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eResearch Australasia Conference | Melbourne – Australia | 10 - 14 October - 2016 The American Geophysical Union Data Management Maturity Program Presenter Lesley Wyborn 3 Shelley Stall 1 , Brooks Hanson 2 , Lesley Wyborn 3 1American Geophysical Union, Washington, USA, [email protected] 2 American Geophysical Union, Washington, USA, [email protected] 3 National Computational Infrastructure, Canberra, Australia, [email protected] SUMMARY The American Geophysical Union (AGU), with 60,000 members internationally, is the largest global professional society for the Geosciences. In response to emerging data management mandates from funders, AGU has developed a program that will help data repositories, large and small, domain-specific to general, use best practices to assess and improve their data management practices. The cornerstone of the program is the Data Management Maturity (DMM) SM framework which has been adapted to the specific needs of the Earth and space sciences. A data management assessment using the DMM SM involves identifying accomplishments and weaknesses in an organization, compared with leading practices for data management. Recommendations can help to improve quality and consistency across the community that will facilitate reuse in the data lifecycle. Through governance, quality, and architecture process areas the assessment can measure the ability for repositories to make their data accessible, discoverable, and interoperable. INTRODUCTION Emerging data management mandates from funders, in addition to growing recognition of the enduring value of data, are posing new challenges for researchers and repositories. Curating research data, enabling discovery, elevating quality across diverse repositories, and helping researchers collect and organize data through its full life cycle, including publication and reporting back to funding agencies, are multidimensional challenges that will continue to grow in complexity in the coming years. There is no easy solution, but an effective approach has been developed by the AGU. THE AMERICAN GEOPHYSICAL UNION AND ITS POSTION ON DATA MANAGEMENT The AGU is an international scientific society dedicated to promoting discovery in Earth and space science for the benefit of humanity. AGU has more than 60,000 members worldwide and is the largest professional society for the geosciences. AGU is dedicated to the furtherance of the Earth and space sciences and to communicating our science’s ability to benefit humanity [1]. AGU is built on a foundation of shared values that includes valuing the scientific method and the generation and dissemination of scientific knowledge. AGU has developed a series of position statements that relate the understanding and application of the geophysical sciences to relevant public policy [2]. One of the position statements on Data Management and Research Policy states that ‘Earth and space science data should be credited, preserved, open, and accessible as an integral responsibility of scientists, data stewards, and sponsoring institutions’ [3]. To address this emerging data challenge, AGU is developing a program that will help data repositories, large and small, domain-specific to general, use best practices to assess and improve their data management practices. AGU is partnering with the CMMI® Institute to adapt their DMM SM framework to the specific needs of the Earth and space sciences. CMMI® (Capability Maturity Model Integration) is a process improvement training and appraisal program and service, administered and marketed by the CMMI and previously developed and owned by Carnegie Mellon University (CMU). A successful appraisal is required by many U.S. Government contracts to demonstrate excellence in operational practices. The DMM SM model [4], released in August 2014, was developed over 3.5 years by 4 sponsoring organisation, 50+ contributing authors, 70 peer reviewers and 80+ organisations. A data management assessment using the DMM SM involves identifying accomplishments and weaknesses in and organisation compared to leading practices for data management. Recommendations can help improve quality and consistency for the assessed organization and support improvement across the community that will facilitate reuse in the data lifecycle. Through governance, architecture and quality process areas the assessment can measure the ability for data to be accessible, discoverable, and interoperable. DATA MANAGEMENT MATURITY PROCESS AREAS AND THEIR DESCRIPTIONS The DMM SM is comprised of 25 process areas: 20 data management process areas, as well as 5 supporting process areas (Table 1). The model is organized into 5 categories: strategy, governance, data quality, operations, and platform and

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Page 1: The American Geophysical Union Data Management Maturity ... › 2016 › 03 › eresau2016_pa… · that will help data repositories, large and small, domain-specific to general,

eResearchAustralasiaConference|Melbourne–Australia|10-14October-2016

TheAmericanGeophysicalUnionDataManagementMaturityProgram

PresenterLesleyWyborn3

ShelleyStall1,BrooksHanson2,LesleyWyborn31AmericanGeophysicalUnion,Washington,USA,[email protected]

2AmericanGeophysicalUnion,Washington,USA,[email protected],Canberra,Australia,[email protected]

SUMMARYTheAmericanGeophysicalUnion(AGU),with60,000membersinternationally,isthelargestglobalprofessionalsocietyfortheGeosciences.Inresponsetoemergingdatamanagementmandatesfromfunders,AGUhasdevelopedaprogramthatwillhelpdatarepositories, largeandsmall,domain-specific togeneral,usebestpracticestoassessand improvetheir data management practices. The cornerstone of the program is the Data Management Maturity (DMM)SMframework which has been adapted to the specific needs of the Earth and space sciences. A data managementassessmentusingtheDMMSMinvolvesidentifyingaccomplishmentsandweaknessesinanorganization,comparedwithleading practices for data management. Recommendations can help to improve quality and consistency across thecommunitythatwill facilitatereuseinthedatalifecycle.Throughgovernance,quality,andarchitectureprocessareastheassessmentcanmeasuretheabilityforrepositoriestomaketheirdataaccessible,discoverable,andinteroperable.

INTRODUCTIONEmergingdatamanagementmandatesfromfunders,inadditiontogrowingrecognitionoftheenduringvalueofdata,areposingnewchallengesforresearchersandrepositories.Curatingresearchdata,enablingdiscovery,elevatingqualityacrossdiverserepositories,andhelpingresearcherscollectandorganizedatathroughitsfulllifecycle,includingpublicationandreportingbacktofundingagencies,aremultidimensionalchallengesthatwillcontinuetogrowincomplexityinthecomingyears.Thereisnoeasysolution,butaneffectiveapproachhasbeendevelopedbytheAGU.

THEAMERICANGEOPHYSICALUNIONANDITSPOSTIONONDATAMANAGEMENTThe AGU is an international scientific society dedicated topromoting discovery in Earth and space science for thebenefit of humanity. AGUhasmore than 60,000membersworldwide and is the largest professional society for thegeosciences.AGUisdedicatedtothefurtheranceoftheEarthandspacesciencesandtocommunicatingourscience’sabilitytobenefithumanity[1].AGUisbuiltonafoundationofsharedvaluesthatincludesvaluingthescientificmethodandthegenerationanddisseminationofscientificknowledge.AGUhasdevelopedaseriesofpositionstatementsthatrelatetheunderstandingandapplicationofthegeophysicalsciencestorelevantpublicpolicy[2].Oneofthepositionstatements onDataManagement and Research Policy states that ‘Earth and space science data should be credited,preserved,open,andaccessibleasanintegralresponsibilityofscientists,datastewards,andsponsoringinstitutions’[3].

Toaddressthisemergingdatachallenge,AGUisdevelopingaprogramthatwillhelpdatarepositories,largeandsmall,domain-specifictogeneral,usebestpracticestoassessandimprovetheirdatamanagementpractices.AGUispartneringwiththeCMMI®InstitutetoadapttheirDMMSMframeworktothespecificneedsoftheEarthandspacesciences.CMMI®(CapabilityMaturityModelIntegration)isaprocessimprovementtrainingandappraisalprogramandservice,administeredandmarketedbytheCMMIandpreviouslydevelopedandownedbyCarnegieMellonUniversity(CMU).AsuccessfulappraisalisrequiredbymanyU.S.Governmentcontractstodemonstrateexcellenceinoperationalpractices.TheDMMSMmodel[4],releasedinAugust2014,wasdevelopedover3.5yearsby4sponsoringorganisation,50+contributingauthors,70peerreviewersand80+organisations.AdatamanagementassessmentusingtheDMMSMinvolvesidentifyingaccomplishmentsandweaknessesinandorganisationcomparedtoleadingpracticesfordatamanagement.Recommendationscanhelpimprovequalityandconsistencyfortheassessedorganizationandsupportimprovementacrossthecommunitythatwillfacilitatereuseinthedatalifecycle.Throughgovernance,architectureandqualityprocessareastheassessmentcanmeasuretheabilityfordatatobeaccessible,discoverable,andinteroperable.

DATAMANAGEMENTMATURITYPROCESSAREASANDTHEIRDESCRIPTIONSTheDMMSMiscomprisedof25processareas:20datamanagementprocessareas,aswellas5supportingprocessareas(Table1). Themodel isorganized into5categories:strategy,governance,dataquality,operations,andplatformand

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eResearchAustralasiaConference|Melbourne–Australia|10-14October-2016

architecture. These process areas serve as the principal means to communicate the themes, goals, practices, andexample work products of the model. Accomplishment of process area practices allows an organization to buildcapabilities and, in conjunction with managing and institutionalizing practices accomplish maturity in datamanagement.Therearedependenciesandinterrelationshipsamongtheprocessareasdefinedwithinthemodel.

Table1:The25ProcessareasoftheDataManagementMaturityModel

Categories Description ProcessAreas

DataManagementStrategy

Encompassesprocessareasdesignedtofocusondevelopment,strengthening,andenhancementoftheoveralldatamanagementprogramofanorganization.

1.DataManagementStrategy2.Communications3.DataManagementFunction4.GrantStrategy/BusinessCase5.Funding

DataGovernance

Identifiestheimportantdataassets,definesandimplementsprocessestomanagetheseassets,andformallymanagesthemthroughouttheorganization.

6.GovernanceManagement7.Vocabulary/Glossary8.MetadataManagement

DataQuality Defineacollaborativeapproachforreceiving,assessing,cleansing,andcuratingdatatoensurefitnessforintendeduseinthescientificcommunity.Thisincludesensuringmetadatacontentandstandardsaremet,datasubmissionsarecomplete,anddataisaccessibleattherighttime.

9.DataQualityStrategy10.DataProfiling11.DataQualityAssessment12.DataCleansingandCuration

DataOperations

Ensuresdatarequirementsarefullyspecifiedanddataistraceablewithdocumentedprovenance,managesdatachanges,andmanagesdatacontributions.

13.DataRequirementsDefinition14.DataLifecycleManagement15.Contribution/ProviderManagement

PlatformandArchitecture

Ensurestheimplementeddatamanagementplatformsuccessfullyintegrates,archives,andpreservesdataassetstosupporttheorganizationand/orscientificcommunityobjectives.

16.ArchitecturalApproach17.ArchitecturalStandards18.DataManagementPlatform19.DataIntegration20.DataArchivingandPreservation

SupportingProcesses

Foundationalprocessesthatsupportadoption,execution,sustainment,andimprovementofdatamanagementprocesses.

21.MeasurementandAnalysis22.ProcessManagement23.ProcessQualityAssurance24.RiskManagement25.ConfigurationManagement

APPLYINGTHEDATAMANAGEMENTMATURITYMODELSMAdatamanagementassessmentusingtheDMMSMinvolvesafull,on-sitereviewbyacertifieddatamanagementexpertofpracticesandoperationsofarepositoryagainsttheaboveprocessareas.Afullreportincludesrecommendationsandaroadmapfortheorganisationtousetoimproveoperationsandcosteffectivenessandfacilitatecurationandreuseoftheorganisation’sdataassetsthroughoutthedatalifecycle.

During2015/2016twoorganisationstookpartinpilotstudiestotesttheprocess.ThefirstwastheUnitedStatesGeologicalSurvey(USGS)ScienceBasewhichisalong-taildatarepositorytaskedwithprovidingsharedaccesstodataviadatasharing,webservices,andcontentmanagement.ThesecondwastheBiologicalandChemicalOceanographyDataManagementOffice(BCO-DMO)whichworkwithinvestigatorstoservedataonlinefromresearchprojectsfundedbytheBiologicalandChemicalOceanographySectionsandtheDivisionofPolarProgramsAntarcticOrganismsandEcosystemsProgramattheU.S.NationalScienceFoundation.Bothgroupsreportedthattheywereunsurehowtobeginthetask,butultimatelyfoundstrongvalueinhowthemodelhelpedsupporttheirorganizationalplansandgoals.

REFERENCES1. AGUStrategicPlan2016http://sites.agu.org/leadership/strategic-plan/,accessed7June2016.2. AGUPositionStatementsandLettershttp://sciencepolicy.agu.org/agu-position-statements-and-letters/,accessed7

June2016.3. AGU Position Statement on Data https://sciencepolicy.agu.org/files/2013/07/AGU-Data-Position-Statement-Final-

2015.pdfaccessed7June2016.4. CMMIInstitute,2015.DataManagementMaturityModelSM,CarnegieMellonUniversity,Pittsburgh,USA.

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eResearchAustralasiaConference|Melbourne–Australia|10-14October-2016

ABOUTTHEAUTHORS

ShelleyStall is theAssistantDirector forAGU'sDataManagementAssessmentProgramwhereshe is responsible forworkingwithAGU'smembersandtheirorganizationstoassessthecurrentstateoftheirdatamanagementpracticesandprovideguidanceonhowtoprepareandimplementthenecessarychangestoaddressdatachallengesintheEarthand space sciences community. With over twenty-six years working in high-volume, complex data managementenvironments, Ms. Stall has helped organizations in not-for-profit, commercial, defense, and federal civiliancommunities address implementation of regulation, interoperability, worldwide data governance, metadatamanagement,masterdatamanagement,andorganizationalchangemanagement. Ms.Stall'sdiverseexperienceasaprogram and project manager, software architect, database architect, performance and optimization analyst, dataproduct provider, and data integration analyst provides her with a core capability in development of practical andsustainabledatamanagementpractices.Ms.StallisacertifiedEnterpriseDataManagementExpert(EDME)throughtheCMMIInstitute'sDataManagementMaturity(DMM)programandaCertifiedDataManagementProfessional(CDMP)throughDAMAInternational'scertificationprogram.Ms.StallhasadegreeinMathematicsandaMasterofBusinessAdministration(management)withafocusontechnicaloperations.

BrooksHansonservesastheDirectorofPublicationsfortheAmericanGeophysicalUnion(AGU).He'sresponsibleforoverseeingAGU's portfolio of Journals and their operations, helping set overall editorial policies, and leading futuredevelopments.BeforearrivingatAGU,heservedastheDeputyEditorforPhysicalSciencesatScienceandearlierasaneditoratScience.TherehewasresponsibleforhelpingleadtheeditorialdirectionofScience,overseeingpeer-reviewandmanuscriptselectionsinthephysicalsciencesandhelpingdevelopAAAS'sandScience'scontentmanagementandpublishingsystemsandworkflow.BrookshasaPh.D.inGeologyfromUCLAandheldapost-doctoralappointmentattheDepartmentofMineralSciences,SmithsonianInstitution.

LesleyWybornisageochemistbytrainingandjoinedthethenBMRin1972andforthenext42yearsheldavarietyofgeoscienceandgeoinformaticspositionsasBMRchangedtoAGSOthenGeoscienceAustralia. In2014she joinedtheANUandcurrentlyhasajointadjunctfellowshipwithNationalComputationalInfrastructureandtheResearchSchoolofEarthSciences.Shehasbeen involved inmanyAustralianeResearchprojects, including theNeCTAR fundedVGL, theVirtual Hazards, Impacts and Risk Laboratory, and the Provenance Connectivity Projects. She is Deputy Chair of theAustralianAcademyofScience‘DataforScienceCommittee’.ShewasawardedtheAustralianPublicServiceMedalforhercontributionstoGeoscienceandGeoinformaticsin2014,theGeologicalSocietyofAmerica,GeoinformaticsDivisionCareerAchievementAwardfor2015andin2016shewasmadeaFellowoftheGeologicalSocietyofAmerica.


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