NASCIO represents state chiefinformation officers and infor-mation technology executivesand managers from stategovernments across the UnitedStates. For more informationvisit www.nascio.org.
201 East Main Street, Suite 1405Lexington, KY 40507Phone: (859) 514-9153Fax: (859) 514-9166 Email: [email protected]
Data Governance Part III: Frameworks – Structure for Organizing Complexity
NASCIO: Representing Chief Information Officers of the States
Introduction
NASCIO has presented previous researchbriefs that introduce the subject of datagovernance, and emphasize the impor-tance of managing data and informationassets as enterprise assets. Maturity modelswere presented that help describe thejourney state government must anticipateand plan.
This research brief presents the concept offrameworks that will describe what consti-tutes a data governance program. Thefocus will be on frameworks from the DataManagement Association (DAMA), theData Governance Institute (DGI), and IBM.
In general, frameworks assist in describingmajor concepts and their interrelationships.Frameworks assist in organizing thecomplexity of a subject. Frameworks facili-tate communications and discussion. All ofthese descriptors apply as well to frame-works related to data governance.Additionally, data governance frameworksassist in demonstrating how data gover-nance relates to other aspects of datamanagement, data architecture, and enter-prise architecture.
Use of frameworks can assist state govern-ment in planning and executing on aneffective data governance initiative. Theyassist in achieving completeness in aprogram. In any subject or discipline
frameworks and maturity models assist indescribing the scope—both breadth anddepth—of an initiative. This holds true aswell for data, information and knowledgemanagement.
The Challenge
The challenge in state government is ahistory of state agencies operating fairlyautonomously regarding processes andinvestment related to managing informa-tion. In the decades of this history,strategic intent, processes, organization,information management, infrastructure,technology, training, incentives and opera-tions have been developed in a highlydecentralized manner to meet the needs ofstate agencies independent of one anoth-er. As state government pursues anenterprise perspective in managing its dataand information assets, it will recognize adisparity in data maturity levels within thevarious lines of business, subject areas,knowledge bases, and even applications.The emphasis in this series has been onstate wide initiatives. However, it is impor-tant and prudent for state government tolook for examples, best practices,standards, and processes that are currentlyworking effectively within state agencies.This look must include evaluation ofmissteps and false starts that can providevaluable lessons so previous mistakes arenot repeated. An inspection of what’s
Data Governance Part III: Frameworks – Structure for Organizing Complexity
May 2009
NASCIO Staff Contact: Eric SwedenEnterprise Architect [email protected]
already there can provide valuable inputinto the state wide data governanceprograms.
There has been progress in gaining anenterprise perspective over the past decade.Ten years ago a request for criminal historyinformation would most likely have metwith a different response than it wouldtoday. Today, the relevance of criminalhistory information to hiring decisions,education credentialing and custodydecision making is well understood. Stategovernment is designing and implement-ing collaborative information exchangesthat entail all government lines of businessthat historically did not share information.Further discussion of the issues of informa-tion sharing is presented in the NASCIOreport “Perspectives - GovernmentInformation Sharing: Calls to Action.”1
Examples of collaborative informationexchange partners include: justice andhomeland security; justice and publichealth; justice and environmental; trans-portation and environmental; correctionsand healthcare providers. The recognitionof cross line of business collaborativeinformation exchanges has created newdemand and opportunities for more effec-tive government decision making. It hasalso brought to light the disparity in dataterms, definitions, and implementations.Now state government must develop itscapability for managing enterprise dataand information and that is where thedisciplines of data and knowledgemanagement become relevant.Organizations in all sectors have recog-nized the necessity of viewing informationas an enterprise asset as demonstrated bythe advent of Enterprise InformationManagement (EIM) initiatives.
Previous development of point solutionsand “silos of information” have created ahighly diversified portfolio of processes,data assets, and technologies across stategovernment. Today, it is recognized thatstate agencies can benefit decidedly fromworking collaboratively. Such collabora-tion then demands the capability to shareinformation easily and quickly. However,
Data Governance Part III: Frameworks – Structure for Organizing Complexity2
NASCIO: Representing Chief Information Officers of the States
EnterpriseInformationManagement (EIM):An operationalcommitment todefine, secure,maintain andimprove the integrityand efficiency ofinformation assetsacross businessboundaries, thusachieving key objec-tives of anorganization’s enter-prise informationarchitecture strategy.2
Gartner
due to the vast complexities created by ahistory of independence, the greatestbarrier to collaborative work relationshipsis the inability to easily share informationacross agency boundaries. As new circum-stances arrive that were never anticipated,the ability for state government to work asa single enterprise in meeting these newcircumstances is greatly hindered. Thesecircumstances were created during an erawhen collaboration was not promoted andwas even discouraged. Now state govern-ment is seriously challenged in its effortsto treat data and information assets asenterprise assets. Before state governmentcan truly harvest the benefits of sharedservices, SOA, or cloud computing, it mustunderstand and properly manage itsdata/information.
State governments are desperatelyseeking ways to begin to manage theirinformation assets actively. The questionsare myriad, but primary is the question ofhow to get started. NASCIO’s series ongovernance is intended to provide thatguidance. First is the recognition thatthere is required governance or oversightthat must be established which recognizesthe decision rights of all stakeholders. Thishas been described in the introductoryresearch brief.3
Second is developing an understandingregarding the journey that must be antici-pated to achieve a mature datagovernance capability. That journey waspresented in the second in these series.4
Now begins a process of examining thebuilding blocks of data and informationmanagement. These include the concepts,organization, and process that compriseinformation management or datamanagement. Anticipating the need forcollaboration, common subject areas thatare shared across state agencies willeventually have to be identified.
NASCIO: Representing Chief Information Officers of the States
Data Governance Part III: Frameworks – Structure for Organizing Complexity 3
Need for a Business Outcome
As an example of a subject area, stateshave selected PERSON and all the varioussubtypes related to it as an obvious candi-date for establishing a common, sharedsubject area. Another example is PLACE.These subject areas are excellent startingsubjects for establishing commonality ofdescription and representation acrossstate government data architecture. Ifagencies can agree and share this informa-tion, great gains can be achieved forcreating a single state government facetoward the citizen.
Embarking on large enterprise wide initia-tives has not proven successful historically.A better approach is to begin with a focusarea or business outcome that is beingsought within state government. In theprocess of meeting that business need,parallel ongoing activity can be undertak-en to build the enterprise-wide datagovernance capability.
As an example of this kind of focus, somestates have pursued data governanceinitiatives related to education. Stategovernment is interested to know ifprimary education is properly preparingelementary students for intermediate,junior high and high school. Are highschool programs adequately preparingstudents for college programs? Arecollege programs adequate for thedemands of the 21st Century economy?How effective is the teaching / trainingprocess? These critical questions form thefocus area and help describe the businessoutcome that drives a need for establish-ing the necessary data and informationmanagement to continually evaluate theprocess of education. Effective manage-ment of information can provide the basisfor understanding that can then lead tothe necessary strategic decision making toensure changes and transformations areinitiated and orchestrated within theprocesses and systems of education.Proper data governance is necessary toguide and sustain effective management ofinformation.
The demand for data and information toenable effective state governmentdecision making forms the basis forongoing development of business intelli-gence capabilities. Such capabilities arerequired to truly understand the problems,challenges, successes, and requirements ofeducation at all levels. With proper intelli-gence, analysis, and decision making, theeducational process can be continuallyimproved to present relevant, effectivetraining and education outcomes. Further,assumptions can be evaluated and eithervalidated or corrected.
As was presented regarding data gover-nance maturity models, there are also avariety of frameworks that deserve refer-ence. Each one brings valuableperspectives and dimensions to stategovernment data governance and datamanagement programs.
The Data Management Association(DAMA) framework presents how datagovernance drives other functions thatcomprise an enterprise data managementinitiative. The Data Governance Institute(DGI) Framework provides an overarchingprocess for establishing and sustaining adata governance initiative. The IBM DataGovernance Model has an inherent frame-work for data governance which waspresented in NASCIO’s research brief ondata governance maturity models. Thisframework presents an emphasis on therelationships among the major groupingsof data governance elements.
The Data Management AssociationInternational - DAMA
DAMA published the Data ManagementBody of Knowledge (DMBOK) in 2009. Thisis the culmination of years of work, andcontributions from an internationalcommunity of data management profes-sionals representing all sectors of theglobal economy. This framework is veryrelevant to NASCIO’s presentation ofgovernance. The DMBOK goes beyondstrictly data governance to dovetail into
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Data Governance Part III: Frameworks – Structure for Organizing Complexity4
the functions and processes for compre-hensive data management. As presentedin earlier NASCIO research briefs, “datamanagement” is the prevailing term.However, DAMA has made it clear that thediscipline of data management is broadenough to include data, information andknowledge. It is expected that over timemore organizational emphasis will begiven to the importance of managingdata, information and knowledge assets.The DMBOK will be a valuable resource forstate government as it pursues moreactively managing these assets. Itprovides detailed contextual diagrams,descriptions, diagrams, and a storehouseof references for each of the 10 functionsthat comprise the DAMA DataManagement Framework.
The goals of the DMBOK are focused ondata and knowledge management:� Build consensus� Provide standard definitions� Identify guiding principles� Provide an overview of commonly
accepted good practices� Identify common issues� Clarify scope and boundaries� Provide a guide to other related
resources
The recurring themes of the DMBOK aredescribed in Figure 1.
The DAMA framework is really a set of twoframeworks that encompass data manage-ment: a functional framework and anenvironmental element framework. Thecenter cell in the functional frameworkdescribes governance. The placement ofthis cell describes the overarching role ofdata governance—it literally touchesevery aspect of data / informationmanagement. Inspection of the DAMAfunctional framework reveals the compo-nents of data management that must beaddressed in a data management operat-ing discipline. DAMA published the DataManagement Body of Knowledge(DMBOK)5 which provides a descriptionand context diagram for each of the 10functions depicted. The DAMA frameworkhas changed somewhat over time. It isexpected that it will continue to change asdata and information managementcontinues to mature.
The DAMA Functional and EnvironmentalElement frameworks are described indetail in the Data Management Body ofKnowledge (DMBOK). The two componentframeworks are meant to work together.The core framework is the blue circle—
DataManagement
Themes
DataIntegration
DataStewardship
DataQuality
EnterprisePerspective
CulturalChange
Leadership
FIGURE 1: DMBOK Themes
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Data Governance Part III: Frameworks – Structure for Organizing Complexity 5
Data Management Functions. Eachfunction within the blue circle iscomprised of the associated elementsfrom the framework of EnvironmentalElements. DAMA created a two dimen-sional worksheet that demonstrates theinteraction. For each function, the variousenvironmental elements must be defined.The worksheet will be unique for eachenterprise and reflect specific organiza-tion, culture, and focus for that enterprise.
The implied sequence in the framework ofdata management functions is to beginwith Data Governance, advance to DataArchitecture at 12:00 and move aroundclockwise to Data Quality Management at11:00. Most noteworthy is the importanceof starting with data governance. All otherfunctions are subordinate to that function.Further inspection of the Data Governancefunction will make it clear why thatfunction should drive the others. It iswithin that function that the intent of anenterprise data governance program is
established. That governance includes:� Strategic business intent – Business
Goals and Objectives� Strategic intent of data management� Organization� Policies� Performance metrics
This approach is consistent with theNASCIO Enterprise Architecture ValueChain which begins with understandingand developing the environmentalcontext, followed by the understanding ofspecific needs and markets under consid-eration, followed by establishing strategicintent and then enabling that intentthrough capabilities. Data management /knowledge management is a criticalenabling capability as has been empha-sized in previous NASCIO research briefson this subject.
DAMA pursues this subject further byemphasizing that each data managementfunction must account for what DAMA
� Enterprise Data Modeling� Value Chain Analysis� Related Data Architecture
DataArchitectureManagement
� Analysis � Data Modeling� Database Design� Implementation
Data quality must beconsidered integralto any IT environment.- Gartner
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Data Governance Part III: Frameworks – Structure for Organizing Complexity6
calls environmental elements. These arepresented in Figure 3.
DAMA’s Data Management Body ofKnowledge (DMBOK) provides a textualfunctional decomposition of the datamanagement functions. There are 10functions and 102 activities. Each activityis categorized as belonging to one of fourActivity Groups:
Planning ActivitiesActivities that set the strategic and tacticalcourse for other data management activi-ties. Planning activities may be performedon a recurring basis.
Control ActivitiesSupervisory activities performed on an on-going basis.
Development ActivitiesActivities undertaken within projects andrecognized as part of the systems develop-ment lifecycle (SDLC), creating datadeliverables through analysis, design,building, testing and deployment.
Operational ActivitiesService and support activities performedon an on-going basis.
Clear strategic intent is established foreach function according to the environ-mental element Goals and Principles. Eachfunction is further described using acontext diagram. Suppliers, inputs,consumers, outputs, metrics and othercomponents are presented as followsusing the function Data Governance as anexample functional context diagram (seeFigure 4).
Governance then touches every aspect ofdata management as demonstrated by itsplacement in the center of the DAMAframework of data management functions(see Figure 5).
The Data Governance Institute(DGI)
In order to understand the DGI framework,it is helpful to understand the underlyingconceptual relationship among business
� Recognized Best Practices � Common Approaches� Alternative Techniques
Practices & Techniques
� Individual Roles� Organizational Roles� Business & IT Roles� Qualifications & Skills
Roles & Responsibilities
� Phases, Tasks, Steps � Dependencies� Sequence & Flow� Use Case Scenarios� Trigger Events
Activities
� Inputs & Outputs � Information� Documents� Databases� Other Resources
Deliverables
� Vision & Mission � Business Benefits� Strategic Goals� Specific Objectives� Guiding Principles
Goals & Principles
FIGURE 3: DAMA Environmental Framework
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Data Governance Part III: Frameworks – Structure for Organizing Complexity 7
FIGURE 4: DAMA Context Diagram for the Governance Function
FIGURE 5: DAMA Worksheet – How the Two Frameworks Relate
Environmental Elements
Data Management Functions Goals &Principles
Activities Deliverables Roles &Responsibilities
Technology Practices &Techniques
Organization &Culture
Data Governance
Data Architecture Management
Data Development
Database OperationsManagement
Data Security Management
Reference & Master DataManagement
Data Warehousing & BusinessIntelligence Management
Document & ContentManagement
Meta Data Management
Data Quality Management
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Data Governance Part III: Frameworks – Structure for Organizing Complexity8
functions, information and technologythat is promoted by the DGI.
DGI has observed that the importance ofdata governance has become a high prior-ity for the business. Some organizationshave moved “information management”functions out of information technologyorganizations and into the business side.The business side then has responsibilityfor managing information in order toachieve its strategic intent. Technology isthen engaged to assist in the manage-ment of that information. However,management of information is viewed asprimarily a business responsibility. Theserelationships are demonstrated by theVenn diagram presented. Information isthe linchpin between the business and IT.
Data governance is not viewed as an endin itself. Data governance is required toensure data quality which contributestoward effective decision making and deliv-ery of quality services to citizens. This is theoutcome that should be presented inmaking the business case for a data gover-nance initiative in state government.
DGI makes a strong point that informationtechnology (IT), and data governance onlyexist to assist the business in managinginformation. The capability to manageinformation enables strategic businessintent.
The DGI framework for data governance(see Figure 7) presents major componentsand also a process or sequence fornavigating through the framework.6 It isrecommended that the 11” x 17” DGIposter be downloaded as a reference.7
This document provides detailed descrip-tions of the components and presents anintended sequencing that answers theinterrogatives, Who/What/When/Where/Why/How.
DGI presents the lifecycle of data gover-nance intended to ensure the propersequence of activities are followed so thata data governance initiative not only isinitiated for the right reasons, but is alsoable to sustain itself (see Figure 8).
From the Data Governance Institute, welearn the necessity of establishing a focus
FIGURE 6: Key Responsibilities in Data Governance
NASCIO: Representing Chief Information Officers of the States
Data Governance Part III: Frameworks – Structure for Organizing Complexity 9
area—at least initially. There is a need tobalance the longer term vision asdescribed by the data governance maturi-ty models8 with the need to delivermanageable, carefully scoped outcomes.Large initiatives don’t typically work on afirst launch. Support will wane withoutimmediate or short term outcomes thatdemonstrate value for the initiative. Evenwhile a large initiative is working to createan effective operating discipline, govern-ment continues to create even more dataand information. Collaboration amongstate agencies requires the developmentof short term to medium term solutions.
The surrounding rationale for the DGIFramework is consistent with NASCIO’sapproach to enterprise architecture and
governance. As presented in the NASCIOEnterprise Architecture Value Chain. Themotivation and strategic intent drives theidentified need for data governance.
DGI spends considerable effort in explor-ing universal drivers and focus areas forestablishing intent and scope (see Figure9). These must be established first beforemoving into other aspects of the frame-work.
Organizations need to establish a gover-nance approach, or process that clearlydescribes the rules of engagement formanaging data. The DGI Framework isintended to provide the followingoutcomes for a data governance initiative:
FIGURE 7: DGI Data Governance Framework
FIGURE 8: 7 Phases in the Data Governance Life Cycle
FIGURE 9: Universal Drivers for any Data Governance Initiative
10
NASCIO: Representing Chief Information Officers of the States
Data Governance Part III: Frameworks – Structure for Organizing Complexity
� Achieve clarity� Ensure value from efforts� Create a clear mission� Maintain scope and focus� Establish accountabilities� Define measurable successes
The supporting process for the DGIFramework initiates through the definitionof a focus area. However, all data gover-nance initiatives will share a common setof “universal goals” (See Figure 10).
Effective data governance initiatives willencompass one or more focus areas. Theprogram design will then be tuned to
address those specific focus areas thatcharacterize the original impetus forestablishing data governance. DGIdescribes the following focus areas. Thespecifics of the data governance charterwill then depend on what the enterpriseestablishes as focus areas (See Figure 11).
These focus areas will then provide signifi-cant influence on the shaping of the datagovernance initiative.
DGI goes on to describe 10 “universalcomponents” of any data governanceprogram that are organized into threemajor groupings.
Specific data governance policiesare dependent uponthe focus of the datagovernanceprogram.
FIGURE 10: UNIVERSAL GOALS FOR DATA GOVERNANCE
11
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Data Governance Part III: Frameworks – Structure for Organizing Complexity
FIGURE 11: DGI Data Governance Focus Areas
FIGURE 12: DGI Data Governance Relationships
NASCIO: Representing Chief Information Officers of the States
Rules and Rules of EngagementThese describe rules in terms of policies,requirements, standards, accountabilitiesand controls. Rules of engagement thendescribe how different groups will shareand delegate responsibilities for establish-ing these rules and executing on them.
1. Mission and vision2. Goals, governance metrics and
success measures, and funding strate-gies
3. Data rules and definitions 4. Decision rights5. Accountabilities6. Controls
People and OrganizationThis component describes how datagovernance will be organized, and whatroles and responsibilities will be defined.
7. Data stakeholders 8. A data governance office9. Data stewards
ProcessesThese are the processes, methods andprocedures for creating and maintaining asustained effort in data governance.
10. Proactive, Reactive, and Ongoing DataGovernance Processes
DGI makes the following recommenda-tions regarding initiating a datagovernance program:� Data governance programs are unique
to each organization. Do not assume adata governance program from anoth-er organization can be simply adopted.
� Leverage existing governance disci-plines. Examples include:� IT Governance� Records Management� Change Management
� Beware of simply putting togetherdata stewards and rules. This approachwill not be successful. This is why theDGI framework was designed with aninherent sequence. Follow thesequence to ensure success.
� Configure your program out of thethings that are already working in yourorganization. This will be moresuccessful than establishing a separate,completely new program.
� Identify and understand the obviousand the not so obvious stakeholders.Identify all stakeholders such asdownstream users of information, webdevelopment teams, developers oftaxonomies, records management, dataarchitects, etc.
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NASCIO: Representing Chief Information Officers of the States
Data Governance Part III: Frameworks – Structure for Organizing Complexity
IBM Data Governance CouncilFramework
This framework is also presented withadditional detail in the NASCIO researchbrief on data governance maturity modelsand forms the basis for the IBM DataGovernance Council maturity model.9 Thisframework presents major concepts thatcomprise not only governance but also anenterprise data management practice.10
Major dependencies are presented acrossgroupings of functions. The functionspresented compare well with the DAMAfunctional framework for data manage-ment.
The IBM Data Governance CouncilFramework was designed to be outcomeoriented. Risk Management, compliance,and value creation are seen as desirableoutcomes of a data governance program,even though they may also be daily opera-
tional activities and present policychallenges. The focus in this framework ison organizational behavior based on anunderlying premise that only people canbe governed and not the data itself per se.Therefore, organizational structures, policyand stewardship are functional require-ments to affect organizational behaviorover the core disciplines of data quality,information lifecycle management (ILM),security and privacy, data architecture,metadata, and, audit and reporting.
Most members of the Data GovernanceCouncil who have used the Frameworkhave done so with a six step approach:
1. Build the organizational structures2. Assess the current situation3. Create an operational charter4. Develop data stewardship5. Measure progress with key metrics6. Report results
Data Risk Management & Compliance
Value Creation
Outcomes
Enablers
Core Disciplines
Supporting Disciplines
Enhance
Requires
Supports
Organizational Structures & Awareness
Policy
DataQuality
Management
InformationLife-Cycle
Management
InformationSecurity
and Privacy
DataArchitecture
Classification &Metadata
Audit InformationLogging & Reporting
Stewardship
FIGURE 13: The IBM Data Governance Council Framework
Elements of Effective Data Governance
NASCIO: Representing Chief Information Officers of the States
Build the Organizational StructuresThe first step in any successful data-gover-nance program is identifying an individualwithin the organization who carries thedelegated authority of executive manage-ment and making that person accountableto make things happen. There is no substi-tute for strong leadership.
Data governance is a political challengethat requires building consensus amongmany diverse stakeholders. Political leader-ship within the organization is therefore apriority. Once established, that leadershipcan create a governing council composedof organizational stakeholders to formu-late stewardship policies and reportprogress to executive management.
Some members have used a processsimilar to the one shown in Figure 14 toimplement the IBM Data GovernanceFramework.
There are four key components in thismodel:
1. The Data Governance Council – Thecouncil is the place where crossorganizational issues get raised,assessed, and policy decisions aremade. The make-up of the council canvary in size and seniority, but it shouldbe cross functional to be effective. Itcan include representatives from thesix core and supporting disciplines inthe IBM Data Governance Framework.It should include representatives fromstate agencies, operations and humanresources.
2. Data Stewardship Roles and Resources– Stewards execute policies andsurface organizational issues. Withoutan active data stewardship program, adata governance council has nomeans to implement policy. In manyorganizations the stewardship tasksare performed informally. The IBMData Governance Council approachestablishes a formality and givesrecognition to these roles.
FIGURE 14: Process for Implementing the IBM Data Governance Council Framework
14 Data Governance Part III: Frameworks – Structure for Organizing Complexity
NASCIO: Representing Chief Information Officers of the States
3. Organizational Charters – A charter is aconstitution of powers, and it enumer-ates: � how the data governance council
and stewardship communitiesinteract
� how often meetings are held� what constitutes a quorum for
votes� funding
The charter may contain a logicalorganizational model to show howgroups interact within the data gover-nance function. A logicalorganizational model is not intendedto represent or align to formal report-ing relationships.
4. An Issue Triage Process – defines howissues are raised, assessed, discussed,and resolved. This is a key element inthe charter, but also has processdependencies that require greaterdetail.
This approach provides a streamlined setof processes that are easy to replicate andcover common organizational structures.It doesn’t matter if this is done on adepartmental, divisional, or enterprise
level. It also doesn’t matter if the subjectarea is one data subject or all data subjectareas. The approach is scalable and willgrow as the governance initiative grows.The same steps and processes are equallyuseful.
Assessing the Current SituationWith a solid organizational infrastructure,the next step is to setup an assessmentprocess. Benchmarking data governancecapabilities at the start of the program isnecessary in order to understand wherethe organization is initially in terms oforganizational practices and where itwants to go. The Data Governance CouncilMaturity Model is a good tool for this, butassessments shouldn’t be used just on amacro basis. Data governance may exist atvarious levels of maturity within certainsubject areas, departments and agencies.
Normalizing the assessment process forindividual governing issues is an impor-tant part of issue triage. Issue triage is anarbitration process to inject objectivityinto the governance decision-makingprocess. An issue triage process should beemployed to bring new issues andchallenges to the data governance boardor council. Issues and challenges must be
15Data Governance Part III: Frameworks – Structure for Organizing Complexity
FIGURE 15: Example of a Data Governance Scorecard
Good governancepractices can help[organizations]recognize where toinvest in data qualityand content manage-ment programs, tomaximize the valuethat they can create.- Gartner
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16 Data Governance Part III: Frameworks – Structure for Organizing Complexity
assessed consistently and fairly usingcommon methods so that stakeholdershave the opportunity to participate. Thiswill ensure that decision-making is fair anddemocratic.
Create an Operational CharterDemocracies function best with a consti-tution, because writing down roles andenumerated powers is the best way to setboundaries and ensure consistentoutcomes. The charter should delineatethe functions for which the data gover-nance council has jurisdiction, how manymembers from each agency or depart-ment are represented, rules of delegationand substitution, how often meetings arecalled and what constitutes a quorum forvotes. But the most important aspects ofthe charter deal with the three fundamen-tal powers of the data governance council:
1. The power to subsidize projects withfunding
2. The power to veto bad things 3. The power to implement policy with
stewardship
Without written operational and function-al responsibilities, a data governancecouncil won’t serve with a commonpurpose and won’t garner the organiza-tional respect it needs to governeffectively.
Developing Data StewardshipData Stewardship is an organizationalbehavior. Data stewardship recognizes thecustodial obligations that everyone sharesto manage state government dataresources effectively. However, at this earlydate few people understand this behaviorand so data stewards are needed topromote and implement data best practicesthroughout the organization. A datasteward is a policy implementer, someonewho integrates policy into businessprocesses, data structures, applications,and new business entities. It is a role andresponsibility that needs to be developedover time, eventually leading to a commu-nity of data stewards.
These people can not be found on job
boards. The role can not be outsourced tovendors. These are the people who knowthe organization well from both businessand IT perspectives. They are detail orient-ed and have excellent personalrelationships with both IT and businessmanagers. These are the doers who notonly enforce data governance policies andcoordinate change, but also identify keyissues and bring them to the data gover-nance council when they require triage.Typically these people can be identified byasking the question “Who do I ask aboutsuch and such data?” The informalnetworks within any organization will leadto the right people.
Measuring Progress with Key MetricsEvery new data governance program willhave about 90 days to demonstrateprogress before losing political capital.Therefore, knowing what to measure is asimportant as knowing what should bedone, and how to do it. The DataGovernance Council Maturity Model hasmany key metrics across five levels ofmaturity that provide valuable bench-marks of organizational behavior. TheMaturity Model is intended for normalizedassessments during issue triage, trackingkey performance metrics for each issueand monitoring project and programprogress. Metrics are established for eachof the 11 elements presented in the IBMData Governance Council Framework.
Making these key metrics and progressstatistics available to the organization in adashboard or business intelligence appli-cation is a fundamental aspect of effectivegovernance. Especially in the beginning,transparency delivers huge benefits.Governance works best when it is openand available. Tracking progress andletting everyone know what is beingtracked is a powerful tool in affectingorganizational change. This will also assistin gaining not only participation, butownership in the data governance process.As has been described, literally everyone inthe organization must play a part in datastewardship. Access to these metrics willassist in achieving that perspective.
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17Data Governance Part III: Frameworks – Structure for Organizing Complexity
FIGURE 16: Example of a Data Governance Normalized Metrics
FIGURE 17: Example of a Data Governance Dashboard
NASCIO: Representing Chief Information Officers of the States
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Report the ResultsReporting is a political tool to eitherreward correct behavior or threaten badbehavior. The data governance programmust be on a regular reporting cycle withboth the CIO and the Legislative AuditCommittee. Reporting should present anormalized report that illustrates bothprogram progress in terms of maturity aswell as project progress in terms of Good,Fair, and Poor. Reporting is also a tool tomonitor stewardship progress on projecttasks around data quality, ILM, security &privacy, metadata, etc. Report recipientsmay be project leadership, executives,architects, and boards.
Having a Data Governance Strategy Map isan effective tool for monitoring DGprogram status at both high and lowlevels.
A Data Governance Framework is a graphi-cal illustration of many complex politicalprocesses. Data is a strategic asset thatcan’t be governed per se. Only people canbe governed, because only people candecide to enhance and protect data,respect custodial obligations, and throughtheir stewardship improve the organiza-tion. But accomplishing these goalsrequires more than charts and programs.It requires using common tools andprocesses, benchmarks and metrics,reports and charters. And - lots of practice.
The IBM Data Governance CouncilFramework is both a benchmark and abest-practices guide developed by over 50organizations working together andsharing experiences. Implementation willvary from state to state as there is no onesize fits all approach to data governance.However, the six common steps enumerat-ed above can help any state governmentdata governance program get off to agood start and lay the foundation forsustainable success. In the end, however,the success of data governance is depend-ent on leadership. The best frameworks inthe world will not compensate for poorleadership. Nor will the best leaders beable to lead well for long without a consis-tent framework to guide behavior. Good
leaders and good frameworks can bringenormous benefits to state organizationslooking to reap the opportunities of effec-tive data governance.
Similar to DAMA’s Data Management Bodyof Knowledge (DMBOK), the IBM frame-work makes a strong point regarding theneed for valuing data and informationassets. IBM goes further to make the pointthat enterprise value is a determinedoutcome of data governance and datamanagement. The concepts of riskmanagement and compliance are present-ed and create an additional motivation foran effective data governance initiative.IBM includes the concept of InformationLife-Cycle Management defined as asystematic policy-based approach to infor-mation collection, use, retention, anddeletion. Refer to NASCIO’s report “DataGovernance Part II: Maturity Models – APath to Progress” for more detail on thismodel.
Summary
Frameworks are a necessary ingredient inplanning and executing on an enterprisedata governance program in state govern-ment. The frameworks presented shouldbe used in organizing concepts and estab-lishing the components of a datagovernance initiative and data / informa-tion management.
The full operating discipline for datagovernance will entail the use of maturitymodels, frameworks, process, and organi-zation. Underpinning any such endeavormust be a strong business need or opportu-nity. Enterprise wide initiatives that arenot built on a specific near to mediumterm deliverable will not succeed. Short tomedium term “wins” must be achieved tomaintain motivation. However, data /knowledge management must also beunderstood as a long term initiative thatconstitutes more than a cost of doingbusiness. There is a strategic aspect tomanaging data and knowledge assets thatis highly proactive.
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Data/Knowledge management has bothstrategic and operational elements. It ispossibly the most important strategic andoperational capability an enterprise canpossess. Some knowledge assets will gainvalue over time. Others will depreciate tolittle value. State government knowledgeassets must be continually evaluated forvalue. Such assets must be available so thevalue can be harvested. Knowledge that ismerely kept only has potential value. Itmust be accessible to generate realbenefits to state government. Knowledge
5. Identify all stakeholders, some ofwhom may not be obvious. Enlisttheir assistance in making the case fordata governance.
6. Exploit the best ideas that are alreadyin place within state agencies. Bringthese ideas into state wide initiatives.Include ideas from local government,academia and industry.
7. Ensure new programs and projectsmaintain compliance with the statedata governance standards.Contracts, MOUs, cooperative agree-ments, service level agreements allneed to maintain such compliance sothe state data governance programdoes not erode over time.
8. Continue to work with NASCIO andother state government associationsto influence the federal governmentfunding process to move away fromstove-piped funding. Funding needsto support enterprise data gover-nance initiatives in order to continuebuild toward more effective andefficient government.
9. In evaluating service offerings andcloud computing, understand theimplications and assurances formanaging data / knowledge assets.
Data Governance Part III: Frameworks – Structure for Organizing Complexity
assets must be protected from unautho-rized access and use so that it can betrusted and used to generate value for thetrue owners.
NASCIO will continue to explore thesubject of data governance with addition-al events and publications that will focuson organization and process.
Calls to Action
1. Adopt one or more frameworks formanaging a state data governanceprogram. The framework should becomprehensive. Use of the frame-works in this report are highlyrecommended.
2. Establish a focus area and businessinitiative for driving a data gover-nance program. Identify and focus ona business problem to be solved or abusiness opportunity to be harvested.Enterprise initiatives without abusiness problem or issue will notsucceed.
3. Leverage existing governance tolaunch and sustain data governanceprocesses. Continue to encouragecross-line-of-business collaborationon data governance.
4. Data governance should not bepresented as an overhead project.Make the case for data governancebased on real outcomes sought suchas:� Improved decision making based
on improved data quality.� Greater value gained in service
delivery to citizens.� Knowledge management as a
strategic weapon for gainingcompetitive advantage, orefficiently meeting regulatoryrequirements.
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Appendix A: Acknowledgements
Steven Adler, Program Director of IBMData Governance Solutions
Micheline Casey, Director, IdentityManagement, State of Colorado
Robert Culp, Alliance Manager, ESRIStrategic Alliance, IBM
Learon Dalby, GIS Program Manager, NSGICPresident, The State of Arkansas
Michael Fenton, Director of EnterpriseArchitecture, The State of North Carolina
Stephen Fletcher, Chief InformationOfficer, State of Utah, Co-Chair of theNASCIO Enterprise ArchitectureCommittee
Michael J Hammel, Enterprise Architect,Commonwealth of Virginia
Deborah Henderson, President DAMAFoundation, VP Education and ResearchDAMA International
Christopher Ipsen, Enterprise Architect,State of Nevada
Dugan Petty, Chief Information Officer,State of Oregon, Co-Chair of the NASCIOEnterprise Architecture Committee
Doug Robinson, Executive Director,NASCIO
Bill Roth, Chief Technology Architect, TheState of Kansas
Dr. Anne Marie Smith, Principal Consultant,Director of Education, EWSolutions, Inc.
Glenn Thomas, Director of DataArchitecture, Commonwealth of Kentucky
Gwen Thomas, President, The DataGovernance Institute
Tom Walters, Division of Data Architecture,Commonwealth of Kentucky
Chuck Tyger, Associate Director EnterpriseArchitecture, The Commonwealth ofVirginia
Chris Walls, Senior Website & PublicationsCoordinator, AMR Management Services
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Appendix B: Resources
NASCIO www.nascio.org
IT Governance and BusinessOutcomes – A SharedResponsibility between IT andBusiness Leadershiphttp://www.nascio.org/committees/EA/download.cfm?id=98
Data Governance – ManagingInformation As An EnterpriseAsset Part I – An Introductionhttp://www.nascio.org/committees/EA/download.cfm?id=100
Data Governance Part II: MaturityModels – A Path to Progresshttp://www.nascio.org/committees/EA/download.cfm?id=111
Enterprise Architecture: The Pathto Government Transformationhttp://www.nascio.org/committees/EA/
Call for Action, A Blueprint forBetter Government: TheInformation Sharing Imperativehttp://www.nascio.org/advocacy/dcFlyIn/callForAction05.pdf
In Hot Pursuit: AchievingInteroperability Through XMLhttp://www.nascio.org/publications/index.cfm#21
We Need to Talk: GovernanceModels to AdvanceCommunications Interoperabilityhttp://www.nascio.org/publications/index.cfm#50
A National Framework forCollaborative InformationExchange: What is NIEM?http://www.nascio.org/publications/index.cfm#47
List of NASCIO Corporate Partnershttp://www.nascio.org/aboutNascio/corpProfiles/
List of NASCIO Publicationshttp://www.nascio.org/publications
List of NASCIO Committeeshttp://www.nascio.org/committees
The Data Administration Newsletterhttp://www.tdan.com/index.php
Presents 8 chapters that describehow to implement data gover-nance
The Data Governance Institutehttp://datagovernance.com/
DGI created a poster on datagovernance that can bedownloaded, or ordered inhardcopy online.
The Data Management AssociationInternational – DAMA – www.dama.org
The Data Management Body ofKnowledge (DMBOK) -http://www.dama.org/i4a/pages/index.cfm?pageid=3364
The IT Governance Institute (ITGI)http://www.itgi.org/
Information Systems Audit and ControlAssociation (ISACA)http://www.isaca.org/
Certification in Governance ofEnterprise IT (CGEIT) from ISACAhttp://www.isaca.org/Template.cfm?Section=Certification&Template=/TaggedPage/TaggedPageDisplay.cfm&TPLID=16&ContentID=36129
The Center for Information SystemsResearch (CISR)http://mitsloan.mit.edu/cisr/
The National Information ExchangeModel (NIEM) www.niem.gov
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Global Justice Reference Architecturefor SOAhttp://www.it.ojp.gov/topic.jsp?topic_id=242
Global Justice ReferenceArchitecture (JRA) Specification:Version 1.7http://www.it.ojp.gov/documents/JRA_Specification_1-7.doc
The Global Justice ReferenceArchitecture (JRA) Web ServicesService Interaction Profile Version1.1http://www.it.ojp.gov/documents/WS-SIP_Aug_31_version_1_1_FINAL(3).pdf
The Global Justice ReferenceArchitecture (JRA) ebXMLMessaging Service InteractionProfile Version 1.0http://www.it.ojp.gov/documents/ebXML_SIP_v01_Final_Version_100407.pdf
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Appendix C: Endnotes
1 NASCIO report “PERSPECTIVES –Government Information Sharing: Calls toAction”, available at www.nascio.org/publi-cations.
2 Casonator, R., Friedman, T., “EnterpriseInformation Management Is StillMisunderstood by Many”, Gartner, June 9,2008, Gartner ID Number: G00158458.
3 NASCIO report “Data Governance –Managing Information As An EnterpriseAsset Part I – An Introduction”, available atwww.nascio.org/publications.
4 NASCIO report “Data Governance Part II:Maturity Models – A Path to Progress,available at www.nascio.org/publications.
5 The Data Management Body ofKnowledge, DAMA International, 2009,ISBN 978-0-9771400-84, LOC No.2008912034.
6 “How to Use the DGI Data GovernanceFramework to Configure Your Program”,The Data Governance Institute, retrievedon May 7, 2009, from http://www.datagovernance.com/whitepaper_abstracts.html
7 See The Data Governance Institutehttp://datagovernance.com
8 See Data Governance Part II
9 NASCIO report “Data Governance Part II:Maturity Models – A Path to Progress”,available at www.nascio.org/publications.
10 IBM Data Governance Council MaturityModel, October 2007, retrieved on May 12,2008, from http://www935.ibm.com/services/uk/cio/pdf/leverage_wp_data_gov_council_maturity_model.pdf
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