Data Governance: Building a Framework for MDM

Data Governance:Building a Framework for MDM

Aaron ZornesChief Research OfficerThe CDI-MDM Institute

Daniel TeacheyCorp. Communications Director DataFlux

Data Governance

“84% of businesses surveyed believe that poor data governancecan cause: limited user acceptance, lower productivity, reduced

business decision accuracy, & higher TCO”


The-CDI-MDM-Institute.com a.k.a. tcdii.com

Building a Framework for MDM

© 2007 The CDI-MDM Institute The-CDI-MDM-Institute.com

Working DefinitionsData Governance (DG) Data Governance (DG)

Customer Data Integration (CDI) Customer Data Integration (CDI)

Processes & technologies for recognizing a customer & its

relationships at any touch-point while aggregating, managing &

harmonizing accurate, up-to-date knowledge about that customer to

deliver it ‘just in time’ in an actionable form to touch-points.

Formal orchestration of people, process, & technology to enable an organization to leverage data as an enterprise asset.

Master Data Management (MDM) Master Data Management (MDM)

The authoritative, reliable foundation for data used across

many applications & constituencies with the goal to

provide a single view of the truth no matter where it lies.

Data governance is a prerequisite to efficient & effectiveinformation integration & business intelligence –

two key differentiators in highly-competitive industries


Data GovernanceStrategic Planning Assumption

� During 2007, enterprise-level data governance will bemandated as a core deliverable of large-scale CDI-MDM projects delivered via RFPs

� Through 2007-08, major systems integrators & CDI-MDM boutiques will focus on productizing their datagovernance frameworks while most CDI-MDMsolution providers will struggle to link businessprocess design with process hub architecture

� By 2008-09, both corporate & LOB data stewards willbe a common position as Global 5000 enterprisesformalize this function amidst increasing de facto &de jeure recognition of information as a corporateasset.

CDI-MDM MILESTONE


Why Data Governance? Why Now?� Businesses have been governing data for 20+ years,

however, only a rare few are doing it well today� Many companies historically assigned DG to a data

management group whose job is to integrate &manage data

� Contemporary DG challenges are far greater� Break down functional stovepipes� Integrate processes across the enterprise –

including corporate technology, all LOBs,functional areas & geographic regions

� Engage all levels of managementBased on recognition of issues at hand, an improving economy, &increasing regulatory requirements, businesses are now recognizingthe opportunity to take a more strategic view of data governance


Why Data Governance? Why Now? – cont’d

� Once you know what data is worth, you need to calculateprobability for risk in a business processes

� When you understand value of data & probability of risk, youcan evaluate how much to spend to protect it, manage it,and invest in adequate controls

� This is basis of modern underwriting – assets, risk, controls� Doing this systemically requires a combination of

organizational structures, business processes, & technology– a “data governance blueprint” for:� Data quality� Information integration� Business intelligence

IT management must work with business leadership to design &refine “future state” business processes associated with datagovernance commitments


Data Governance Maturity Level

Overall, Financial Services Providers are leading the way for non-FSPs

� BASIC (“anarchy”) – App-centric approach; meets business needs only on project-specificbasis

� FOUNDATIONAL (“IT monarchy”) – Policy-driven standardization on technology &methods; common usage of tools & procedures across projects

� ADVANCED (“business monarchy”) – Rationalized data with data & metadata activelyshared in production across sources

� DISTINCTIVE (“Federalist”) – SOA (modular components), integrated view of compliancerequirements, formalized organization with defined roles & responsibilities, clearly definedmetrics, iterative learning cycle

0%

10%

20%

30%

40%

50%

Basic Foundational Advanced Distinctive

FSP

Non-FSP

Source: 2006 -07 CDI-MDM Institute survey of 50 Global 5000 IT organizations


Data Governance Challenges

� Adjudicating betweencentralized vs.decentralized datastewardship

� Evolving key stakeholdersfrom “data ownership” to“data stewardship”

� Upgrading notion of “data”to “policy/process”

Data quality-enabled information integration is fundamentalto ability to "leverage data as an enterprise asset”

BusinessExec

Dept’lExec

Dept’lExec

Dept’lExec

CIO

ChiefData

OfficerVP ofInfra-

structureVP ofAppli-

cations

Data GovernanceSteering Committee

CorporateData

StewardBU

DataSteward

BUData

Steward

Data GovernanceIn Practice


Data Governance EnablesEnterprise Information Integration

PrivacyPrivacyPreferencesPreferences

PricingPricingBundlesBundles

CompatibilityCompatibility

LegacyLegacy

Shipping Shipping ApprovalsApprovals

EligibilityEligibility

Upgrade/Upgrade/DowngradeDowngrade

Next Best Next Best OfferOffer

DiscountDiscountPoliciesPolicies

Process /Process /Policy HubPolicy Hub

Methodology is needed to bind process steps, skills &software to produce data governance deliverables


“Top 5” IT Drivers

67%

47%

60%

46%

47%

81%

63%

56%

56%

31%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Defining processes & business rules forongoing governance

Developing standard metadata mgmt

Building governance infrastructure, technology& supporting organization

Monitoring to improve data quality

Developing architecture best practices &standards

Non-FSP

FSP

IT organizations cannot “go it alone” –data governance experience & accelerators are needed

Source: 2006-07 CDI-MDM Institute survey of 50 Global 5000 IT organizations


Data Steward Residence & Specific Role

67%

47%

60%

46%

47%

81%

63%

56%

56%

31%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Defining processes & business rules forongoing governance

Developing standard metadata mgmt

Building governance infrastructure, technology& supporting organization

Monitoring to improve data quality

Developing architecture best practices &standards

Non-FSP

FSP

DS is a business-oriented function – concept is embraced, active, generallyproject-oriented & within the BU (not within IT ) – clearly, business isstepping up more on a project basis to take responsibility for the data



Survivorship of Trusted Sources

23%

19%

16%

13%

13%

0% 5% 10% 15% 20% 25%

Rules engine of data integration tool

Rules engine of enterprise CRM suite

Not applicable

Rules engine of legacy system

User-originated master data update

Rules themselves are in many different places & need to be extractedout into horizontal layer & then replicated back to individual units –this is “true data governance-driven MDM” in the long run



Basic Data Governance Key Steps

� Establish formal data governance process that mapsto the business architecture

� Differentiate between data stewardship & ownershipwhile defining, staffing & communicating

� Define & continuously measure adequate, agreed-upondata quality metrics

� Design/select, & implement appropriate architecturecomponents to improve data quality

� Empower & hold accountable “up stream” datastewards for their domains

Formalize the treatment of “data” as a corporate asset


Recommendations� Data governance is difficult – enterprises need to

embrace a blueprint for success� IT management must understand impact of data

governance on enterprise information integration plans� All businesses embrace political structures� Data governance is necessary to support politics of both

centralized & distributed information integration� Without firm commitment to corporate data governance,

BU-specific data governance projects run risk of becoming“yet another master data mart”

� Savvy IT organizations begin with data stewardship &business metadata definition

Data governance is a key enabler for data quality-driveninformation integration –

IT management should get “on message”


Authoritative

RelevantIndependent

Aaron ZornesFounder & Chief Research Officer

The CDI-MDM InstituteThe-CDI-MDM-Institute.com a.k.a.

www.tcdii.com


Daniel TeacheyDataFlux

About DataFlux

� Recognized as a leading provider of data quality and data integration solutions– Intelligent Enterprise Readers’ Choice Award, 2006– CRM magazine “Market Winner” in Data Quality, 2006– CTO and co-founder Scott Gidley named an “Innovator to

Watch in 2006” by Infoworld magazine

� Acquired by SAS, the world’s largest privately-owned software company, in 2000.

Data Quality is the Foundation

� Master Data Management– Unify your data to create a single view of enterprise information.

� Data Governance– Enforce business standards and rules across the enterprise.

� Compliance– Meet enterprise, industry and government regulations.

Data Management Landscape

Corporate Data Governance

Enterprise Data Maturity Model

Our Approach

Data Quality Integration Platform

Metadata DiscoveryData Profiling

Data QualityData IntegrationData Visualization

Data MonitoringData Enrichment

Solutions

Accelerators

Customer Data Product Data

Data Monitoring: Key for Data Governance

� Create business rules and enforce them in batch or real time– Business rules can be simple…

• If address is invalid, email an admin)

– …or more complex • If loan-to-value ratio in table A is .8 or above and table B does not show a PMI,

then email an admin and enact a corrective routine

� Data monitoring options:– Audit – Measure data quality over time– Trend – Monitor new data as it enters a data store– Alert – Notify user or application when user-defined rule is broken

� Data monitoring is critical for data governance initiatives– Support the codification and reuse of business policies– Provide a way to ensure that data meets business standards

Data Governance SuccessMortgage Provider

Business Issue Existing data quality controls developed and maintained by programmers. Rules duplicated across different programs and different groups. No central repository of rules. Very difficult to control internal processes.

Mission Meet standards for data quality across the enterprise. Implement reusable data governance controls to monitor data movement, business data quality, accounting, etc.

Results Established a “use once, reuse repeatedly” model to centralize all business rules for data quality. Monitored internal sourcesto validate the quality of the business processes used to run the business. Generated better reports to federal and industry agencies.

Data Governance SuccessChemicals Company

Business Issue � Enforce business policies for product and inventory classifications (reduce manual rework and execute automatic consistency checks)

� Consolidate data to a “master data” source. Needed to analyze, improve and validate data quality along the way

Mission Meet or exceed data quality goals across the enterprise. Implement reusable data governance controls to monitor data movement, business data quality, accounting, etc.

Results Established a “use once, reuse repeatedly” model to centralize all business rules for data quality. Monitored internal sourcesto validate the quality of the business processes used to run the business. Generated better reports to federal and industry agencies

Monitor Results

Data Governance Dashboards



Daniel TeacheyCorp. Communications Director DataFlux

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