Van Informatie op Orde naar Informatie van Waarde Data Governance en Datakwaliteit

1. Maaike van Noordt – Managing Consultant IBM GBS 27 januari 2011 Van Informatie op Orde naar Informatie van WaardeData Governance en Datakwaliteit

2. Doelstelling De sessie Data Governance en Datakwaliteit heeft de volgende doelstelling: Het uitwisselen van informatie tussen IBM en deelnemers over relevante modellen, best practices van IBM met betrekking tot de inrichting van de organisatie en de processen voor de governance van: • de data; • de kwaliteit van die data. Governance van: Data ↓ Data kwaliteit

3. Data Governance Maaike van Noordt – Managing Consultant IBM GBS 27 januari 2011

4. INFORMATIE GOVERNANCE Mensen Processen Technologie Extract Extract Extract Extract Extract Extract Wat is Data (Informatie) Governance Information Governance is het organiseren van mensen, processen en technologie om de organisatie in staat te stellen om zowel gestructureerde als ongestructureerde data te optimaliseren, te beveiliging, en maximaal te benutten. Belangrijkste doelstellingen van data governance: • Zorgen dat beslissingen gemaakt worden op basis van de juiste gegevens • Zekerstellen dat informatie consistent is, met eenduidige betekenis • Het verhogen van het gebruik van, en het vertrouwen in, data als een waardevol bezit van het bedrijf • Beveiliging van data, en het voldoen aan wet en regelgeving

5. IBM´s Data Governance Framework Business Outcomes / Reporting Data Risk Management and Compliance Value Creation Enablers Organisation Structures and Awareness Policy Stewardship Requires Core Disciplines Enhance Data Quality Management Information Lifecycle Management Information Security and Privacy Supporting Disciplines Supports Data Architecture Classification, Standards and Metadata Audit Information, Logging and Reporting 5

6. Enablers Enablers Organizational Structures & Awareness Organizational Structures & Awareness Policy Policy Data Stewardship Data Stewardship Enablers Why needed? Checklist Prerequisite for orchestration of data governance • Without a process and formalized ownership Data Governance will remain a paper exercise • Without formalized ownership and stewardship no action, meaning no improvement • Most data governance ‘enablers’ need to be addressed enterprise-wide (like ownership and process) • Data Governance RACI defined • Data Governance roles and responsibilities defined • Process and ownership across chain formalized • Policies and standards operationalized • Model management organization process formalized What needs to be done? Enterprise • Formalize definition of information domains • Start allocating business terms to information domains • Formalize process, ownership, stewardship, policies and standards • Implement strategic, tactical and operational model management Division / project • Allow to further split procedures per Information Domain • Request all data from a single point of contact at the division • Data definitions should always be assessed by the data governance board

7. Enterprise Data Governance will drive common understanding of the business through consistent, standardized, and integrated information Benefits Features Provide customers with valuable insight generated from integrated high quality information Become a trusted advisor to its customers Providing staff with accurate and relevant information Provide better service value delivery Meet regulatory compliance and provide business continuity requirements Reduce regulatory risk and improved financial performance Standardize definitions, processes, and business rules Create greater transparency on financial and data transformation processes Provide timely, consistent, and integrated information Improve marketing / customer management opportunities Deliver and maintain a “single version of the truth” Enhance and enable development of enterprise-wide strategic solutions Improved data quality Improve business decision capabilities Consistent usage of data through an integrated end-to-end solution infrastructure Better support the business 7

8. An Example of a Data Governance Organisation eg – LoB heads or their direct reports delegates Executive Committee member Empowered DGO Interlock with major programs LoB stewards Marketing, Finance, Treasury, Operations etc Domain stewards for Master Data Eg Customer Data, Product Data, Finance Data etc Systems/Applications managers from LoB’s Systems/Data managers from IS DATA GOVERNANCE BOARD Executive Sponsor Data Governance Council Strategic direction ( Representation from both Business & IT) DATA GOVERNANCE OFFICE Data Quality Manager DG Program Manager Data Audit/Reporting Information Architect Projects Data Stewards Data Steward Committee (Representation from both Business & IT) PM Data Domain Steward Data Stewards Data Stewards Data Stewards Per System Per System Per System Developer Data Stewards Data Stewards Data Stewards LOB Marketing Per System Per System Per System Product / Tariff Business Analyst Data Domain Data Domain Data Domain Data Domain Data Domain Data Domain LOB Finance Customer LOB Data Steward Coordinator LOB LOB Data Stewards Data Stewards Data Stewards Collaboration Per System Per System Per System LOB LOB AA, IA Data Stewards Data Stewards Data Stewards Per System Per System Per System 8

9. Example roles and responsibilities Function Activity Impact • Communicate vision • Show commitment • Identify and assign leadership team • Organizational engagement • Facilitates change management • Required for successful data governance Executive • Participation on Data Governance Council • Participation on Data Governance Committee • Prioritize enterprise-wide issues • Provide administrative foundation for Data Governance effort • Ensure program continuity and effectiveness • Ensure enterprise-wide focus and alignment Data Governance Leadership Manager of Line of Business (LOB) • Understand DG requirements, practices, processes • Participate on Data Governance Council • Support other Functional Area / LOBs on shared items • Prioritize issues within Line of Business • Effective inter- and intra-organizational change • Ensure issue identification • Realized remediation solutions for LOB • Improved LOB data quality and data governance • Understand DG requirements, practices, processes • Participateon Data Governance Council • Prioritize issues within Functional Area • Support other Functional Area / LOB on shared items • Effective inter- and intra-organizational change • Ensure issue identification • Realized remediation solutions for LOB • Improved FA data quality and data governance Functional Area (FA) • Provide access to systems and tools • Provide system documentation and other support • Facilitate systemic analysis • Audit and control • Accelerated program performance • Improved data quality • Consistent application managing continuous data governance for IS operations Information Systems / Technology SME • Receive and validate issues from field • Log valid issues in central location • Participate on DG Committee and support LOB or FA as needed • Effective business rules • Effective validation of issues raised • Timely logging of valid issues • Effective remediation of data quality issues Data Steward / Business SME 9

10. Typical Client Issues – From Data Governance Assessments Enablers Organizational Structures & Awareness Policy Stewardship Core Disciplines Data Quality Management Information Life-Cycle Management Information Security and Privacy Supporting Disciplines Data Architecture Classification & Metadata Audit Information Reporting 10 “Weak, needs improvement” “non-existent” “Delegated to IS” “Stewardship training needed” “Too many policies” “Difficult to enforce” “Have policies but difficult to enforce them, FSA will treat this as a crime” “Customer data is a continuing problem “Difficult to source data, from other LOB’s always been a problem”  ”Started a customer DQ program but has limited effect” “None, have to rely on favors to get and understand the data” “We don’t know how long to keep our data – so we keep all of it” “Test data is not controlled” “Non existent, available in pockets” “None company-wide” 10

11. We use the IBM Data Governance Framework to assess, prioritise, plan and implement effective change for our clients 2 Questionnaire 1 Data Governance Framework 3 Data Management Maturity Assessment 4 Delivery Road Map 11

12. IBM’s Data Governance Maturity Model evaluates where a client is on maturity model – and where the client wants to be Getting to Level 3 Requires New Thinking … … and Smarter Ways of Working …here to optimize value from information … here to measure and improve Need to be here for basic MDM Inject organisation-wide information stewardship, policies, principles, standards, processes, tools here Most organisations are here 12

13. Typical Data Governance Roadmap 13

14. Datakwaliteit Maaike van Noordt – Managing Consultant IBM GBS 27 januari 2011

15. Data Quality is a core component of Data Governance Data quality is defined by how effectively the data supports the transactions and decisions needed to meet an organization’s strategic goals and objectives, as embodied in its ability to manage its assets and conduct its core operations. The level of data quality required to effectively support operations will vary by information system or business unit, depending upon the information needs to conduct that business unit’s operations. Since the “purpose” varies, so does the bar that is used to measure fitness to purpose Data quality is typically a result of solid adherence to the definition of data quality criteria from both a business process and data design perspective 15 15

16. Information Governance Maturity Model Outcomes Data Risk Management & Compliance Value Creation Enablers Organizational Structures & Awareness Requires Policy Stewardship Enhance Core Disciplines Data Quality Management Information Life-Cycle Management Information Security and Privacy Supporting Disciplines Supports Data Architecture Classification & Metadata Audit Information Logging & Reporting Data Governance Council 16

17. Common Data Quality issues • Lack of information standards • Different formats & structures across different systems • Data surprises in individual fields • Data misplaced in the database • Information buried in free-form fields • Data myopia • Lack of consistent identifiers inhibit a single view • The redundancy nightmare • Duplicate records with a lack of standards 17 17

18. Breakdown of data quality dimensions • Technology-driven bad data qualities are those types that are caused by not applying technology constraints either in the database or in data integration. • Business-driven bad data qualities are those types that are caused by end users inaccurately creating data or defining data 18 18

19. 2 1 3 4 Data Quality Management Framework • Data Quality Policy • Data Organization • Data Administration • Data Quality Architecture • Data Quality Process • Data Quality Measurement • Data Quality Audits • Data Quality Reporting Improve - detail 19 19

20. Effective data quality consists of two types of practices Preventative data quality best practices focus on the development of new data sources and integration processes Detective data quality best practices focus on identification and remediation of poor data quality When these practices are undertaken as part of an overall data governance strategy with executive support and enterprise implementation data quality efforts result in sustained success 20 20

21. How can we cleanse data? Standardization Verification Identify Matches & Duplicates Manage Matches Enrich Data Reference Data + 21