Data Management Maturity Model Introduction
Data Management Maturity ModelIntroductionUniversity of OttawaDecember 12, 2014DMM model, CMM Integration, SCAMPI, SCAMPI Lead Appraiser, TSP, and IDEAL are service marks of Carnegie Mellon University. CMMI, Capability Maturity Model, Capability Maturity Modeling, CMM, DMM, and Carnegie Mellon are registered in the US Patent andTrademark Office by Carnegie Mellon University.For more information on CMU/SEI Trademark use, please visit https://www.sei.cmu.edu/legal/marks/index.cfmSM
PresenterMelanie MeccaProgram Director,DM Products & ServicesCMMI Institute Development lead and primary author,Data Management Maturity (DMM)SMModel Led creation of DMM Assessmentmethod Leading development of DMM Trainingand Certification courses 30 years data management / dataarchitecture 7 years Enterprise Architecture (FEA) Program design and implementation EDM Expert (EDME).1
Presentation Objectives Introduction to the Model Model drivers, themes, concepts and structureHow an organization’s program is evaluatedComplementarity with bodies of knowledge and standardsUse cases for the DMM. Introduction to the supporting infrastructure Training Certifications DMM Partners / DMM Community.2
Agenda Introduction to the DMM What is it - why did we develop it Why our industry needs it What is its structure and approach DMM in Action DMM Assessments Use Cases How the DMM is Supported Adoption / Case StudiesTrainingCertificationsPartner ProgramIndustry Alliances3
Data Management Maturity ModelHistory, Description, Structure
CMMI – Worldwide Process ImprovementCMMI Quick Stats: Over 10,000organizations 94 Countries 12 Nationalgovernments 10 languages 500 Partners 1500 Appraisals in20135
Data Management Maturity (DMM)SM ModelThe DMM was released onAugust 7, 2014 3.5 years in development4 sponsoring organizations50 contributing authors70 peer reviewers80 organizations involved300 practice statements300 work products6
DMM - Guided Navigation to Lasting Solutions Reference model framework of fundamental data managementcapabilities Measurement instrument for organizations to evaluate capabilitymaturity, identify gaps, and incorporate guidelines for improvements Developed by CMMI Institute with our corporate sponsors - BoozAllen Hamilton, Lockheed Martin, Microsoft Corporation, and KinglandSystems Structured, crafted, and refined to leverage the strengths and provenapproach of CMMI, with the contribution of many subject matterexperts Pioneering Organizations – DMM Assessments: Microsoft; Fannie Mae; Federal Reserve System Statistics; OntarioTeachers’ Pension Plan; Freddie Mac; Securities and ExchangeCommission; Treasury Office of Financial Research7
Who Wrote It and Why Author team and peer review team represent multipleindustries - deep experience in designing andimplementing data management programs and solutions Industry skills include: EDW, MDM, DQ, BI, SOA, governance, big data,enterprise architecture, data architecture, business and data strategy,platform implementation, business process engineering, business rules,software engineering, appraisals and benchmarking, DMBoK, DRM, etc. Consortium approach – tested and proven approaches Broad practical wisdom - What worksExtensive discussions, implementation testing, and rigorous reviewsresulting in consensus We wrote it for ourselves and for You – all organizations To quickly and accurately measure where we areTo accelerate the journey forward with a clear path and milestones8
DMM Drivers An effective data management program requires a planned strategic effort Integrate multi-discipline effortsInculcate a shared vision and understandingData is a ‘thing’ – vital infrastructure element foundation of the n-tierarchitecture Not a project, more than a program – a lifestyle. Organizations needed a comprehensive reference model to evaluate datamanagement capabilities and measure improvements – benchmark andguidance DMM targeted to unify understanding and priorities of lines of business, IT,and data management per se. Aimed at the biggest challenges: Achieving an organization-wide perspectiveClearly communicating to the businessAligning of business with IT/DMSustaining a multi-year effort.9
DMM Themes Architecture and technology neutral – applicable to legacy, DW, SOA,unstructured data environments, mainframe-to-Hadoop, etc. Industry independent – usable by every organization with dataassets, applicable to every industry Emphasis on current state – organization is assessed on theimplemented data layer and existing DM processes Foundation for collaborative and sustained process improvement –for the life of the DM program [aka, forever].If you manage data, the DMM is for you10
Foundation for advanced solutionsYou can accomplish Advanced DataAdvancedSolutions without proficiency inDataBasic Data Management Practices,Solutionsbut solutions will: MDM Take longer Analytics Big Data Cost more IOT Non-extensible Warehousing Deliver less SOA PresentgreaterFundamental Data Management PracticesriskData Management StrategyData GovernanceData IntegrationMetadata ManagementData Management FunctionData Quality ProgramCopyright 2013 by Data Blueprint111111
DMM at a Glance12
You Are What You DO Model emphasizes behavior Creating effective, repeatable processesLeveraging and extending across the organization Activities result in work products Processes, standards, guidelines, templates, policies, etc.Reuse and extension maximum value, lower costs, happier staff Process Areas were designed to stand alone forevaluation Reflects real-world organizationsSimplifies the data management landscape for all partiesFlexible for multiple purposesBecause “everything is connected,” relationships are indicated13
DMM Capability LevelsQualityReuseRiskAd Level12PerformedManaged14
Capability and Maturity Level DefinitionsLevel1: PerformedDescriptionProcesses are performed ad hoc, primarily at the project level. There are no processesareas applied across business areas. Process discipline is primarily reactive; forexample, for data quality, the emphasis is on data repair. Foundational improvementsmay exist, but improvements are not yet extended within the organization ormaintained.PerspectiveData is managed as arequirement for theimplementation of projects2: ManagedProcesses are planned and executed in accordance with policy; employ skilled peoplehaving adequate resources to produce controlled outputs; involve relevant stakeholders;are monitored, controlled, and reviewed; and are evaluated for adherence to its processdescription.There is awareness of theimportance of managingdata as a criticalinfrastructure asset.3: DefinedSets of standard processes have been established and improved over time, providing apredictable measure of consistency. Processes to meet specific needs are tailored fromthe set of standard processes according to the organization’s guidelines.Data is treated at theorganizational level ascritical for successfulmission performance.4: MeasuredManaged and measured process metrics have been established. There are formalprocesses for managing variances. Quality and process performance is understood instatistical terms and is managed across the life of the process.Data is treated as a sourceof competitive advantage.5: OptimizedProcess performance is continually improved through both incremental and innovativeimprovements. Feedback is used to drive process enhancements and business growth.Best practices are shared with peers and industry.Data is seen as critical forsurvival in a dynamic andcompetitive market.15
DMM Benchmark to Date16
What the DMM is Not Not a compendium of all datamanagement knowledge Does not address every topic andsub-topic – focus on businessdecisions 35 years of evolution Foundational thinkers & Talented vendorsWealth of collective experience Fully mature industry practices. Not a cookbook Too much specificity 1000 pages Doesn’t identify the “one best way”17
Data Management Maturity ModelIn Action
How the DMMSM Helps an OrganizationCommon languageGradated path step-by-stepimprovementsSharedunderstanding ofprogressAccelerationFunctional workproducts to aidimplementationUnambiguouspracticestatements forclearunderstanding19
Why the DMM is useful Educational tool for all stakeholders Practical wisdom guides activities and implementation Contextually flexible WHAT, not HOW – which is dependent on businesspriorities, technical choices, and organizational culture Enables a thorough gap analysis in record time Capabilities that are absent, unintegrated or weak Strengths you can build on and extend Overlooked techniques, toolsets, capabilities Builds support - strategic, financial, and commitment ofeffort (coalition of the willing).20
Why Executive Needs the DMMCollaborative Influence Engaging the lines of business Alignment with mission, goals, andobjectivesForge a shared perspectiveCollective understanding Current strengths and weaknesses Roles to support the data assets Reveals critical needs for the datamanagement program Quick WinsHigh Value / reasonable timeframeHigh Value / Strategic Wins hearts and minds motivates collaboration forimprovements21
Starting the Journey - DMM Assessment Method To maximize the DMM’s value as a catalyst for forging sharedperspective and accelerating the program, our method: Provides interactive collaboration event with broad range of stakeholdersEvaluates capabilities collectively by consensus affirmationsNaturally facilitates unification of factions - everyone has a voice and roleSolicits key business input through supplemental interviewsVerifies the evaluation with work product reviews (evidence)Report and executive briefing presents Scoring, Findings, Observations,Strengths, and targeted specific Recommendations. Audit-level benchmark method will be introduced in 2015 for formalbenchmark of maturity, leveraging the CMMI SCAMPI A method Option for organizations and regulators.To date, over 200 individuals from business, IT, and data management in early adopter organizations haveemployed the DMM - practice by practice, work product by work product - to evaluate their capabilities.22
Measurement Confidence Activity-focused, evidencebased evaluation Emphasizes metrics – fromDay 1 to Forever Organizations can gauge theirachievements against peers Metrics justify support forfunding improvement initiatives Enhances an organization’sreputation – quality andprogress is evident to all.23
DMM Assessment SummarySample Organization24
Early Adopters – DMM Assessment Drivers Microsoft – Integrated Information Management supportingtransition to the Real-Time Enterprise, data governance Fannie Mae – Validation of EDM program and governance, discoveryfor new business priorities Federal Reserve System Statistics – Validation of inherent strengths,discovery of gaps, leverage capabilities across function and the Banks Ontario Teachers Pension Plan – Evaluation of well-roundedprogram, voice of the customer, next steps Freddie Mac – Evaluation of current state as preparation for a SingleFamily-wide data management program launch Center for Army Analysis – Enhance data asset strength to supportanalytics for logistics, targeting, troop deployments for warfightingeffort.Wherever you are, there you are25
Sample Output – Excerpt - DM RoadmapComprehensive and Realistic Roadmap for the Journey26
MicrosoftThe Real Time EnterpriseVirtually everything in business today is an undifferentiatedcommodity, except how a company manages its information. Howyou manage information determines whether you win or lose.– Bill GatesBusiness ProcessesProcesses achieve businessresultsPeoplePeople make decisionsInformationDecisions are driven byInformationTechnologyTechnology speeds the deliveryof information[ 27 ]Strategic EnterpriseArchitecture
MicrosoftEstablishing a Common Data Management LanguageData Management Maturity Model[ 28 ]
MicrosoftMaturity Levels Related to Real Time DataReal –TimeCompetitive imizedProcesses are improved on a continuous basis andadvocated at the executive management level.Level4MeasuredEstablished metrics. Variance management across theprocess lifecycle.LevelBatchEnabling tablished processes, improved over time. Tailoredto meet specific needs predictably and consistently.Formalized processes. Infrastructure supports at businessunit level. Clearly defined roles and responsibilities.Ad-hoc processes. Emphasis on data repair . Transitory improvements.[ 29 ]Strategic EnterpriseArchitecture
MicrosoftCMMI Assessment Recommendations Unified effort to maximize datasharing and quality Monitor and measure adherenceto data standards Governance Integrate data governancestructuresPrioritize policies, processes,standards, to support corporateinitiativesMap key business processesto dataLeverage Meta Datarepository Platform &Architecture Leverage best practices fordata archival and retentionMaximize shared servicesutilizationData Quality [ 30 ]Top-down approach toprioritizationUp-stream error preventionCommon Data DefinitionsStrategic EnterpriseArchitecture
MicrosoftKey Lessons In the world of Devices and Services, Data Management is a pillar of effectiveness DMM is a key tool to facilitate the Real-Time Enterprise journey Active participation of cross-functional teams from Business and IT is key for success Employee education on the importance of data and the impact of data management is agood investment Build on Strengths!Microsoft IT Annual Report may be found at:http://aka.ms/itannualreport[ 31 ]Strategic EnterpriseArchitecture
DMM Assessment – Fannie MaeGovernance Results How the DMM Assessment pointed out enhancements for astrong Governance Program: Identified the need to strengthen Governance to increasetangible business benefits Identified immediate “quick wins” and strategic initiatives Strengthened relationship between data governance and dataquality Incorporated the feedback from Assessment recommendationsand LOBs into design of the new Data Governance Operatingmodel for the firm Established “fit for purpose” governance – expanded service toLOBs.32
When Should I Employ the DMM? Assess your current capabilities before: Embarking on a major architecture transformationDeveloping (or enhancing) your data management strategyEstablishing data governanceUndertaking a major expansion of analyticsImplementing a data quality programImplementing a metadata repositoryDesigning and implementing multi-LOB solutions: Master Data ManagementShared Data ServicesEnterprise Data WarehouseConversion to an ERPEtc., etc., etc.Energy audit - Executive physical33
DMM Infrastructure:Support for a Global StandardReference Model
DMM Training & Certification Three successive courses leading to certification as anEnterprise Data Management Expert (EDME). Effectivecomplement to CDMP, CBIP and similar certifications DMM Introduction – 3 days – implementation oriented DMM Advanced – 5 days – mastery of DMM content Enterprise Data Management Expert – 5 days QualificationsMastery of Method and DeliverablesExamObservation / Certification3 year license / recertificationIntroAdvancedEDME35
Near-Future Certification DMM Lead Appraiser (DMM LA) training and certification Fall 2015 Leveraging the SCAMPI A benchmarking method used by over 10,000organizations for a formal appraisal. Option to publish results Of interest to regulatory organizations - audit For organizations ready to benchmark their progress. Individuals are sponsored by a PartnerIntroAdvancedDMM LA36
DMM Partner ProgramPartner Program – organizations sponsor EDMEs and have a voice in the evolutionof the DMM going riorityAccessEarlyInsightBetaTesting37
Industry Outreach / Alliances Articles and White PapersConferences and Webinars – seminars and case studiesFederal AgenciesTrade AssociationsData Management Association International And .we are eager to align with similar standardsand standards bodies (so give us a call). 38
For More InformationPlease visit our web site:Home, FAQs, White Papers, Model ement-maturityTraining Schedule and ngDMM Partner -partner/becomepartner-dmm/39
Contactmmecca@cmmiinstitute.com – Melanie Mecca240-274-7720 (M)Please connect with me on Linked In40
Appendix:DMM Governance PrinciplesGovernance Management Process AreaExcerpt from ‘Data Governance – the Forest and the Trees’Winter Data Governance conference
Governance in the DMM Big Three Key functions for data assets BuildingNurturingSustainingCompliance One process area (Vertical) Decisions, Activities, Work Productsin all process areas (Horizontal) Active Engagement Executive Support Does not dictate a structure.Architecture42
DMM Governance Calls Out OversightPoliciesApprovalsAlignmentMetricsAccess ControlsCompliance / AuditRoles and ResponsibilitiesStakeholder representation (all levels)43
Governance Management (GM)PurposeDevelop the ownership, stewardship, and operational structure needed to ensure thatcorporate data is managed as a critical asset and implemented in an effective andsustainable manner.DefinitionProcesses that facilitate collaborative decision making and effectively implementbuilding, sustaining, and compliance functions for the data assets. Governance bodiesare fundamental to create a corporate culture of shared responsibility for data.44
GM GoalsA process is established and followed for aligning data governance with businesspriorities, including ongoing evaluation and refinement to address changes in thebusiness, such as the need to encompass new functions and domains.Data governance ensures that all relevant stakeholders are included, and thatroles, responsibilities, and authorities are clearly defined and established.Compliance and control mechanisms with appropriate policies, processes, andstandards are established and followed.45
GM Benefits to the DM Program “Every shoulder to the wheel” to build, nurture, and improve thedata assets Makes organization-wide data decisions possible, increasesawareness and engagement of executives Structured collaboration Clear roles – ends confusion Clear responsibilities – self-tasking Implements and enforces compliance with policies, processes,and standards Critical foundation for well-executed enterprise data initiatives Data Management and Data Quality Strategies Business Glossary Metadata Management, etc., etc. Increases agility through reuse of established processes Increases communications, relevance and impact of the datamanagement program and the data management function46
GM Capability LevelsLevel 1Project-based datagovernanceactivities areperformed;owners, stewards,and accountabilityare defined at theproject level.Level 2Defined governancestructure isestablished; roles,responsibilities,accountabilityestablished atsubject area level;defined policies,processes, andstandards;governance reviewprocess.Level 3Organization-widegovernance structurewith executivesponsorship;operational executivedata governance; allhigh-priority subjectareas arerepresented;processes arestandardized; metricsemployed to evaluateeffectiveness;analyzed againstobjectives.Level 4Governance ismanaged usingstatistical keyperformanceindicators andresults used torefine theprogram.Level 5Governance iscontinuouslyimproved throughresearch in bestpractices; strongprogram is a modelof success.47
GM Key Activities - Summary Approve the data management strategy Approve policies, processes, and standards Actively participate in “enterprise” initiatives, such as: Define and approve business terms by subject areaDetermine metadata categories, sub-categories and propertiesDefine and participate in the data quality strategy and program, etc.Assign accountability and responsibilityDevelop decision authorities and change mechanismsEnforce complianceAddress external requirements and organization-wideneeds, such as role-based access, etc.48
Data Governance CapabilitiesProcess AreaFunctional Practice StatementGovernanceManagement1.1Data governance functions are performed.1.2Ownership, stewardship, and accountability for data sets areprimarily project-based assignments.2.1A defined and documented data governance structure is in place.2.2Governance roles, responsibilities, and accountabilities areestablished for data subject area by priority, as stated in thebusiness or data strategy.2.3Data subject area representatives participate in data governance andassociated processes.2.4Data governance follows defined policies, processes, and standards.2.5A review process is established and followed to evaluate andimprove data governance.49
Data Governance CapabilitiesProcess AreaFunctional Practice StatementGovernanceManagement3.1 An organization-wide data governance structure and rollout plan isestablished with executive sponsorship.3.2 Executive level organization-wide data governance is operationalfor the organization’s high-priority subject areas.3.3 Data governance includes representatives from all business unitswhich are suppliers or consumers of high-priority data subject areas.3.4Standard data governance policies and processes are followed.3.5 Data governance determines and approves appropriate metrics toevaluate effectiveness of governance activities.3.6 An evaluation process is established for refining data governance toalign with changing business priorities and to expand as needed toencompass new functions and domains.3.7 Classroom, mentoring, e-learning, or on-the-job training in datagovernance processes is required for new governance members and otherstakeholders.50
Data Governance CapabilitiesProcess AreaFunctional Practice StatementGovernanceManagement3.8 Data governance activities and results are analyzed againstobjectives periodically and reported to executive management.4.1 Statistical and other quantitative techniques are applied todetermine if governance efforts are changing organizational behaviorsappropriately.4.2 Adjustments to data governance activities and structure are madebased on analysis results.5.1 External governance structures and industry case studies areevaluated for best practices and lessons learned, providing ideas forimprovements.5.2 The data governance structure is communicated to the peer industryas a model of best practice.5.3Data governance processes are continually refined and improved.51
Governance Practices Across the DMM52
Governance - What Should We Be Doing Now? Conduct a DMM Assessment – then leverage that energy! If you don’t have an organization-wide program Engage an Executive Sponsor Start with one critical line of business or one multiple stakeholderprogram. If you have a limited program – perhaps only based in IT, in onebusiness area, etc. Present business case aimed at solving key issues Persuade key stakeholders based on their business interests If you have a complex program Identify areas of lack of clarity in RACI Work with senior body to enhance organizational structure Propose a streamlined interaction model.53
Data Management Maturity Model Introduction University of Ottawa December 12, 2014 SM DMM model, CMM Integration, SCAMPI, SCAMPI Lead Appraiser, TSP, and IDEAL are service marks of Carnegie Mellon University. CMMI, Capability Maturity Model, Capability Maturity Modeling, CMM, DMM
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