A research roadmap to advance data collaboratives practice as a novel research direction

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A research roadmap to advance data collaboratives practice as a novel research direction

Susha, Iryna; Pardo, Theresa A.; Janssen, Marijn; Adler, Natalia; Verhulst, Stefaan G.; Harbour, Todd DOI

10.4018/IJEGR.2018070101

Publication date 2018

Document Version Final published version Published in

International Journal of Electronic Government Research

Citation (APA)

Susha, I., Pardo, T. A., Janssen, M., Adler, N., Verhulst, S. G., & Harbour, T. (2018). A research roadmap to advance data collaboratives practice as a novel research direction. International Journal of Electronic Government Research, 14(3), 1-11. https://doi.org/10.4018/IJEGR.2018070101

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DOI: 10.4018/IJEGR.2018070101

A Research Roadmap to Advance

Data Collaboratives Practice as

a Novel Research Direction

Iryna Susha, Örebro University, Örebro, Sweden

Theresa A. Pardo, CTG, University at Albany, SUNY, USA

Marijn Janssen, Delft University of Technology, Delft, The Netherlands https://orcid.org/0000-0001-6211-8790

Natalia Adler, UNICEF, USA

Stefaan G. Verhulst, The Governance Lab, New York University, USA Todd Harbour, New York State, USA

ABSTRACT Anincreasingnumberofinitiativeshaveemergedaroundtheworldtohelpfacilitatedatasharing andcollaborationstoleveragedifferentsourcesofdatatoaddresssocietalproblems.Theyare called“datacollaboratives”.Datacollaborativesareseenasanovelwaytomatchreallifeproblems withrelevantexpertiseanddatafromacrossthesectors.Despiteitssignificanceandgrowing experimentationbypractitioners,therehasbeenlimitedresearchinthisfield.Inthisarticle,the authorsreportontheoutcomesofapaneldiscussingcriticalissuesfacingdatacollaborativesand developaresearchanddevelopmentagenda.Thepanelincludedparticipantsfromthegovernment, academics,andpractitionersandwasheldinJune2017duringthe18th_{InternationalConferenceon} DigitalGovernmentResearchatCityUniversityofNewYork(StatenIsland,NewYork,USA).The articlebeginsbydiscussingtheconceptofdatacollaboratives.Thentheauthorsformulateresearch questionsandtopicsfortheresearchroadmapbasedonthepaneldiscussions.Theresearchroadmap posesquestionsacrossninedifferenttopics:conceptualizingdatacollaboratives,valueofdata, matchingdatatoproblems,impactanalysis,incentives,capabilities,governance,datamanagement, andinteroperability.Finally,theauthorsdiscusshowdigitalgovernmentresearchcancontributeto answeringsomeoftheidentifiedresearchquestions. KEywoRDS

Data Collaborative, Data Philanthropy, Data Sharing, Digital Government, Evidence Based Policy, Public Private Partnership

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1. INTRoDUCTIoN Theworld’smostcomplexand‘wicked’socioeconomicproblems-fromclimatechangetothe spreadofepidemics-cannotbetackledbyasingleauthorityinthepublicsectoralone.Ittakesa villagetounderstandandunpackthesecomplexproblemsinordertomakethemunderstandableand actionable.Datacanhelp,especiallyconsideringitsexponentialgrowthinthepastfewyears.Access tonewdatasetsandexpertisecanhelpprovideamoreaccurateandcomprehensivepictureofthe situationandempowerdecision-makerstotailortheirinterventionsaccordingly.However,muchof themostrelevantdatathatcanhelpuntanglethesecomplexproblems(andtheexpertiserequiredto makesenseofthatdata)oftenresidewithcorporationsintheformofwebclicks,likebuttons,cell phonedata,satellitedata,etc.Asaresult,anincreasingnumberofinitiativeshaveemergedaround theworldtohelpfacilitatedatasharingandcollaborationstoleveragedifferentsourcesofdatato addresssocietalproblems.Theyarecalleddata collaboratives(VerhulstandSangokoya,2015)and canbedefinedas“cross-sector(andpublic/private)collaborationinitiativesaimedatdatacollection, sharing,orprocessingforthepurposeofaddressingasocietalchallenge”(Susha,Janssen,&Verhulst, 2017,p.2691). Datacollaborativesisanovelresearchtopicwhichexpandsthehorizonsofnewapproaches oftacklingpublicproblemsandimprovingpublicservicesthroughdatascienceandcollaborative solutions.Thereisgrowingexperimentationwithdatacollaboratives(asmappedbyTheGovLabat datacollaboratives.org),howeverthereisalackofcommonterminologyandsharedunderstanding ofthenoveltyandcomplexityofthisnewphenomenon.Therefore,wehaveconvenedapanelthat broughttogetherresearchersandpractitionersworkingondatacollaborativestoshowcasetheirresults anddiscusscriticalissueswiththedigitalgovernmentcommunity.Thepanelwasheldduringthe 18th_{InternationalConferenceonDigitalGovernmentResearchon8June2017atCityUniversity} ofNewYork(StatenIsland,NewYork,USA).Itfeaturedfivespeakers:agovernmentofficial,two practitioners,andtwoacademics(allco-authorsofthispaper).Thepanelwasmoderatedbythefirst authorofthepaper.Inthispaper,wereportontheoutcomesofthispanelwiththeaimofidentifying aresearchagendatohelpbuildbridgesbetweenresearchandpracticeinthisarea.

2. DATA CoLLABoRATIVES AS A NoVEL RESEARCH DIRECTIoN

Digitalgovernmentfosterstheuseofinformationandtechnologytosupportandimprovepublic policies and government operations, engage citizens, and provide comprehensive and timely governmentservices.Theglobaldigitalgovernmentresearchcommunityisinterestedinthe developmentandimpactofdigitalgovernment.Thecommunityexistsattheintersectionofcomputer andinformationscience,socialandbehavioralscience,andfocusesontheneedsandproblemsof government.Buildingnewknowledgeaboutdatacollaborativesasacomplexphenomenonisinline withtheinterestsofthiscommunity. Therehasbeenalotofworkdoneinthefieldofdatasharinginthedigitalgovernmentcommunity (e.g.Landsbergen&Wolken,2002;Gil-Garcia&Pardo,2005)andonlyrecentlythefocushas shiftedtowardstheinclusionofprivateparties(Bharosa,etal,2013).Thesameistrueforwork relatedtoinformationsharingandcollaborationamonggovernmentorganizations.Tung-Mouand Maxwell(2010,p.73)reviewedliteratureforfactorsaffectinginformationsharing,whichinclude promotionofacultureofinformationstewardshipasopposedtoownership;strongleadershipsupport toinformationsharingefforts;legislativeandregulatorymandates;rewardsystemsthatpromote informationsharingbothwithinandacrossorganizations;theestablishmentofsharedgoals;andthe developmentofongoingtrustedrelationshipsbasedonmutualunderstandingofneedsandconcerns andsharedresponsibility.Gil-Garcia,Chun,andJanssen(2009)discussedthechallengestogovernment informationsharingandintegrationandgroupedthemintechnical,organizational,political,andlegal categories.Datacollaborativesintroducenewcomplexitiestothesecollaborativeengagements,with

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theprivatesectorplayingabiggerroleandtheneedfornewstructures,procedures,processes,and practicestoshapenewwaysofworking. Datacollaborativescancreatevalueinfivedifferentways:improvingsituationalawarenessand response,improvingpublicservicedesignanddelivery,enablingforecastingandpredictionbasedon thedata,supportingevaluationandimpactassessmentofpolicies,aswellascontributingtoknowledge creationandtransferbetweenthesectors(VerhulstandYoung,2017).Inadatacollaborative, ‘doinggood’forthesocietybycontributingtosolvingsocietalproblemsisthecentralgoal,and providingdatasetsandexpertiseshouldhelptorealizethisgoal.Akeyelementtoaccomplishthis isthecollaborationandinteractionbetweenpeople,organizations,andcommunities.Apartfrom thecontributionbyorganizations,thequalityoftheresultsisakeyaspectasthereputationofboth privateandpublicsectororganizationsshouldnotbeharmed.Thisbringsustothefollowingmain featuresandprinciplesunderlyingdatacollaboratives: 1. Definingasocietalproblemandconsideringhowdatacancontributetoit; 2. Theneedtocollect,shareorexchangedataamongdiverseinformationsystems; 3. Processingcapabilitiesbyhavingsomekindofsharedinfrastructure; 4. Theneedformobilizingexpertisethatareoftennotavailablebyasingleorganization; 5. Dominationbyprivateorganizationsinwhichgovernmentsmightplayamarginalrole; 6. Qualitycontrolsforensuringcorrectandaccurateresultsofdataanalysis; 7. Sharingtheresultsinabroadercommunityandstimulatingnextsteps. Animportanttensionindatacollaborativesisthatthereisalargedependencybetweenthe resourcesandcapabilitiesofdifferentparties,moreoverpartiesmighthavedifferentinterestsand incentivesforcollaboration.Thesolvingofasocietalproblemisabindingfactor,andacollaboration mightworkonlyonceforacertainpurposeandnotforotherpurposes.Collaborationmightbe furthercomplicatedduetoheterogeneousdatasetsandsystemsandvariouslevelsofreadiness.Also, thecostmightnotbeevenlysharedandmightbehardtoaccess.Theinterdependencecreatesnew complexitiesanduncertainties. Datacollaborativesencompassdifferentkindsoftechnologies,suchasbigdata,advanced analytics,visualizationsandrequireachangeofrolesandnewcapabilitiesfromgovernment(Janssen &Helbig,2016).Publicsectororganizationsmightonlybeoneofthemanyplayersandhaveno hierarchicalorlegislativecontrolovertheotherorganizations.Yet,theymightbethemostinterested intheoutcomesoftheanalysisandmostequippedtotakeactionsonthebasisoftheanalysis.Atthe sametime,theyshouldtrustthedataandtheoutcomesandalsobeinvolvedtoensuretheacceptance oftheoutcomesandthetakingoffurthersteps.

3. A REAL-LIFE CASE oF DATA CoLLABoRATIVE

Thepanelhighlightedmanydifferentsocietalchallengeswhichhavethepotentialtobeaddressed throughdatacollaboratives:disasterresponse,childhoodobesity,urbanmobility,genderissues, environmentalmonitoringetc.ThepresentationsfromUNICEFandTheGovLabshowcasedconcrete examplesofappliedcollaborativesolutionstoreal-lifeissuesthankstodatacollaboratives. Oneoftheexampleshighlightedwasthegrowingnumberofsuicidesamongyoungpeoplein India.Theproblemwasselectedfortworeasons.First,becausesuicideisacomplexphenomenonthat cannotbetackledbyoneorganizationalone,hencetheneedforcollaboration.Second,currentdata collectionmethodsareinsufficienttounderstandthemagnitudeanddriversoftheproblem.Suicide attemptsandsuicidalideationaregrosslyunderreportedbytheIndianNationalCrimeRecordsBureau anddriverssuchascyber-bullying,peerpressure,examinationfailure,forcedmarriage,conflictover dowries,andindebtednessareonlyidentifiedandreportedinindividualinstancesafterthefact. Accordingtoareportbasedonverbalautopsy,suicideisthesecondleadingcauseofdeathamong

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peopleaged15-24inIndia,accountingforabout60,000deathsin2013.Therefore,therewasroom forexperimentingwithnoveldataapproaches. UNICEFHQandTheGovLabworkedwiththeUNICEFIndiaCountryOfficetowardlaunching adatacollaborativetobetterunderstandthisgrowingphenomenonandunlockaccesstomore informationaboutadolescentsandyouthwithmentalhealthissuesandotherindicatorsofsuicidal tendencies.ThegoalofthedatacollaborativewastogainuniqueinsightthatcanhelpUNICEF’s effortstospreadawarenessaboutmentalhealthcareandrelatedadvocacyaimedatsuicideprevention. Withbuy-infromtheUNICEFIndiaCountryOffice,theteamsinNewYorkproceededtoscope existingliteratureandframeworkstodevelopaproblemdefinitionandsubsequentdataaudittoshed lighttoemergingtrendsofsuicideinIndia. Datacollaborativesshouldbeproblem-drivenasopposedtodatasupplydriven.Itbuildsonwell-establishedapproachesthatlookatproblemsasspringboardtoaddresscomplexproblems(Cameron, 1986;Oliver,1992;SeoandCreed,2002).TakingapagefromaProblemDrivenIterativeAdaptation (PDIA)framework(Andrewsetal,2015),thedatacollaborativesmethodologydevelopedbyThe GovLabfocusesonlocallynominatedproblems,expandingtheauthorizingenvironmenttoinclude notonlythepublicsector,butseveralcollaboratorsprovidingdataandexpertise.Toattractthese collaborations,thedevelopmentofaproblem-drivencasestudyonsuicidetrendsinIndiaservesa twofoldgoal.First,ithelpsbreakdownacomplexsocialproblemintomoremanageablepartsthat canbeeasilyunderstoodbyamultitudeofpartners,includingthoseintheprivatesector.Second,it makesacompellingcaseforactionthathelpsmobilizedata-drivencollaborativesolutionstotackle this‘wicked’problem. Oncetheproblemwasdefined,UNICEFHQandTheGovLabreachedouttodatascientiststo brainstormtechnicallyfeasibledatasciencesolutionsfortheunderreportingofsuicidetrendsinIndia. ResearchersfromtheInstituteofScientificInterchangeFoundation(ISIFoundation),basedinTurin, Italy,agreedtojointheeffortsthroughmultiplebrainstormingmeetings.Severaldataapproachesand datasourceswereidentified,mostlyrelatedtoonlinebrowsingbehaviorandotherinternetactivity usingsocialmedia,chatroommessages,andinternetsearchqueries,allofwhichcouldhelpshed lighttousers’suicidalthoughts/ideation. Followingthedata-audit,thenextstepwastoreachouttocorporationsthathadtheavailabledata inhand.ResearchersfromMicrosoftResearchCenter,basedinIsrael,joinedthedatacollaborative providingadditionalexpertiseanddatabasedonanonymizedandaggregatedqueriessubmittedto theBingsearchengineandYahooQuestionsbyusersinIndia. Theongoingresearchseekstodocumentthevalueofdatacollaborativebyfocusingonhowto complementexistingofficialbaselinesthattendtounderreportsuicidebylookingatthecorrelation betweenthenumberofsearchqueriesofpredefinedkeywordsrelatedtosuicideandthenumberof officialsuicidesrecordsperstate.SubjectMatterExpertsonsuicideinIndia(mostlyfromNGOsso far)areperiodicallyinvitedtoprovidefeedbacktotheseresearchfindingsandgovernmentofficials willbeinvolvedastheworkevolves.TheresearchisexpectedtobefinalizedbeforeDecember2017, afterwhichdecision-makersinIndiawillbecontactedtoworkonimplementation.

4. RESEARCH RoADMAP FoR DATA CoLLABoRATIVES

Belowwepresentthecriticalissuesandquestionswhichwereidentifiedbasedonthepaneldiscussions. Wegroupedthemintonine topicswhichareexplainedinmoredetailbelow.

4.1. Conceptualizing Data Collaboratives

Adigitalgovernmentresearchagendafocusedondatacollaborativesshouldpursuequestionsabout datacollaborativesthemselves:whatisadatacollaborative,whatformsdotheytake,andhowarethey differentfromongoingpractice?Newexplanatoryanddescriptiveresearchisneededtounderstand thisphenomenonmorefully.Weneedtounderstandwhatisnewaboutdatacollaboratives,what

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setsthemapartfromotherpublicsectorandprivatesectordatasharingandintegrationactivities andpublicprivatepartnerships,howandinwhatwaytheyvarybypolicydomain,bypartners,by region,andwhatistheroleofgovernment.Thisalsoinvolvesexaminingwhatnewquestionscanbe answeredandwhatnewmethodscanbeusedindatacollaboratives.Newunderstandingoftherole ofvariousactorsintheformationandoperationofadatacollaborativeisrequired;theideaisnot wherethedatasits–butwhoispartoftheconversation. 4.2. Value of Data Adigitalgovernmentresearchagendafocusedondatacollaborativesmustexaminequestionsabout thevalueofdataitselfasanindividual,organizational,andsocietalasset.Further,questionsabout whetherthegrowingrecognitionofthevalueofdatatohelpsolvesocietalgrandchallengesisenabling orconstrainingdatacollaborativeformation,resiliency,andsustainabilityareimportant.Forexample, willgrowingrecognitionofthepotentialtomonetizeprivatedataconstraindatacollaborativesornot? 4.3. Matching Problems to Data to Partners

Aresearchagendashouldpursuequestionsabouthowdatacollaborativesareformedaroundthe identificationofsharedinterestinaproblemandthematchingofproblems(demand)tothosewith dataandexpertise(supply).InthecaseofthedatacollaborativeinIndia,datascientistsanddata-drivencompanieswerecontactedviapersonalconnectionsofUNICEFHQandTheGovLab.How doesonemovebeyondtheseinterpersonalconnectionstosetupadatacollaborative? Evenwhenthecontactwithpotentialdata-holdersandexpertsisestablished,howdoesthe negotiationtakeplace?Sayingtopotentialpartners“weneedyourdata”isnotenoughtogetit, especiallyconsideringhowthisassetisincreasinglybecomingmoreandmoremonetizedandis beingreferredtoas“theworld’smostvaluableresource”(TheEconomist,2017).Aswehaveseen, groundingtheneedfordataonacompellingproblem-drivencasestudieshelpedUNICEFandThe GovLabmobilizeandconvincepartnerstosharetheirdataandexpertiseprobono.Buthowcanthisbe replicatedatscale?Oneofthebiggestobstaclesforengagingtheprivatesectorindatacollaboratives isawidespreadlackofcapacityamongthedemandsidetobreakdowncomplexproblemsinto smaller,betterdefinedandmoreactionablechallengesthatcanbeeasilyunderstoodbycorporations willingtosharedataandexpertise.Newtoolsandtechniquestounderstandallthevariousunderlying aspectsofthiscomplexityanduncertaintyofrelevantproblemsmaybeneeded,aswellasnewtools forconductinganalysesofcostandriskforsharingandfornotsharing. Further,questionsaboutthenatureofsuchpartnerships,forexample,howtofindthebalance betweensharingmoredatawithmorepeoplewithsharingsomedatawithsomepeoplethatisless open,moretargeted,andcreatedhighvalue,mustbepursued.Toolsandtechniquestomodelproblems andsystemsareneededsothatnewunderstandingofhowandwherenewkindsofdatacanhelpsolve thoseproblemsisneeded.Inotherwords,weneedtofindwaystoquicklyidentifywhatdatasources arerelevanttothereal-lifeproblems–howtofindthem,testtheirrelevanceandutility,andthen makethedatafullyavailable.Finally,howcannewunderstandingofproblems,matchingproblems todata,andsecuringpartnerswhoarecommittedtotheprinciplesofadatacollaborativehappen quicklysothattheresultingcapabilitycancontributetocommunitiesincrisis? 4.4. Impact Analysis Adigitalgovernmentresearchagendafocusedondatacollaborativesshouldpursuequestionsabout theimpactofdatacollaboratives.Asanexample,inthecaseofthedatacollaborativeinIndia,the partnersfacedthechallengeofhowtotranslatetheinsightsgeneratedintoactionablerecommendations. Howcanwedetermineimpactfromdatacollaboratives?Whatkindofpublicvalueisbeingorcan becreatedthroughdatacollaboratives?Whatrolecancorporatedataplayinhelpinggovernments meettheirmission?

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4.5. Incentives Fromthepractitioners’pointofview,therearecertainincentivesatplayforcorporationstosharedata, suchascorporatesocialresponsibilityandthechancetobenefitfromexternaldatascienceexpertise (VerhulstandYoung,2017).However,suchincentivesoftenconflictwiththeurgencyandgoalsof thegovernmentagenciesorresearchorganizationsrequestingthedata.Accessingcorporatedataon ashortnotice,forexamplewhenadisasterstrikes,isdifficultandmayincurhighcosts.Moreover,it isnotalwaysobviouswhohastherelevantdataforaparticularproblemandhowtoapproachthese data-holdersoutsideinterpersonalnetworks.Dataisoftenguardedandover-protectedbydata-owners dueto,amongotherreasons,keepingtheorganization’scompetitiveadvantage. Variousstakeholdersneedtoworktogetherandtheyneedincentivesfordoingthis.New knowledgeisneededaboutwhydifferentactorswouldsharedata.Whatincentivestructuresdrive datacollaboratives?Arenewincentivesrequired?Howdodifferentstructuresincentivizedifferent partners?Howbesttoincentivizedatacollaborativeformation,whatconditionswillcontributeto valuecreationthroughadatacollaborativemodel,andhowdatacollaborativescanbesustainedover timeratherthanformedasaconsequenceofacrisis?Theseareallrelatedquestions.Forexample, inthecaseofthedatacollaborativeinIndia,oneofthechallengingquestionswashowtomakethe engagementsustainable,on-goingandgeneralizableandhowtoscaleuptheeffortandgobeyondthe progressmade.Besides,howtofacilitateaproductivedialoguebetweendifferentcommunities(such ascorporatedatascientistsandsocialworkers)withdifferentculturalbackgroundsandincentives? 4.6. Capabilities Adigitalgovernmentresearchagendafocusedondatacollaborativesshouldbuildnewunderstanding ofthecapabilitiesrequiredtoidentifytheneedforadatacollaborative,tolaunchthatcollaborative, anduseittocreatepublicvaluethatcanbesustainableovertime.Theagendashouldincludequestions aboutcapabilitiesrequiredtoensuredataisreadywhenneeded,actorshavingtheanalyticsskills necessarytoleveragetheavailabilityofnewdatainsupportofasocietalchallenge,aboutcapabilities neededtoaddressinformationpolicyissuesandtechnicalinfrastructurerequirements.Additional questionsinclude:Whatcapabilitiesarerequiredinwhatcontexts?Whatroledoestrustplayand howdoesthecapabilitytobuildtrustinfluencedatacollaborativevaluecreation?Howdoesthe collaborativemodelcreatenewcapabilitymodels?Isitpossibletocreateclarityaroundrolesand responsibilitiesinbuildingthedatacollaborativeandcreatingvalue?Aretheredifferentcapabilities neededtocreateanincidentfocuseddatacollaborativeversusasustainabledatacollaborativeora problem-versussystemiccapacityfocusedone? 4.7. Governance Fordatacollaborativesgovernanceisaboutthedistributionofdecision-makingauthoritiestoensure theproperworkingofacollaboration.Governancecanbeviewedasthedefiningandallocatingof actionsanddecisionsthatensureaformofcollaborationthatcannotbeexternallyimposed(Stoker, 1998).Adigitalgovernmentresearchagendafocusedondatacollaborativesmustexaminetheways inwhichgovernanceplaysaroleinbuildingandsustainingdatacollaboratives.Inparticular,what datagovernancemodelsworkfordatacollaborativesindifferentcontexts?Whatdatagovernance modelsarebestfortheorganizationswhoarecontributorstodatacollaboratives–thesupplyside; howdoesthedemandsideinfluencedatagovernanceinsupplyorganizations?Existingresearch highlightstheimportanceofestablishingrules,principles,andstandardsandputtinginplacethe elementsofresponsibledatagovernance.Whatarethoserules,principlesandstandards?Whatare theelementsofresponsibledatagovernancevisavisadatacollaborative?Issuesofdataownership shouldalsoberesearched,especiallywiththisassetcontainingvitalinformationthatcanhelppublic interest(i.e.asinthecaseofepidemics).Towhatextentcandatasharingchangefromvoluntaryto obligatory?Questionsaboutdatagovernanceasanenablertodatacollaborativesmustbeexamined,

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inparticulartheimpactofdatagovernanceingettingdatareadyandfitforusewithintheoftencrisis conditionsaroundwhichdatacollaborativesemerge. 4.8. Data Management Adigitalgovernmentresearchagendafocusedondatacollaborativesmustexploretheroleofdata managementandhowandtowhatextentrelatedcapabilitiesandtheuseofbestpracticesenable datacollaboratives.Whoownswhichdata?Whatisthequalityofthesedata?Forinstance,inthe caseofthedatacollaborativeinIndia,thepartnersfacedachallengeofhowtoassurethedatais representative.Besides,newunderstandingabouthowtocreatestructureddataoutofunstructured dataandmakethatnewlystructureddataavailableasanassetwithinthedatacollaborativeisneeded. Organizationshaveyettofullyrealizethepotentialthattheirdataholds.Despiteampleevidence tothecontrary,dataoftenremainssiloed,undiscoverable,andinaccessiblebytheenterprise.For organizationstobeginusingdataasacriticaldecision-makingasset,theorganizationmustfirst understandthatitneedstotreatdataasanasset,somethingthatisinherentlyvaluableandneedstobe managedinadeliberateandexplicitmanner.Itisimportanttonotethatorganizationsshouldnotwait untilthingsaresettled,stableorfinishedbeforestartingtomanagetheirdata.Forthoseorganizations thatdonotdevelopdatastrategiesandlearntomanagetheirdata,theywillpainfullylearntherewill alwaysbeagoodreasonfornotmakingchangestotheirorganizationanditsbehavior.Thislackof organizationalfortitude,commitmentandactionwillhavedetrimentaleffectsontheorganizationand itsabilitytocontinuemeetingthedemandsofanever-evolving,competitiveenvironment. Fundamentally,organizationsneedtodevelopanintegrativedisciplineforstructuring,describing andgoverninginformationdataassets,regardlessoforganizationalandtechnologicalboundaries, andtoimproveoperationalefficiency,promotetransparencyandenablebusinessinsight.Nomatter howanorganizationacquiresdata—throughsharingrelationshipswithgovernmentorindustry(data collaboratives),commercialacquisition,orcreatedusingorganizationalresources—datagovernance isthefoundationalfunctiontohelpensurethattheequitiesofallpartiesareprotected. 4.9. Interoperability Interoperabilityistheabilityofdiversesystemstocollaboratewitheachother(Scholl&Klischewski, 2007).Interoperabilityisnotonlyatechnicalissue,italsoappliestoorganizationalaspects(Goldkuhl, 2009).Dataisstoredindifferentformatsandinheterogeneoussystemswhichmighthinderitseasy use.Mucheffortsmightberequiredtoderivedatafromthesystemsandtolinkdataandtoanalyze thedata. Recentadvancesindatatechnologieshaveresultedinsignificantlyimprovedwaysofinteracting withorganizationaldataassets—despitethevaguenessofthebigdatapromise.Inotherwords,big datatechnologiesandindustryspecificdatascientistscanprovideorganizationssomeoftheresources requiredtodomorewithdata,butorganizationsstillneedawell-definedtargetandanunderstanding ofwheretheyarerelativetothatgap.Thiscanbeimprovedbyrecognizingthatweneedtoteach moredatamanagementprinciplesaspartofcoredatasciencecurriculaandtopartnerdatascientists withcapableandqualifieddatamanagementprofessionals.Finally,whileorganizationslongto exploittheirdataassetsandperformadvancedanalytics,organizationsneedtorealizethatthey mustfirstcrawl,thenwalk,thenrunaboutdataand,iforganizationstrulywanttotrusttheresultsof computationalabilities,theymustbeabletoaccountfordataacrosstheentiredatalifecycle—from acquisitionthroughfinaldisposition. Table1belowsummarizestheresearchquestionsandtopicsdiscussedaboveintoadata collaborativesresearchroadmap. Tosummarize,questionsofmutualinteresttothoseseekingtocreatenewpublicvaluethrough datacollaborativesandthedigitalgovernmentcommunityrangefromthemoretechnicaltothemore social,includingwhatformsdodatacollaborativestakeandwhy,whatimpactaretheyhavingand why,whatincentivesaredrivingparticipationincollaborativesandwhatfactorsinfluencethesuccess andsustainabilityofdatacollaboratives.

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5. CoNCLUSIoNS AND FUTURE STEPS Inthispaper,weproposedaresearchroadmapwithninetopicsandmultipleresearchquestions whichcanhelpadvanceourunderstandingofdatacollaborativesasanovelresearchdirection.The roadmapwasformulatedbasedonthediscussionsduringthepanelattheInternationalConference onDigitalGovernmentResearch(dg.o2017).Asanexample,wedescribedinmoredetailoneofthe real-lifecasesdiscussedduringthepanel-adatacollaborativeonsuicidesamongyoungpeoplein IndiainitiatedbytheUNICEFandTheGovLab.Manyofthechallengesexperiencedbythepartners ofthisdatacollaborativearereflectedinourresearchroadmap,suchasmatchingdatatoproblems, ensuringcontinuousengagement,translatingdatainsightsintoimpact,creatingadialogueamong diversestakeholdersandothers.Thisshowsthevalueofderivingaresearchagendafrompractices inthefieldandfromdiscussionsbetweenpractitionersandresearchers.

Table 1. Research roadmap for data collaboratives: summary of research questions and topics

Topic Research question

Conceptualizing

datacollaboratives ●Whatsetsdatacollaborativesapartfrommoretraditionalpublic-privatepartnershipsfocusedondatasharingorintegration? ●Whatformsdodatacollaborativestakeandhowdothesevarybypolicydomain,bypartners,byregion? ●Whatshouldbetherolesofvariousactors,includinggovernment,intheformationandoperationofdata collaboratives? Valueofdata ●Whatarethetensionsbetweenthevalueofdataasanindividual,organizational,andsocietalasset? ●Howdoestherecognitionofthesedifferenttypesofvalueofdataenableorconstraindatacollaborativespractice? ●Howisitpossibletoreconcilethevalueofmonetizingprivatedatawiththesocietalvalueofusingthemforpublic good? Matchingdatato

problemstopartners ●Howshoulddatacollaborativesbeformed? ●Whattoolsortechniquescanbeusedto‘breakdown’ormodelcomplexproblemstounderstandwherenewdatamay beuseful? ●Whattoolsortechniquescanbeusedtoidentifyrelevantdataandtesttheirutilityforacertainproblem? ●Howcanthecostsandrisksofsharingandnotsharingdatabeassessed? ●Whatdatasharingmechanismisoptimalinacertainsituationandcanmaximizethevalueofdata? ●Howisitpossibletoorchestratedatasharingundertheconditionsofurgencysuchasintimesofcrises? Impactanalysis ●Howcantheimpactofdatacollaborativesbemeasured? ●Whatkindofvalueisorcanbecreatedthroughdatacollaborativesforvariousactors? ●Howcandatacollaborativeshelpgovernmentsmeettheirmissionandcreatepublicvalue? Incentives ●Whatincentivesmotivatevariousactorstoengageinadatacollaborative? ●Whatnewincentivestructuresmaybeneededtoadvancetheuptakeofdatacollaboratives? ●Howisitbesttoincentivizetheformationofdatacollaborativesasopposedtosustainingtheminthelongrun? ●Whatincentivesarelikelytomaximizethevalueofdatacollaboratives? Capabilities ●Whatcapabilitiesarerequiredtoprovidetimelyaccesstocorporatedata? ●Whatdataanalyticscapabilitiesarerequiredtoleverageavailabledata? ●Whatcapabilitiesarerequiredatdifferentstagesofadatacollaborativefromformationtoevaluation? ●Whatcapabilitiesarerequiredinvariouscontexts(bypolicydomain,bypartner,byregion)? ●Whatistheroleoftrustbuildingcapabilityinfacilitatingdatacollaborativesandcreatingvaluefromthem? ●Howdoesthedatacollaborativemodelcreatenewcapabilitymodels? ●Whatcapabilitiesarerequiredforone-offdatacollaborativesasopposedtolong-termones? ●Howisitpossibletocreateclarityaroundrolesandresponsibilitiesofactorsinvolvedinadatacollaborative? Governance ●Whatroledoesgovernanceplayinbuildingandsustainingdatacollaboratives? ●Whatdatagovernancemodelsareoptimalfordatacollaborativesindifferentcontexts? ●Whatarethedynamicsbetweensupplyanddemandsidepartieswhenitcomestodatagovernance? ●Whatelementsofresponsibledatagovernancemustbeputinplacevisavisdatacollaboratives? ●Whatrules,norms,standardsmustbeproposed? ●Howcandatagovernanceimpacttimelydataaccessinsituationsofurgency? Datamanagement ●Whatistheroleofdatamanagementandrelatedcapabilitiesandbestpracticesinenablingdatacollaboratives? ●Whatapproachescanbeusedtotransformunstructureddataintostructuredandmakethemavailablewithinadata collaborative? ●Howcandataownershipbedefinedwithinadatacollaborative? Interoperability ●Howcandatabeintegratedandmeettherequirementsofeachparty? ●Whatapproachesexistforsupportingdatainteroperability? ●Howcandatabeanonymizedandtheresultsbeaccomplished? ●Whatisthequalityofdata?

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Theresearchroadmapshowsthatdatacollaborativesexhibitacaseofextremecomplexity, whereasthenoveltyofthephenomenonispoorlyunderstood.Weidentifiedanumberoftechnical, aswellsocialchallenges.Namely,therearedifferentroles,levelsofexpertise,andcapabilitieswhich arerequiredfromthesideofdataprovidersanddatarecipients.Itisimportanttoestablishrules, principles,andstandardsandputinplacetheelementsofaresponsibledatagovernance.Moreover, datacollaborativesshouldbelookeduponinthecontextoflargerdatamanagementregimesand practicesoftheparticipatingorganizations.Thesuggestionofthepanelwastoshiftthefocusfrom technology-centrictodata-centricorganizations,whichwouldrequirethinkingmoreaboutre-orchestratingtheprocesses,policyinstruments,andcapabilitiesoforganizationsandbuildingtrust amongpartnersarounddatasharing. Asanextstep,wecallondigitalgovernmentcommunitytofurtherexplorethequestions raisedinthispaper.Thedigitalgovernmentcommunity’sdeepdisciplinaryexpertiseandextensive experienceasinterdisciplinary,appliedscientistspoisesthemtomakesubstantialcontributionsto theemergingconceptualizationandoperationofdatacollaborativesassocio-technicalphenomena. Digitalgovernmentresearchcanpotentiallyofferusefultheoreticalframeworksandanalyticaltoolsto investigatesomeoftheresearchquestionssurroundingdatacollaboratives.Howeachstudyiscarried outandwhateachexaminationcontributestotheunderstandingofdatacollaborativeswillvary,but eachhasthepotentialtocreatenewknowledgeaboutthisimportantandinterestingevolutionofthe practiceofdatacollaboratives,inparticularwhenviewedthroughtheeyesofthepublicsectorpartners. ACKNowLEDGMENT IrynaSushaissupportedbytheSwedishResearchCouncilunderthegrantagreement2015-06563 aspartoftheproject“Datacollaborativesasanewformofinnovationforaddressingsocietal challengesintheageofdata.”MarijnJanssenissupportedbytheECprojectN°676247VRE4EIC “AEurope-wideInteroperableVirtualResearchEnvironmenttoEmpowerMultidisciplinaryResearch Communities”(https://www.vre4eic.eu)

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Iryna Susha is a postdoctoral researcher in the Department of Informatics at Örebro University, a guest researcher at Delft University of Technology, and a visiting scholar at The Governance Lab of New York University. Iryna’s current research focus is on data collaboratives and how they can be efficiently used to address societal challenges. Her earlier works on open government, open data, and citizen participation have appeared in various international journals and conferences.

Theresa A. Pardo, Ph.D. is director of the Center for Technology in Government and a full research professor of public administration and policy at the University at Albany (UAlbany), SUNY. Under her leadership, the Center works in the U.S. and around the world with governments and private sector and non-profit organizations to carry out applied research and strategic consulting projects focused on sustainable innovation and value creation in the public sector. Theresa serves as OpenNY Adviser to NYS’s Governor Andrew Cuomo, as Chair of the U.S. EPA’s National Advisory Committee and as a member of the U.S. National Science Foundation’s Business and Operations Advisory Committee. She is a member of the User Working Group of the NASA Socioeconomic Data and Applications Center and the Steering Committee of the NorthEast Big Data Hub, and a past president of the Digital Government Society. Theresa is the founder of the Global Smart Cities Smart Government Research Practice Consortium and serves as a member of the City of Schenectady’s Smart Cities Advisory Commission. In 2016, she served as the first female chair of the Oman’s Sultan Qaboos E-Government Award Jury. Theresa’s work has been recognized with UAlbany’s Distinguished Alumni and Excellence in Teaching Awards as well as the Rockefeller College Distinguished Service Award. Theresa is ranked among the top five digital government scholars in terms of citations to her published work and is a recipient of Government Technology Magazine’s 2015 Top 25 Doers, Drivers, and Dreamers Award. Theresa holds a doctorate in Information Science from UAlbany, SUNY. Marijn Janssen is a full Professor in ICT & Governance and chair of the Information and Communication Technology section of the Technology, Policy and Management Faculty of Delft University of Technology. His research interests are in the field of orchestration, shared services arrangements, and open and big data and infrastructures. He is Co-Editor-in-Chief of Government Information Quarterly (GIQ), associate editor of International Journal of Electronic Government (IJEGR), conference chair of IFIP EGOV series and is chairing mini-tracks at the DG.o, ICEGOV, HICCS and AMCIS conferences. He was ranked as one of the leading e-government researchers in surveys in 2009, 2014 and 2016, and has published over 400 refereed publications. More information: www.tbm.tudelft.nl/marijnj. Natalia Adler is the Data, Research and Policy Planning Specialist at UNICEF HQ, where she’s trying to leverage data science and expertise to tackle complex socioeconomic problems through data collaboratives with the private sector and academia. She has also conceptualized a “Cities for Children” initiative, looking at the intersection of urbanization, climate change and child rights in Latin America. Natalia has previously worked with UNICEF Nicaragua and Mozambique advancing human centered design, lean start-up techniques, entrepreneurial ecosystems, and public finance management for child rights.

Stefaan G. Verhulst is Co-Founder and Chief Research and Development Officer of the Governance Laboratory @ NYU (GovLab) where he is responsible for building a research foundation on how to transform governance using advances in science and technology. Before joining NYU full time, Verhulst spent more than a decade as Chief of Research for the Markle Foundation, where he continues to serve as Senior Advisor. At Markle, an operational foundation based in New York, he was responsible for overseeing strategic research on all the priority areas of the Foundation. He is also an Adjunct Professor in the Department of Culture and Communications at New York University, Senior Research Fellow for the Center for Media and Communications Studies at Central European University in Budapest; and an Affiliated Senior Research Fellow at the Center for Global Communications Studies at the University of Pennsylvania’s Annenberg School for Communications. Previously at Oxford University he co-founded and was the Head of the Programme in Comparative Media Law and Policy at the Centre for Socio Legal Studies, and also served as Senior Research Fellow of Wolfson College. He is still an emeritus fellow at Oxford. He also taught several years at the London School of Economics. Verhulst was the UNESCO Chairholder in Communications Law and Policy for the UK, a former lecturer on Communications Law and Policy issues in Belgium, and Founder and Co-Director of the International Media and Info-Comms Policy and Law Studies at the University of Glasgow School of Law.

Todd Harbour is the Chief Data Officer (CDO) for New York State, where he orchestrates work to design and implement a data management regime for the Empire State. Prior to this role, Todd was a senior federal government official based in the Washington DC metropolitan area, where he led work to establish data strategies, business frameworks, and data management platforms, which helped provide a reliable basis for answering questions from Congress and organizational leaders. Previously, Todd served as senior vice president at FGM Inc., a software and systems engineering corporation in Northern Virginia.