Getting Started with Data Governance. Philip Russom TDWI Research Director, Data Management June 14, 2012

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1 Getting Started with Data Governance Philip Russom TDWI Research Director, Data Management June 14, 2012

2 Speakers Philip Russom Director, TDWI Research Daniel Teachey Senior Director of Marketing, DataFlux 3

3 Agenda Background Defining Data Governance (DG) Balancing Competing DG Goals Success Factors when Starting DG Successful Starting Points for DG Consolidating competing DG programs Sponsorship and mandate Organizational Structures for DG programs Success Factors in 2nd Phase DG Moving up to the Next Level of DG Scaling up DG with Software Automation Recommendations 4

4 Common DG Starting Points Definitions of Data Governance A definition covering most of the components of DG Data governance (DG) is usually manifested as an executive-level data governance board, committee, or other organizational structure that creates and enforces policies and procedures for the business use and technical mgt of data across entire organization Business Goals for DG Mostly control of data access Compliance with internal & external regulations for data usage Reduce risk exposure relative to data and its use Technology Goals for DG Mostly improvements to data & its mgt Establish and enforce standards for data Improve data s quality; remediate its inconsistencies; share data broadly; manage change relative to data usage A nutshell definition that s easy to remember Data governance is an organizational structure that oversees the broad use (compliance) and usability (standards) of enterprise data 5

5 Competing DG Programs Starting Points for DG Compliance (regulatory) Compliance (privacy) Standards (DQ, MDM) Standards (BI, DW) Multiple DG Programs Each w/own Model Own process, standards, workflow, staffing, etc. Instead of Competing Consolidate efforts, human resources, executive clout Reconcile & balance competing goals DG for Compliance (often Top-Down from Biz) Originally driven by Biz People Goals: regulatory, privacy, risk Consolidation of DG Programs Single team, process Single model, ent scope DG for Data Standards (often Bottom-Up from IT) Originally driven by IT People Goals: DQ, MDM, BI, DW 6

6 Collaborative DG Leads to Success, but Requires Balance Two areas of collaboration: Technology coordinating efforts of multiple data mgt teams Business communicating enterprise direction, plus how data can support it Both areas are valuable as independent endeavors. There s additional value in bringing them together: Priority Order business goals should lead, followed by support from data mgt. Determine & communicate biz goals & their data requirements Align data mgt work to business goals Coordinate diverse data mgt teams & solutions Collaborative Data Governance 7

7 Organizational Structure Sustains Data Governance Data Governance Committee Like a Steering Committee, but more permanent Data Governance Board Like an Advisory Board, but producing documented policies Competency Center Primary focus could be BI, data integration, data mgt, etc. Data governance is a secondary function Data Management Teams Data quality and stewardship Business Intelligence and Data Warehousing Enterprise Data Architecture (EDA) Group Database Administration (DBA) Group 8

8 Committee Membership Sustains DG Committee Chair must be a strong person A biz sponsor with a mandate or an individual with passion and drive Cochairs address DG s inherent dualities One chair is high placed executive with big stick; other gets work done One chair represents business; other represents IT or data mgt Directors dominate DG Leadership Half of committees include BI or DW Director; quarter IT Director Directors have clout, but are not alienated from data work Data Management Professionals are the bulk of committee members Priority order: BI, DW, DQ, stewardship, EDA, biz analyst, DI, DBA Only 10% of DG boards include anyone from content mgt Line-of-Business (LOB) Managers are common contributors They know how data affects the business better than anyone They often know what compliance means to operational details Chief Officers sit on about a third of DG committees Priority order: CIO/CTO, CFO, compliance officer; rarely CEO 9

9 SURVEY SAYS: Executive mandate is crucial in early stages. 68% of survey respondents pointed to data ownership and other territorial issues as the leading barriers to data governance success. SOURCE: TDWI survey Overcoming these barriers requires a strong and attentive executive sponsor. 10

10 Data Governance for Business Initiatives What types of business initiatives do you think should be guided by data governance? (Select all that apply.) BI is the leading candidate for DG. Compliance issues are pressing, and DG can help. Business transformations transform data, too, so DG is required. Source: TDWI Survey, 273 respondents. Business intelligence Data privacy Compliance Business transformations Mergers and acquisitions Reorganizations Marketing campaigns 28% 26% 34% 47% 69% 66% 88% 11

11 DG for Data Management Implementations Which data management practices do you think should be guided by data governance? (Select all that apply.) Data integration and data quality go hand-in-hand with DG. Data integration Data quality 83% 82% Master data and metadata need DG, too. DG can affect data models and data architecture. Source: TDWI Tech Survey, 117 respondents. Master data management Metadata management Data warehousing Enterprise data architecture Database administration 26% 56% 56% 75% 74% Other 3% 12

12 The Three Pillars of DG as Starting Points 1 DG s Org Structures and Goals 3 - The Three Pillars of DG: Compliance Transformation Integration 2 DG s Role in Biz & Tech COMPLIANCE (internal & external) With regulations for: - HIPPA, SOX, Basel II - Data security - Data privacy, etc. To achieve this: - Limit data access - Improve data quality for accurate regulatory reports, etc. Enterprise Data Governance DG Board with a process consisting of people, policies, and procedures BUSINESS TRANSFORMATION Via changes in: - Organization structure - Business processes - System consolidations and data ownership, etc. To achieve this: - Enable change - Mandate change - Manage change BUSINESS INTEGRATION By supplying data for: - Business intelligence view of products, customers, financials - Business partners, etc. To achieve this: - Expand data sharing - Improve data quality for better decisions, customer relations, etc. Business Initiatives: Compliance, Security, MnAs, Reorgs, BI, CRM, etc. Data Management Practices: Data quality, integration, warehousing, MDM, etc. 13

13 Why are the Three Pillars of DG important? Each is a starting point: Audit paranoia forces firms to start with data compliance Establish DG before Biz Integration practices, like MDM or DQ Establish DG before Biz Transformations, like M&As or re-orgs Mature DG involves all three, regardless of starting point: Successful DG expands toward Enterprise Data Governance The three pillars draw the Big Picture. They help you see: Starting points, expansion phases, long-term growth and goals The whole, mature DG program that you will have some day Let the Three Pillars guide you: Where to start, where to grow, benefits, biz/tech initiatives to involve, aligning data mgt to biz COMPLIANCE (internal & external) With regulations for: - HIPPA, SOX, Basel II - Data security - Data privacy, etc. To achieve this: - Limit data access - Improve data quality for accurate regulatory reports, etc. Enterprise Data Governance DG Board with a process consisting of people, policies, and procedures BUSINESS TRANSFORMATION Via changes in: - Organization structure - Business processes - System consolidations and data ownership, etc. To achieve this: - Enable change - Mandate change - Manage change BUSINESS INTEGRATION By supplying data for: - Business intelligence view of products, customers, financials - Business partners, etc. To achieve this: - Expand data sharing - Improve data quality for better decisions, customer relations, etc. Business Initiatives: Compliance, Security, MnAs, Reorgs, BI, CRM, etc. Data Management Practices: Data quality, integration, warehousing, MDM, etc. 14

14 In early phases, Data Governance is mostly about the Four Ps People work together to establish and enforce policies E.g., which data is subject to governance, as well as the allowable access and usage of such data Procedures enable change management & collaboration E.g., reviewing and acting on requests for data access, improvement, and other changes People, policies, and procedures all combine to enable a larger DG process, which grows to reach most of an enterprise. Policies Data Governance Process Procedures People 15

15 Moving Up to the Next Level of DG First level is easy Few people to manage Loose DG process Few policies to police Governing few apps, databases, processes Next level is far harder Many people involved Firm DG process Many policies Governing far more apps, databases, etc Software helps scale up DG into enterprise scope ~18 to 24 months Later Lifecycle Stages Permanent Program Broad Goals Enterprise Scope People & Process, plus Software Automation Early Lifecycle Stages Proof of Concept Localized Goals Dep t Scope Purely People Enterprise vs Local 16

16 SURVEY SAYS: Over half of users think software can automate DG processes. Is software automation for DG really possible? Over half of survey respondents said yes, a quarter said no, and the rest don t know. SOURCE: TDWI Best Practices Survey This indicates that some kind of software automation for DG is possible. TDWI s opinion is that some data governance tasks can be automated with software, and some can t. 17

17 Data Mgt Tool Features that Automate DG Data discovery, profiling, and monitoring Cradle to grave record of data and its standards Data quality metrics as DG policies for data standards DQ dashboards as performance mgt for DG standards Business rules for DQ as an expression of DG policies Exception processing as remediation of non-compliant data Data validation as real-time correction of non-comp data Business glossary as inventory of governed data Tool features for data stewardship and business auditing as enablers for the collaboration DG needs Stewardship as precedent for data governance Some orgs start with DQ/stewardship and morph it into DG 18

18 Recommendations Feel confident you can start and sustain a Data Governance (DG) program. Choose a starting point based on need. Be aware of intersections with biz & IT initiatives. Balance the opposing goals of DG Biz-oriented compliance and risk (control) Tech-oriented data standards (improvement) Expect to consolidate multiple governance programs. Choose an org structure for DG suited to corporate culture. Board, committee; competency center, stewardship, etc. Recruit a board chairperson (or two!) who will make DG a priority Staff the DG board with mix of: Business and data mgt people; Executives and data workers Various data mgt disciplines; various lines of business Consider starting with one of DG s three pillars. Begin with compliance or business integration. You may need business transformation before these People & Process at first; later, look for software automation for DG 19

19 Questions? 30

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