malaysia: Emerging Sector ProfleFigure 3: How do you expect your spend on Big Data to change in 2014 compared to 2013?n=75Dont know / prefer not to say = 16 Source: Big Data MalaysiaHigh outsourcing intent amongst managers in ICT industries suggestedtechnicalfragmentationinBigDataindustries. While48percentofnon-ICTmanagerssaidtheywereeither moderatelyorextremelywillingtooutsourcehigh-skillstasks intheirBigDatainitiativestoexternalconsultants,therewas greater willingness in ICT industries, with a comparative number of managers accounting for 69 percent. This suggests increasing technical fragmentation in Big Data. There are opportunities for boutiquefirmsinMalaysiatomeetspecialisttechnicalneeds. LocalfirmscannurturelocaltalentandexporttoglobalICT marketsIncreasing by more than 25%Increasing by between 10% and 25%Increasin by between 5% and 10%No change / Increasin by less than 5%28%17%24%9%9Big Data in Malaysia: Emerging Sector ProfleEnd usesApplications of Big Data may be relevant to every organisational processandobjective,whetherinternallyorexternallyfocused. While some uses of Big Data are clearly understood and valued, othersarestillemerging.GregWhalen,ofBigDataplatform vendor,Pivotal,sharedwithussuccessfulusecasesranging fromidentifyingdriversofmanufacturingqualitytodetecting malwareoutbreakstopredictingcustomerchurntoidentifying at-risk patients. While applications might be relevant to specific domains,sharingcasestudiesacrossindustriescanhighlight creative processes in extending data analysis. Ultimately, there is aneedbyorganisationstodeterminehowBigDatastrategies align with their organisational values and objectives. Customer-centricusesofBigDatadominatedprioritisation byrespondents.Customerbehaviouralprofilingwasthe mosthighlyratedend-useintermsofrelevance.Improving customerserviceandexperience,retention,acquisition,cross-sell and upsell were also identified as important uses of Big Data. Respondents also valued Big Data for competitive intelligence in benchmarkingagainsttheirindustry.Thehighrankingofsocial trends monitoring highlighted the general appeal of social data. Of less relevance to respondents were uses rated to compliance andregulatoryissues,infrastructureandassetsmonitoring, supply-chain monitoring, risk management and operational cost management. This suggests that currently, interest in Big Data is primarily driven by the pursuit of top-line revenue and business growth, rather than bottom-line factors such as efficiency.Figure4:Howrelevantarethefollowingend-usesforBig Data capabilities from your own perspective?Thereweredifferencesinprioritisationbetweenrespondents inICTandnon-ICTindustries.Non-ICTrespondentsweremore likelytovaluesocialtrendsandbrandmonitoring,Conversely, ICTrespondentsexhibitedaswingtowardsinternaloperational end-uses.Thegreatestdifferencesbetweenthetwogroupsof respondentswereininfrastructureandassetsmonitoring,risk management and supply-chain monitoring. ICT respondents also Foster a data-centric organisational culture, where organisational processes, decisions, and objectives are supported by leveraging a variety of data resources.Customer behavioural proling Very relevant 103Customer service and/or experience 101Compettve intelligence 105Customer retenton 104Social trends monitoring108Customer acquisiton 117Customer cross-sell and/or up-sell 104Forecastng supply and demand 110Brand monitoring 109Product and service innovaton Moderately relevant 115Operatonal cost management 115Risk management 135Supply-chain monitoring Slightly relevant 132Infrastructure and assets monitoring 158Compliance and regulatory issues Not all relevant 111n=108 n=56 n=52End-uses ranked by priority of relevance Industry swing by All10Big Data in Malaysia: Emerging Sector ProfleSource: Big Data Malaysiagaveahigherprioritisationtoproductandserviceinnovation. Thereisaneedfornon-technicalbusinessestoappreciate howdata-drivenfeedbackcanimproveproductsandservices forcompetitiveadvantage.Differencesinprioritisationshould promptstakeholdersformulatingusescasesofBigDatato considerwhethertheyaretoointernallyorexternallyfocused; there is an opportunity to think outside-the-box for applications and to learn from other industries. Big Data vendors may choose to tailor products to specialised organisational functions.Managers need to align the perception of the value of Big Data forbothinternalandexternalgoalstowardsadata-centric organisation.Forexample,data-drivenapproachesinchange management,productandservicedevelopmentandcustomer buildingmaybecomplementedwithdata-drivenapproaches ofincreasingoperationalefficiency.IntheirperceptionofBig Data uses, managers were more likely than practitioners to value riskmanagement,customerretention,customeracquisition, productandserviceinnovation,customerbehaviouralprofiling and forecasting supply and demand. Practitioners had a stronger internal operational focus, being more likely to prioritise supply-chainmonitoringandinfrastructureandassetsmonitoring.The mantra of reducing cost while increasing value should be at the core of applying Big Data in organisations. 11Big Data in Malaysia: Emerging Sector ProfleSkillsOne of the most frequently cited obstacles to delivering Big Data initiativesisthedifficultyinrecruitingtherequiredskills.We presentedrespondentswithalistofskills3andaskedthemto scoreeachskillaccordingtoneedfromtheirperspective.Each responsescoredona5pointscale,rangingfromNoneedto Critical need, then scores aggregated to produce the following heatmap:Figure 5: To deliver your Big Data initiatives, how much need is there to recruit the following skills?Source: Big Data MalaysiaThemostcommonlydesiredskillacrossthecombined respondent pool was specialised data analysis, modeling, and simulation. This was followed by distributed systems, deployment and / or administration, and fundamental computer science and software engineering. There was consensus across segments that pure business skills (distinct from domain knowledge) was of low importance,buttheleastrelevantskillsetwashardware/sensor design.Weincludedthisskillsetinourlistasaproxyforthe Internet-of-Things concept that is often seen as closely related to Big Data.3Our list of skills was derived in part from the Skills List presented in Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work by Harris, Murphy, and Vaisman, 2013. We grouped together several related items from their list into composite items in our list.Cultivate multi-disciplinary data science teams, consisting of specialists with advanced skills in mathematics and statistics, domain experts, and others.ICT only All respondentsNon-ICTonlySpecialised data analysis, modeling, and simulation (e.g. operations research, machine learning, etc.)3Priority 1Priority 1 Distributed systems (e.g. Hadoop) deployment and/or administrationPriority 122 Fundamental computer science and/or software engineering433 Industry-specific/domain knowledge 244 Applied math and/or statistics 556 Web/mobile development and/or visualization 765 Research experience from any quantitative discipline 677 Business (strategy, marketing, product development, etc.)888 Hardware/sensor design 999 n=52 n=108 n=5612Big Data in Malaysia: Emerging Sector ProfleThereisnuanceinskillsprioritiesbetweenICTandother industries.Theoverallrespondentpoolcitedspecialiseddata analysis,modeling,andsimulationastheirmostpressingneed, with distributed systems (e.g. Hadoop) coming in a close second. However, when segmenting by industry (ICT vs all other industries), we see skews in preferences: Specialised data analysis, modeling, and simulation: 1st priority by non-ICT respondents. 3rd priority by ICT respondents. Distributed systems deployment and/or administration: Distant 2nd priority by non-ICT respondents. 1st priority by ICT respondents. Industry-specific/domain knowledge: 4th priority by non-ICT respondents. 2nd priority by ICT respondents.Thedifferenceindomainknowledgemaybeexpected(in particularifICTrespondentsaimtoprovideservicestonon-ICTconsumers,inwhichcasetheywouldneedtoacquire target domain knowledge to provide adequate service), but the difference in distributed systems versus specialised data analysis may point to different aspirations by the respondent groups.Differences in skills acquisition between ICT and non-ICT firms is likely to produce co-dependencies that shape a sustainable Big Data ecosystem. Broadly speaking, if we may assume that a significant portion of ICT respondents are participating in the Big Data ecosystem as solutions providers to non-ICT respondents and that only a small portion of non-ICT respondents are participating intheBigDataecosystemsasproviders,thenourfindingsmay indicate the emergence of a model of engagement with ICT players providing Big Data infrastructure capabilities and solutions, with somedegreeofindustrycustomisation.Meanwhile,non-ICT consumersaredevelopingdeepanalyticalcapabilitiespertinent totheirbusinessneedsontopoftheprovidedinfrastructure, notwithstandingtheroleofsmallboutiqueICTandanalytics consultanciesthatcanfillgapswhereveropportunitiesarise. SomeorganisationsmayparticipateintheBigDataecosystem as both producers and consumers, especially organisations in the ICT and marketing services sectors.Thereisaneedtopromotethevalueofappliedmathand statisticsskillsforBigDataanalytics.Itisnoteworthyand likelyapointforcautionthatrespondentsratedspecialised data analysis skills so highly but were lukewarm to applied math orstatisticsskills.Itispossiblethattherespondentpoolhave already developed significant practical expertise in applied math andstatistics,butanotherlikelyscenarioisthatthereexistsa lackofunderstandingoftheutilityandvalueofappliedmath 13Big Data in Malaysia: Emerging Sector ProfleandstatisticstoBigDataanalytics.Thehypearoundmore novelapproachessuchasmachinelearningmaybeattracting disproportionatemindshareoverappliedmathandstatistics, which respondents may have perceived as basic4 .Thebreadthofourrespondentpoolgivesrisetoabigrange intechnicalknowledge,whichmightleadtoconcernsthat somerespondentsmayhavenothadsufficientunderstanding toproperlyassesstheirneedforsomespecifictechnicalskills. Toaccountforthispossibility,wefollowedupwithadditional datacollection.Firstly,weaskedrespondents,Howwouldyou assess your own degree of technical understanding of Big Data?5 . Respondents chose one of the following options: Total beginner, Technical details are not relevant to my job role, Intermediate, andAdvanced.RespondentswhoansweredIntermediateor Advanced(56percentofourtotalrespondentpool)werethen presentedwithanexpandedskillslist6andaskedtoselectonly theirfirst,second,andthirdskillspriorities.Theresponsesfor eachskillwereweightedbypriorityandtheresultingscores aggregated. These results from skilled respondents reinforce our earlier findings: ICT respondents are skewed towards distributed systems skills. Non-ICTrespondentsareskewedtowardsadvancedalgorithms. There is lukewarm interest in math and statistical methods.An additional finding is that despite the overall lukewarm interest in statistical methods, there is one bright spot: temporal statistics. This points to interest in time series analysis, which is an important method applicable to many areas.DespitedifferencesbetweenICTandnon-ICTperspectives amongst intermediate and advanced respondents, it is clear that there is significant shared interest in the top five skills: Big and Distributed Data (eg. Hadoop, MapReduce) Algorithms (eg. computational complexity, CS theory) Machine Learning (eg. decision trees, neural nets, SVM,clustering) Back-End Programming (eg. JAVA/Rails/Objective C) Visualisation (eg. statistical graphics, mapping, web-baseddataviz)Targeted training in these areas, especially if offered in combination with each other, is likely to see widespread interest and provide 4In the Profiles section of this report, we will show that several thought leaders suggest that skills such as statistics and a scientific background are of significant value to Big Data initiatives, contradicting respondent perceptions.5This self-rating question only served as a basis to drill down into follow-up questioning; in no way do we suggest it may be used as an accurate gauge of the extent of Big Data technical knowledge in Malaysia.6Here we used the full Skills List from Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work by Harris, Murphy, and Vaisman, 2013.14Big Data in Malaysia: Emerging Sector Proflea major boost to the development of the Big Data ecosystem in Malaysia. Figure6:Ifyou(oryouremployee/serviceprovider)could only develop one skill, what should it be?Respondents presented with a skills list and asked to select their first priority, second priority, and third priority only. Responses for each skill scored according to priority, then aggregated.Source: Big Data Malaysia0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6Non-ICT (n=26)Combined (n=61)ICT (n=35)Big and Distributed DataAlgorithmsMachine LearningBack-End ProgrammingVisualizationTemporal StatisticsData ManipulationGraphical ModelsSurveys and MarketingProduct DevelopmentFront-End ProgrammingUnstructured DataSystems AdministrationStructured DataBusinesSpatial StatisticsClassical StatisticsSimulationMathBayesian/Monte-Carlo StatisticsScienceOptimizationNormalized priority15Big Data in Malaysia: Emerging Sector ProfleCapabilitiesThis section highlights specific capabilities that respondents seek in their Big Data solutions. Capabilities are desired characteristics and features that may cut across industry segments, be applicable tomultipleend-usesand/orrelyonsomecombinationofskills toproduceandconsume.Assuch,weinvestigatecapabilities asaseparate,thoughnotnecessarilyindependentissue.Some capabilitiesarespecifictocertaintypesofdata,butwestill regard these as unique capabilities rather than data source issues becausesimplyhavingaccesstothedatadoesnotproduce desiredoutcomesitisthehandlingofthedatathatcreates value. Ameaningfulopiniononthisissuerequiressomedegreeof technicalknowledge,soweonlyincludedrespondentswho answered either Intermediate or Advanced to the question, How would you assess your own degree of technical understanding of Big Data?7 (56 percent of our respondent pool). In addition to drilling into ICT vs other industry segments, here we also investigate the setofrespondentswhoareManagers,i.e.respondentswho answered Yes to the question, Does your role in your organisation include managerial/leadership responsibilities? (69 percent of our respondent pool). The subset of our respondent pool that comprise managers with at least Intermediate technical understanding of Big Data make up 43 percent of our total respondent pool.Uncoveringpatternsinreal-timeisastronglydesired capability.Despitedifferencesinperspectiveinthedifferent segmentsstudied,Realtimeinsightsfromreal-timedata streamsandUncoveringpatterns(e.g.segments,correlations) frommulti-structureddatasetsconsistentlyplacedinthetop3 intermsofpriority.Theseprioritieswereespeciallystrongfor ICTrespondents,whowentontoindicateDatadiscoveryand exploration across many data sources as their third priority. Taken incombination,thesearecapabilitiesthatareespeciallyrelated toBigDatainfrastructure,onceagainillustratinganapparent inclination for ICT respondents to focus on supplying capabilities forexternalconsumption,ratherthanfortheirowninternal consumption. 7Respondents choose one of the following options: Total beginner, Technical details are not relevant to my job role, Intermediate, and Advanced.Equip your organisation to manage and make decisions based on real-time data, in order to remain competitive.16Big Data in Malaysia: Emerging Sector ProfleFigure7:WhichofthefollowingBigDatacapabilitiesare relevant to you?Note: Each response scored on a 5 point scale, ranging from Not at all relevant to Very relevant, then scores aggregated to produce the above heatmap.Data visualisation is the number one priority for managers. This capability was also strongly desired by non-ICT respondents. This points to a consumption need; non-ICT and especially manager respondents may prioritise Big Data capabilities to support their decisionmakingprocesses,andvisualisationofdataisahighly effective way to reveal patterns and trends that computer systems may not yet be able to detect in a fully-automated fashion. In the overall respondent pool, visualisation was only the third priority because it was pulled down by ICT respondents, who only placed it as the fourth priority.Once again, there is no interest in Internet of Things (IoT). Despitetheopportunitiesforlinkingthetwoworlds(withIoT serving as a data source and Big Data producing value out of the raw data) it seems these fields, which are still relatively nascent in Malaysia, are yet to instigate much interest in combination. Even within the ICT segment of our respondent pool, there is virtually no interest in IoT, which suggests that even the supply-side is not yet ready to deliver these capabilities to the Malaysian market. As the Big Data and IoT ecosystems continue to evolve in Malaysia wemayseethislackofinterestgivewaytointerestingstartup initiatives.Inthemeantime,awarenessraisinginitiativesshould focus on this link.ICT only All respondents Non-ICT onlyManagers onlyReal-time insights from real-time data streams 2 Priority 1 Priority 1 3Uncovering patterns (e.g. segments, correlations) from multi-structured datasetsPriority 1 2 3 2Visualizing/presenting insights 4 3 2 Priority 1Data discovery and exploration across many data sources3 4 4 4Statistical analysis on big working data sets (>100GB) 5 5 5 5Automated decision making 7 6 7 6Machine-generated data (e.g. log files, periodic diagnostics)6 7 9 8Content and sentiment from online media (e.g. social media)9 8 6 9Efficiently and safely storing large data sets on infrastructure controlled by my organization8 9 10 7Image, video, and audio data 10 10 8 10Physical sensor networks (e.g. "Internet of Things") 11 11 11 11n=35 n=61 n=26 n=4617Big Data in Malaysia: Emerging Sector ProfleSource: Big Data MalaysiaData sourcesDataisthenewoil.Dimensionsofdataqualityandvalueare increasingly as important as volume, velocity and variety. The wider socialandbusinessimpactofBigDatainitiativesisdependent on the quality of data and information on which they are based. Dataenvironmentswhetheropenorproprietaryarebecoming increasinglycomplexasnewbusinessmodelsemerge.Demand for open data for both private and public sector use is increasing forendeavourssuchasdatajournalism.InMalaysia,complex social problems such as traffic congestion or local crime could be addressed by open data and crowdsourcing approaches, in both diagnosingproblemsandinprovidingfeedbackmechanisms from citizens. There are many issues related to data sources that weidentifiedduringoursurvey.Theseincludeuseofinternal andexternaldata,accesstoopengovernmentdata,regulatory issues and appetite for using third-party infrastructure (e.g. cloud services)fordatamanagement.Thesourcingandmanagement ofdatarequiresconsiderationofcomplexrelationshipsand issues, including strategic partnerships, security, and distribution, to name a few.Most organisations have a single source of data rather than combining internal and external sources. ICT respondents were morelikelythannon-ICTrespondentstouseopenaccess(e.g. government) and proprietary third party data as the sole source forBigDatainitiatives.Non-ICTrespondentswerepolarisedin terms of data resourcing where 43 percent said they were using a combination of sources, while 17 percent of respondents were not using any data for Big Data initiatives. There are a segment of users thatwouldbenefitfromexternalconsultationingettingstarted with the basic goal of identifying data sources. Respondents who werelikelytohighly-valuecustomerbehaviouralprofilingwere dependent on third party data such as social media. Figure 8: My organisation uses sources for Big Data initiatives primarily from the following:Dont know/prefer not to say = 16Source: Big Data MalaysiaCombine data from internal sources within your organisation together with external sources (including public and proprietary third-party sources) to spur innovation.17%7%28%27%6%20%6%11%43%36%Non-ICT(n=47)ICT (n=45)None yetInternal dataOpen-access third-party data (incl.government)Proprietary third-party dataCombinationLarge organisations should make concerted efforts to release data to the public in machine-consumable form to unlock value hidden within their data assets.18Big Data in Malaysia: Emerging Sector ProfleOpendatafromgovernmentsourcesisneededtosupport theBigDataecosystem.Twooutoffiverespondentssaid thattheyeitherAgreeorStronglyagreethathavingaccess tosomegovernmentdatadoesorwillcreatevaluableBigData opportunitiesforthem.Justunderhalfofrespondentswere neutral,while1in10respondentssaidtheyeitherDisagreeor Stronglydisagree.Thelargenumberofneutralrespondents suggests more education is required around how open data can be used for Big Data projects. Respondents identified a number ofusesofgovernmentdata.Topicsthatwerefrequentlycited included: Demographic and socioeconomic data Population (online and offline) Crime; Border security and migration Public and community services (utilities, health, education) Location (by utility); Geographic Information Systems (GIS) Financial; credit WeatherByfarthegreatestneedwasdemographicandsocio-economic data. One respondent, head of decision science at a multinational bank,saidthatInorderforustounderstandtheneedsof Malaysians, statistical data from the population census is important toidentifycorrelationswithinternalbehaviouraldata.Thereis a need for government agencies to share data multilaterally for social problems that have many dimensions. For example, access tohealthcareisalsoafunctionoftransportation,locationand income. Ministries can utilise internal data management for service optimisation. Jin Chuan Tai, Director of ChrysaSys Consulting said that Government has many different sets of survey data, collected from various sources. For instance, Ministry of Health data can be sourced from private and general hospitals, clinics or consultancies. Big Data offers a mechanism to speed up consolidation of all this information, without any processing delays to configure each and every source. Therewasageneralwearinessbyrespondentsofbureaucracy andredtapeinachievingtheirBigDataprojects.Aboutonein five respondents said they believe their local legal and regulatory environmenttobeahindrancetotheirBigDatainitiatives, comparedto15percentwhodidnot.Mostrespondents(47 percent) were neutral. A further 15 percent said dont know clearly identifyingneedforfurthereducationonemergingregulatory trends in the era of Big Data. The most common hindrance cited wasthePersonalDataProtectionAct2010(PDPA),gazettedin Malaysia in late 20138. This was a common response highlighting the need for balancing consumer protection with business needs, andbettereducationspecificallyonthePDPA.Otherissuesof concern to respondents included data compliance, data risk and loss of data.8The hindrance posed by the PDPA on Big Data initiatives may be more in terms of perception, rather than actual legal definition. In the Profiles section of this report, we will show that one industry thought leader believes that the PDPA is actually beneficial, by providing reassurance to the public that their data is protected with a legal framework in place.The PDPA should be frequently reviewed, and revised if necessary, to protect individuals while simultaneously encouraging data projects.19Big Data in Malaysia: Emerging Sector ProflePublic cloud vendors need to identify and address concerns privacy, resource cost, and perceptions specific to Malaysian professionals.Onlyoneinfourrespondentswaswillingto upload internal data to a public cloud service. While 37 percent ofrespondentssaidtheyeitherDisagreeorStronglydisagree withthestatement,Iamwillingtouploadmyinternaldatato third-partyinfrastructure(e.g.publiccloud),justoverathird ofrespondentswereneutral,indicatingthattherearealarge numberoffence-sittersthathaveyettobeswayedeitherway. Respondents who were more likely to invest in Big Data initiatives werealsomuchmorelikelytobewillingtousepubliccloud services, suggesting that maturity around data use and Big Data literacy reduces the anxiety around uploading data to third-party infrastructure.Figure9:Iamwillingtouploadmyinternaldatatothird-party infrastructure (e.g. a public cloud)Source: Big Data Malaysia 0%14%23%37%20%6%50%100%% of respondents n=108AgreeNeutralDisagreeStrongly agreeStrongly disagree20Big Data in Malaysia: Emerging Sector ProfleProflesOverviewThe previous section presented an aggregate view of the state of BigDataactivityinMalaysia.Inthissectionwepresentacloser look, from the perspective of five industry leaders:Heislyc Loh, Founder and Chief Executive Officer, CoRateTom Hogg, Commercial Director, Effective MeasureSiim Saarlo, Chief Technology Officer, STATSITRobin Woo, Senior IT Manager, Western Digital CorporationIan Phoon, Data Scientist, a Malaysian bankOurobjectiveinselectingtheseintervieweeswastostrikea balanceinbackgroundsinendusesandtohighlightdiverse industries. We have representatives from finance, information and communication technology, manufacturing, marketing and social media; across roles in Commercial, Digital Marketing and Product departments. The areas of activity and expanding interest in Big Dataanalyticsweprofiledincludedmarketresearch(e.g.brand monitoring, demographic profiling, and detection of purchasing intent),manufacturing(e.g.rootcauseanalysis),finance(e.g. riskmanagement),andmanyothers(includingideasinweb applications).Someoftheseusecasesarenicheinterestsand specifictocertainindustries,butmayneverthelessbeofhigh value and high impact. The initiatives described by our interviewees point to the existence ofpocketsofmaturityintheMalaysianBigDataecosystem. Sophisticatedtoolsandtechniquessuchastheapplicationof naturallanguageprocessing,machinelearning,fulltextsearch and analysis, statistics, social science, data visualisation, and data governance as well as general data quality measures are being applied towards tasks such as semantic analysis, defect root-cause-analysis,financialearly-warningsystems,andgeneralpredictive analysis solutions. In some situations there is already recognition of the need for specific Big Data return on investment measures. One respondent referred to the conceptual idea of a value to bit ratio,whichattemptstocapturetheobjectiveofincreasingthe valueofdataassets,whilereducingcostsassociatedwithdata collection, retention, and management.The organisations represented here are leveraging a wide variety of data sources, including social media, website traffic and browser interactions, traditional surveys, external third-party media (such as news reports), and others. While in most cases many options for data collection exist, several respondents expressed the view that the Malaysian government should do better in terms of releasing government data, for instance by making available regular census informationthroughpromotiontoorganisationsthatwouldbe benefit from this data.21Big Data in Malaysia: Emerging Sector ProfleBeyondopeningupgovernmentdata,ourrespondentsraised severalotherpointsabouttheprospectsofBigDataanalytics as a significant part of the Malaysian economy. Firstly, the issue oftalentavailabilitywashighlighted.Exacerbatingthisissue, there is a perception that relevant training is relatively lacking in Malaysia(e.g.ascomparedtoSingapore).Untilalocalpoolof skilled specialists can be grown, it may continue to be necessary tooutsourcethehighest-valuetaskstospecialistselsewhere. Therearedifferentopinionsonthepreciserequirementsand priorities for data scientists, but a strong working knowledge of statistics seems to be on everyones wish list. From the Big Data consumption perspective, it is pertinent that the issue of privacy is receiving significant interest in Malaysia. There is a global trend of increasing concern to do with data privacy, and our respondents indicatedthatMalaysianssharetheseconcerns.ThePersonal Data Protection Act 2010 (PDPA), beyond its legal definition, has had an awareness-raising effect.Asforissuesuniquetostartupventures,despiteafavourable viewofBigDatainitiativesbyfundingorganisations,theusual factors impacting all IT startups in Malaysia (in particular access to post-seed-level-funding, and exit opportunities) remain. These issuesaremademoreacutebythelongerrunwayrequiredto achievesufficientscaletoproducesufficientdatasuchthat BigDataanalyticscanbemeaningfullyexploited.Someofour respondentshereshareviewsbasedonsubstantialregionalor global experience as a point of comparison to their successes and challenges in the local landscape. All are dedicated to seeing Big Data succeed in Malaysia.22Big Data in Malaysia: Emerging Sector Profle23Big Data in Malaysia: Emerging Sector ProfleHeislyc Loh, CoRateCoRate is an early stage startup that aims to help people consume informationontheInternetmoreeffectively,byprovidinga smartarticlecollectionplatform-likepocketwithautomatic categorisation,organisationandsummarisationcapabilities. Thesefunctionswillbedevelopedthroughacombinationof crowd sourcing and data analytics. CoRate founder, Heislyc Loh is confident that their vision will be widely accepted by people who needtodealwithlargeamountsofinformation,rangingfrom business analysts and researchers to students. He calls this target group of people, infoholics.End uses and data sourcesGoing beyond social bookmarking, the platform is an ambitious yet classic example of Big Data, with a roadmap to utilise natural language processing, machine learning, and other elements. It is envisioned that the platform will leverage three major data source categories: data and metadata within Internet articles themselves, datacapturedonuserinteractionwiththearticle(e.g.browser extensions will allow users to highlight interesting passages), and interactions and relationships between users (i.e. social data).Were confdent our vision will be widely accepted by people who need to deal with large amounts of information.24Big Data in Malaysia: Emerging Sector ProfleLoh recognises that this vision will be challenging to realise in the bestofcircumstances,butisneverthelessoptimistic.Sincelate 2013 a very early-stage piece of the product has been released in the form of a Chrome web browser extension, but by-and-large developmentworkonlycommencedatthestartof2014,and building-upofateaminKualaLumpurisongoing.Thecurrent teamcomprisesmostlyfront-enddeveloperswithanemphasis onUserExperience(UX)asCoRatetriestoachieveend-user traction. At this early stage their technology stack consist of web app mainstays MongoDB, Ruby-on-Rails and Javascript.Malaysian environment and skills recruitmentLoh believes that funding in Malaysia is not an issue at the early stages,inpartduetotheavailabilityofgrantsfromvarious governmentagencies,andBigDatainitiativesinparticular areviewedfavourably.However,inthemedium-to-long-term CoRatehasitssightsonSiliconValley.Thisisprimarilybecause despite the availability of early stage funding, there is very little high-capitalinvestment(e.g.SeriesBandbeyond)availablein Malaysia.Furthermore,givenCoRatesconsumer-Internetfocus, exit opportunities are relatively thin outside of Silicon Valley.Hegoesontoacknowledgethatbeyondtheinitialphases, realisingCoRatesvisionwillrequirehigh-endspecialisedskills, and finding individuals in Malaysia with relevant experience would prove challenging. Nevertheless, he believes that in the long term thesecapabilitiescanbedevelopedathomebecausethere aresmartpeoplehere.Tojumpstartthedevelopmentprocess he plans to recruit specialists from overseas (primarily from San Fransisco,wherehespentafewweeksinlate2013developing the CoRate concept) with the needed skills.25Big Data in Malaysia: Emerging Sector ProfleTom Hogg, Effective MeasureTomHoggistheCommercialDirectorforEffectiveMeasure inMalaysia.EffectiveMeasureisaleadingproviderofdigital audience, brand and advertising effectiveness measurement and targetingsolutions,bringingbestpracticeonlinemeasurement datatopremiumpublishers,agencies,networks,advertisers andresearchers.EffectiveMeasuresclientbaseinprimarily madeupofpublishers,agenciesandnetworks,andtheyare increasingly working with advertisers and insight agencies. They areheadquarteredinMelbourne,Australiawheretheywere founded and developed their proprietary technology, which now providesonlinemeasurementsolutionsglobally.InSouthEast Asia, Effective Measure operates in Malaysia, Singapore, Thailand, Vietnam, Philippines, Hong Kong and Indonesia. Top advertising clientsglobally,basedondigitalspend,aretypicallyinfinance, automotive,FMCG(foodandbeverage,homecare,healthand beauty),aswellastravelandhospitality.InMalaysia,Effective Measurecoverswebsitetrafficfrom38millionuniquebrowsers each month on its network (including users with multiple devices). They currently offer 70,000 demographic profiles over the online population nearing 20 million people in Malaysia.We need to showcase the power of the local audience... Theres a place for everybody, and room for everyone to operate 26Big Data in Malaysia: Emerging Sector ProfleMeasuring online audiencesIn terms of the Big Data ecosystem in Malaysia, Effective Measure providesdetaileddemographicandpsychographicinsightsinto websiteaudiences,reflectedbyMalaysianinternetusageata censuslevel.Oneoftheircoreobjectivesistohelppublishers andnetworksvalidatewhotheiraudiencesare,sotheycan pitchtoagenciesandadvertisersaboutthekindsofpeopleon theirwebsite.Amainchallengeforlocalpublishersinmarkets likeMalaysiaisthecompetitiveinfluenceofglobaloperations, who have large audience reach. Hogg emphasises that it is very important to help local publishers have a trusted and transparent sourceofdatathatcanbesharedwithagenciesandadvertisers. We need to showcase the power of the local audience, in addition to the audiences visiting these multinational giants. Theres a place for everybody, and room for everyone to operate; the concern for localpublishersisthatmoreandmoreoftheAdEx[advertising spend] is leaving local markets, with the risk that local publishers losetheirrevenues,whichultimatelymeansthatlocalcontentis non-sustainable.Therehavebeenvastchangesinmediameasurementwiththe adventofBigData.Hoggnotesthatonlineenvironmentsoffer easieraccesstomuchlargerdatasets,whereaspreviously advertisingdecisionsweremadeonsmallsubsetsofthe population.Animportantdevelopmentinonlinemeasurement is the ability to compare the impact of advertising on members ofapopulationwhohavebeenexposedtoadigitalcampaign versus members who have not.In post-campaign studies, clients can link impacts back to core objectives such as consumer brand advocacy, uplift in brand awareness, purchase intent and a range of insights based on different business outcomes. Because more advertising spend is going online, validation is required by third-party online measurement providers to justify budgets.Effective Measure presents aggregated, anonymous behavioural databasedongroupsofpeoplethroughadashboardclient service.Hoggobservesthatmostpeoplearentexpertsin statistics, so its really important to make the data easy for people tounderstand.Datavisualisationisincreasinglyimportant inhowclientsconsumedata.Theevolutionofinfographics inBigDatafollowsgeneralconsumptiontrendsformediai.e. interactive, visually stimulating and appealing. In Malaysia, Hogg has observed an increase in requests by clients for key summary points to be pulled out in addition to their dashboard service. Regulatory and Malaysian business environmentHoggsaysthatEffectiveMeasurehasobservedanincreasein sensitivitybyMalaysianrespondentstosometypesofsurvey questions.Hoggbelievesthisispartofaglobaltrendof 27Big Data in Malaysia: Emerging Sector Profleincreasingconsumerconcernwithdataprivacy.Inaddition,the craftingofthePersonalDataProtectionAct(PDPA)inMalaysia hasheightenedawarenessarounddataprivacy;however,Hogg believesthePDPAislikelytobeultimatelybeneficialinhelping peopletounderstandthattheirdataisprotectedwithalegal framework in place. Government can support the Big Data industry inMalaysiabyprovidingregularcensusinformation,insights intoonlinebehaviourandinformationoninfrastructureforthe purposes of corroboration. The more government data that can be accessed, the better for the entire digital community. In terms of comparing the data analytics market in Malaysia versus the rest ofSouthEastAsia,Hoggdoesntseeatremendousdifference, with the exception of Singapore. Hogg observes an equal interest in data analytics in Malaysia as in other markets; however, there is a range of data literacy within Malaysia. Education is required on understanding statistical methodologies in how panels are used toestimatepopulationsizes;thiscanhelpbothpublishersand advertisers understand Big Data methods better.28Big Data in Malaysia: Emerging Sector Profle29Big Data in Malaysia: Emerging Sector ProfleSiim Saarlo, STATSITSiimSaarloistheChiefTechnologyOfficerofSTATSIT,asocial media marketing technology company. STATSIT works with brands andagenciesintheAsiaPacificregiontohelpthemincrease advocacy and sales, to identify influences and to prove the value ofsocialmediaactivities.Ingeneral,STATISITextractsinsights from social media conversations. They define relevant subsets of conversations and conversationalists for customers and projects. Overtheresultingstreamofsocialdata,theyperformanalysis and produce insights in various forms. This includes basic volume monitoring, tailored reports and action steps, influencer profiling and predictions on content demand.PracticesSTATSITreliesondatasciencepracticesandthestatistical capabilitiesofsocialscientists,aswellasmachinelearning algorithms.Newapproachesandalgorithmsgettestedfirston internal and one-off projects before reaching end products. Due totheirnormalsubjectmatter,STATSITutilisenaturallanguage processing(NLP)tosomeextent.However,STATSITavoidmore complexNLPalgorithmsbecausetheseareusuallylanguage There is hardly a profession in IT that would not beneft from some data awareness. 30Big Data in Malaysia: Emerging Sector Proflespecificwhiletheirmainproductscoveravarietyoflanguages. Theirmethodologiesforextractinginsightsfromsocialdata dependonprojectgoals.Theyrelyonbothstandardised quantitativeelementsandsomequalitativeanalysisleading tostandaloneproductse.g.influencersdiscoveryandanalysis. Qualitativeanalysismainlyreliesonanalystworkandisoften iterative, but is supported with different analytical tools, such as full text search, entity and pattern detection etc. InfrastructureSaarlo believes that organisations have an opportunity to optimise their resource usage in terms of infrastructure. He comments that riding the big data wave often makes organisations collect more thantheyanalyse,leavingthemwithalowervaluetobitratio. Recognisingthischallenge,STATISITarelookingforoptionsto change their data collection, processing and usage processes to cut down costs for infrastructure and at the same time increase thevalueofgeneratedinsights.Thisisnotsomuchwiththe aim for cutting cost, but to focus more on value creation before datacollection.Thismightmeanreviewingtheiragreements with infrastructure providers, employing new and more effective technologies, being smart in reducing the amount of active data and focusing analytical efforts etc. In summary, their objective is tobemoreeffectiveandsmartwithbothtechnologyanddata usage.Data environments STATSITworksmostlywithdatathatispublicandavailablefor free.Fromtheperspectiveofaproductdeveloper,anongoing challengeiswhenplatformsrestrictaccesstodataasbusiness modelsmature.Saarlobelievesthatgeneraldataaggregator companieswillgetmoredominantovertime,andthatdata collectionanddataanalysiswillbecomeincreasinglysiloed activities.Saarlopredictsgreaterspecialisationinbigdatawith more companies tackling very specific domain problems in niche fields.Additionally,heseesanincreasingutilisationofopen source components versus approaches that port general machine learning algorithms into completely proprietary solution stacks.Inordertosupportdataintensiveendeavours,Saarlobelieves that governments and organisations should make data available as much as reasonable, keeping in mind an audience of curious andinnovativedevelopers.Hesaysthatasdatascienceisfairly wellpopularisedinprofessionalcircles,studentsshouldalso get taste of the hype, which would naturally increase interest in subjects like maths and statistics. Saarlo says that there is hardly aprofessioninITfieldthatwouldnotbenefitfromsomedata awareness. 31Big Data in Malaysia: Emerging Sector ProfleRobin Woo, Western Digital CorporationWesternDigitalCorporationisoneofthelargestcomputer storagemanufacturersintheworld,andisoneofthemost importantmanufacturersinMalaysia.RobinWoo,SeniorIT ManageratWesternDigitalMalaysia,isresponsibleforleading datacentre operations management and systems administrations atallmanufacturingsitesworldwide.Heisalsoresponsiblefor leadingwebdevelopmentforgeneralITapplicationsglobally, and for leading the companys IT roadmap strategy globally.Scope and contextInWoosview,BigDataandAnalyticsareinseparablebecause ultimately it is what you do with the data that counts. In his view, theend-usesareessentiallyunchangedfromthetraditional goals of analytics, which includes correlation, root-cause analysis, predictive simulation, and triggers to name a few. Nevertheless, therearesomedefiningcharacteristicsthatdistinguishtheBig Data challenge. One must know how to ask the right questions.32Big Data in Malaysia: Emerging Sector ProfleFirstofall,theusecaseshaveevolvedtoincludeunstructured data,thoughstillleveragingstructureddatainincreasingly complex ways. In this way, Big Data can provide greater richness to enhance analytical capabilities. In addition, it can also provide moretimelyresults.Secondly,amajorchangeisthecomputer systemsinvolved;dealingwithaquantumofdataseveral timeslargerthantraditionalsystemshavebeendealingwith necessitatestheuseofsystemsdesignedwithparallelismasa core engineering principle, which is why traditional solutions may notalwaysscaleeffectively.Thekeytosuccess,however,may besomewhatremovedfromcoreengineeringanddatascience algorithms.FromWesternDigitalsexperience,thekeypieceis data governance.Data governanceDatagovernanceisarapidlyevolvingandpoorlydefinedfield. Nevertheless, having a data-driven culture from the outset, top-levelmanagementatWesternDigitalprioritisedthecreation ofinternaldatagovernancestructures.Thiswasinresponseto increasingcomplaintsfromworkerswhowereunabletofind thedatatheyneededtodotheirjobs.Mostoftenthiswasnot because the data was not captured and stored, but because there wasnosystematicapproachtofindingandunderstandingdata assetsscatteredaroundtheglobalorganisation.Somespecific issueswerethelackofastandardnamingconvention,andlack of comprehensive metadata to describe the various attributes of individual pieces of data.Inresponsetothis,adatacouncilwasformed,comprisingkey representatives from business users and IT, which produced several beneficial outcomes, including a maintained data dictionary. Woo believes it was the involvement of end-users in the data council that led to its success. Drawing on the Western Digital experience, Woo argues that the key to success - regardless of the types of datasetsinvolved-istohavedatagovernancestructuresin place. To start, he recommends an organisation may implement adatagovernancestructureforexistingstructureddatasets (conventionallyRDBMSsystems).Thenextstepwouldbeto extendthisdatagovernancestructuretoincludeunstructured data (e.g. log files from equipment, as well as data captured from social networking services).Resourcing and Malaysian environmentAwideskillsetisrequiredforanorganisationtosuccessfully harness Big Data, but Woo suggests that it must start with domain knowledge.Itiscrucialtobewellgroundedwiththevision, goals,andobjectivesofthecorebusiness.Otherwise,allthat datamaysimplyprovetoodistractingtoactuallygetanything relevantdone.Wooobserves,Onemustknowhowtoaskthe 33Big Data in Malaysia: Emerging Sector Profleright questions. In addition to domain and business knowledge, a scientific background is helpful. Big Data experts are few and farbetween.Giventhesteeplearningcurve,thisscenariois unlikely to change very soon, and developing internal capacity is especially difficult. However, a core analytics team is one that is best grown at home, due to the need for a good grasp of evolving businessimperatives.Infrastructureandtechnologyaspects,on the other hand, are best outsourced to proven experts.Ingeneral.WoodoesnotfeelthatMalaysianinitiativesare particularlydisadvantaged,becauseforthemostpart,the challengesaroundBigDataprojectsaffectallcompanies worldwide.However,onespecificfrustrationistherelativelack ofrelevanttraining.WoobelievesthatMalaysiaisrelatively underserved in this regard, compared to other locations such as Singapore.34Big Data in Malaysia: Emerging Sector Profle35Big Data in Malaysia: Emerging Sector ProfleIan Phoon, Malaysian bank IanPhoonworksinriskmanagementataMalaysianbank assessingcreditrisk.Partoftheirdepartmentsroleisassessing loansandsettinglimitsforcommercialandcorporateloans. Phoonhasworkedatseverallocalbankssincegraduatingfrom theUniversityofMalaya.HehasabackgroundinManagement Information Systems including streams in computer science and user experience / usability. His four year degree program included one semester at the University of Melbourne in Australia, and an internship at a financial software business.Data integrity and predictionAthiscurrentemployer,Phoonworkswithacriteriarating system.Hisdepartmentassessestheperformanceofclientsin determining loan limits; their objective is to improve loan quality from a credit perspective. Phoon is responsible for data integrity, helping to analyse, prepare and visualise potential data problems. Thisinvolvespreparingdata,assessingdatainputqualityand providingdesignfeedbackonbackendsystems.Bigdataoffers opportunities for predictive analysis on prospects that are likely There is a clear need for independent benchmarking data in the Malaysian fnancial sector.36Big Data in Malaysia: Emerging Sector Profleto default through data modelling. The most valuable potential of Big Data is in predictive analysis for early warning systems. This involvesusinghistoricalinformationtodeterminepatternsand triggers of company defaults on loans. External news reports and sentiment analysis from the market in assessing the performance of companies can provide valuable predictive data.Tools of the tradeIn his current role, Phoon makes heavy use of traditional statistics tools with a long established track record in the finance domain, suchasSASandEViews.Nevertheless,hebelievesthereis scopefornewandimprovedtoolsandsolutions(e.g.inareas such as data visualisation) and that the need for integration and interoperabilitybetweenthesetoolsisimportant.Despitethe growingprevalenceofanalytics-as-a-serviceandothercloud-based Big Data solutions, cloud services is not likely to be adopted by the traditional banking industry anytime soon, particularly due to perceptions on security.Phoon believes the most important skills for a data scientist are in managing databases and having expertise in statistics. General BusinessIntelligenceskillsarealsoimportant.Hebelievesdata scientistscanhelpbridgethecommunicationgapbetween business and IT functions. As a data scientist, data quality is a key concern in understanding how input integrity can affect prediction models.Hebelievestherewillbeagrowingdemandfordata scientist degrees, but first organisations need to understand the value that data scientists can add.37Big Data in Malaysia: Emerging Sector ProfleIndustry benchmarkingPhoon says there is a clear need for independent benchmarking dataintheMalaysianfinancialsector.Currentregulation preventsinformationsharingbetweenfinancialinstitutions. Commercialbankswouldbenefitfromindustryoverviewssuch asthenumberofloansandcustomersegmentsaswellas guidelinesonbestpractice.Benchmarkingdatacanalsoassist with predictive modelling. While newer business models such as onlinecreditservicesareemerging,Phoonmaintainsthatthe customer-building strengths of traditional banks will give them a competitive edge. InMalaysia,banking,telecommunicationsandmarketingare importantindustrieswhereBigDatacanmakeanimpact,but Phoon sees that adoption of Big Data technologies is not regarded ascriticalyet.Hesuggeststhateducationaroundsuccessesin specificsectorsshouldbesharedmorewidely,evenwithother industries,tohelptherateofbigdataadoption.Campustalks fromBigDatapractitionerswouldalsohelpspreadawareness and drive the growth of talent supply. 38Big Data in Malaysia: Emerging Sector ProfleConclusionInthisreport,wehavesoughttocapturethestateofBigData analyticsinMalaysiainallitsdiversityandvaryinglevelsof maturity. We know there is strong support for Big Data at grass-rootsandexecutivelevels.Butaspirationsandenthusiasmfor BigDataisnotuniformlymatchedwithresourcecommitment. Thereareseveralinhibitorsthatpreventorganisationsin making headway with Big Data projects, including recruiting the appropriate skills, the ideation of novel, high value use cases and access to datasets. Our report raises many more questions. There are varying requirements for the elusive role of the data scientist. There are further questions of how Big Data budgets should be optimally split between infrastructure, data, human resources and other areas. We prompt stakeholders to consider:Are you committing resources aggressively enough? Are you driving a data-centric approach to all organisational processes? Are you considering out-of-the box end uses to give you a competitive edge? Are you prioritising the right blend of skills and capabilities? Are you participating in cultural change for Big Data advocacy?Big Data involves not just technological evolution, but a cultural shiftinthewayweworkandshareknowledge.Thesevalues include an increasing emphasis on transparency and continuous innovation. The advent in Big Data brings rapid change but also emphasisesinterestinclassicaldisciplinessuchasmathematics andstatistics.ThepitfallsintheuseofBigDatashouldbeat theforefrontinconsideringadoption.Statisticianswarnusthat correlationsdonotequalcausation,andthat;theavailabilityof increasing variables does not guarantee greater clarity. The privacy rights of individuals will also constrain how organisations collect, manage and distribute data. However, we believe the productive valueofBigDataanalyticsinsocietiesoutweighsdrawbacksif these concerns are respected and managed carefully. We have no doubt that the Big Data analytics industry will continue to mature and evolve in unexpected ways in Malaysia that will support the nations competitiveness in the global economy. 39Big Data in Malaysia: Emerging Sector ProfleAbout the authorsSandra HanchardSandra is the Organising chair of Big DataMalaysia.Sheisanindustry analystwithexpertiseinconsumer adoptionofnewtechnologies. Formerly, Sandra was the Asia Pacific lead for custom research with global Internetmeasurementfirm,Experian Hitwise.Sheworkedwithleading blue-chipandnewmediabrands togrowtheirbusinessesthrough insightsderivedfromBigData.Her commentaryhasfeaturedprominentlyinthepressinAustralia. Currently,SandraistherecipientofanAustralianPostgraduate Awardforherdoctoralthesiswhichinvestigatessocialmedia informationuseinMalaysia.Sheisapastspeakeratthe MelbourneWritersFestivalandafellowoftheOxfordInternet Institute Summer Doctoral Program at Oxford University.sandra@bigdatamalaysia.orgTirath Ramdas Tirath founded Big Data Malaysia while workingwithAcunu,aLondon-based Big Data startup. Though that was his firstrolethatexplicitlyworethelabel BigData,hisfirstexposuretolarge scale information processing occurred manyyearsearlierduringhisPhDin computer architecture for applications incomputationalquantumchemistry. Otherpriorrolesincludesystems administration,embeddedsystems development for VoIP and video applications, non-profit strategy consulting, as well as business intelligence and data warehousing conceptualapplicationdesign.Currentlybasedprimarilyin Melbourne, Australia, he is a software development consultant to information security startup Bromium. tirath@bigdatamalaysia.org40Big Data in Malaysia: Emerging Sector ProfleAbout Big Data MalaysiaThe first Big Data London meetup was held in a co-working space nearLondonsfamousLeicesterSquareontheeveningofMay 25th2011,featuringtalksonBigDatausecasesininteractive TV, games, and business intelligence. Founded by London-based startupAcunu,thegroupwascreatedanddevelopedtofoster awarenessandunderstandingofthepotentialforBigDatato transformbusinesses,industries,andsocietyatlargewhile always digging into the technology itself.Oneyearlater,thefirstBigDataMalaysiameetupwashelpin the Mercury room at Telekom Malaysia Research & Development in Cyberjaya on the afternoon of May 16th 2012, featuring talks on R, applications in genetics and air traffic modelling, as well as marketingservices.AlsofoundedbyAcunu,BigDataMalaysia shared much of Big Data Londons DNA - in particular core values such as:1.Maintaining a broad appeal (business executives in suits as welcome as hackers in shorts).2.Embracing the wide spectrum of issues to do with Big Data (e.g. distributed systems and data journalism are equally relevant).3.Emphasis on interesting content (more than just a networking forum).Over time Big Data Malaysia has evolved in its own way to address needs and opportunities in Malaysia, embarking on exercises such aspartneringwithrelevantorganisations(suchascommunity/professionalgroupsandcommercialconferenceorganisers), conductingagroupbrainstormingworkshoptodiscussthe emergingimpactofBigDataonsocietyatlarge,coordinating BigDataWeek2013,andproducingtheBigDatainMalaysia: Emerging Sector Profile survey. AlthoughBigDataMalaysiahasevolvedtoconductactivities beyondmeetups,runningregularmeetupsisacoreaspectof the group, to allow people to network and learn. To that end in ourfirst24monthswehaverunmanymeetupsaslistedhere: HelloWorld:May2012,sponsoredbyAcunu,hostedby Telekom Malaysia R&D, Cyberjaya, with talks by:Julian Lee, Revolution AnalyticsDaniel Walters, Experian Marketing ServicesHakim Albasrawy, TandemicBigger,Faster,Messier:July2012,sponsoredbyAcunu, hosted by iTrain, Kuala Lumpur, with talks by:Nicolas Yip, InnityCalumHalcrowandKavehMousaviZamani,RMG Technology41Big Data in Malaysia: Emerging Sector Profle JointmeetupwithSNIAMalaysia:September2012,sponsored by SNIA Malaysia and Acunu, hosted in Cyberjaya,with talks by: SNIA tech tutorial on flash storage Rachelle Foong, HP Tirath Ramdas, Acunu BigDatainTelecommunications:October2012,organised and hosted by TM R&D, Cyberjaya with talks by: Zach Tan, Google Raymond Au, Oracle Shamsul Anuar Yahaya, Telekom Malaysia Big Data in Social Media Analytics: December 2012,hosted by iTrain, Kuala Lumpur, with talks by: Sandra Hanchard, Swinburne Institute for Social Research VelvetUnderground,30thJanuary2013,sponsoredbyAcunu and Experian, a networking event coinciding with theMarketResearchintheMobileWorld(MRMW)conference,Zouk, Kuala Lumpur. Big Data in Market Research and Beyond, 1st February2013, sponsored by Acunu and Experian, coinciding with theMarketResearchintheMobileWorld(MRMW)conference,hosted by iTrain, Kuala Lumpur, with talks by: Colin Pal and Taufek Johar, Experian Marketing Services Tirath Ramdas, Acunu Shazri Shahrir, Telekom Malaysia R&D, and Nurazam Malim,Twistcode Rebekah Lee, Integricity Interactive Attendeesalsoparticipatedinagroupbrainstormingexercise, discussing how Big Data will impact society fromthe perspective of small business, big business, government, and individuals/consumers. MalaysiawaspartofBigDataWeek2013inmid-April2013.BigDataMalaysiaassumedanoversightroleduringthis festival, working to encourage a balanced program as wellas fostering cross-promotion and minimising event overlaps.All together 9 separate events were part of the program, eachof which was organised by different partner organisations: TRANSIT: Distillation Python Malaysia: Big Data Week Workshop Big Data in Telecommunications 2 Big Data: Strategy, Practice, and Research SNIA Malaysia Tech@Break NoSQLAsia:ExploringTheTechnologyBehindBigDataWeek GPU Technology Workshop Asia 2013 The Data Revolution: Powered by the AWS Cloud DataJournalism:StorytellinginMalaysiawithBigData42Big Data in Malaysia: Emerging Sector Profle BigDataMalaysia-Anniversary1.0:30thMay2013,hostedbyiTrain,KualaLumpur,EmceedbyDanielWalters,Experian, with talks by: Khoo Swee Chuan, Netapp SiahHarkCheongandMunHeng,fromamultinationalfinancial services company, but speaking in their personalcapacity BigDataMalaysia,withF-Secure:1stAugust2013,sponsored and hosted by F-Secure, Kuala Lumpur, emceed byNg Swee Meng, OnApp, with talks by: Keng Chee Chan, F-Secure Aizu Ikmal Ahmad, Malaysiakini ZakiullahKhanMohammed,independentsoftwareengineering management consultant BigDatainTelecommunications3:24thSeptember2013, sponsored and hosted by TM R&D, Cyberjaya, emceedby Tirath Ramdas, Marklar Marklar Consulting, with talks by: Mohamad Ansahari Bin Abdul Kudus, Telekom Malaysia Szilard Barany, Teradata RosmawatiBintiAbRaub,TelekomMalaysiaResearch&Development Cents&Security:20thFebruary2014,sponsoredandhosted by F-Secure, Bangsar South, emceed by Szilard Barany,Teradata, with talks by: Foong Chee Mun, MoneyLion Goh Su Gim, F-Secure Pak Mei Yuet, MDeCThefirsttwoyearsofBigDataMalaysiahaveuncovereda significantandstillrapidlygrowinginterestandexcitementfor the possibilities of Big Data in Malaysia, and we look forward to seeingwhatcomesnext.Webelievethatayearfromnow,we shall look back to see that this was the turning point where the majority of discussion changes from this is what we could do to this is what were doing. BigDataMalaysiaexistsonanumberofsocialmediasites;our Facebook group is especially active, and we also have a LinkedIn group,aMeetup.comgroup,andamailinglist.Linkstoall thesepropertiesmaybefoundonourlandingpageat http://bigdatamalaysia.org43Big Data in Malaysia: Emerging Sector Profle