Institutionalize Your Data: Designing and Implementing a Dynamic Blueprint for Data Governance and Management

1. Institutionalize Your Data:Designing and Implementing a Dynamic Blueprint for Data Governance and Management Julianna Sakamoto, Senior Manager, Informatica jsakamoto@informatica.com Tel. 650-385-5010 Provided for DFW DAMA Meeting on July 18th – 11:30 am to 1:30pm

2. Welcome • Critical Time to Examine Your Data Governance and Management Practice • Sarbox 3rd year; Foreign companies on the US exchange mandated to comply • Business is NOT as usual – Our Webinar attracted 881 registrants! • Even playing field in a flattening world – or is it? • Scope • Intentionally kept broad to meet varying degrees of interest and experience levels • Perhaps follow-on break-up sessions or workgroups in the future? • Some sections will be for reference or further reading only • Electronic copies available • Follow-ups • Julianna Sakamoto, jsakamoto@informatica.com, cell: 415-407-4817 • Informatica Team

3. Agenda • Importance of Dynamic Blueprint to Data Governance and Management • Heightened Need of Data-Driven Approach • Challenges of Linking Data to Corporate Measures • Agile Data Governance and Management • Expanding the Definition of Data Governance • Best Practices for Securing Endorsement and Program Initiation • Case Study – Financial Services • Initiative Engagement – Start to Finish • Establish Practice Development Strategy • Design an End State and Conduct Gap Analysis • Identify Quick Wins and Design Project Plan • Establish Resource Plan and Team Model • Measure and Control Goals • Transition to Expanded Scope • PowerCenter for Automating Data Governance and Management Tasks • Q&A and Open Discussions

4. Importance of Dynamic Blueprint to Data Governance and Management

5. Elevated Expectation and Anxiety Around Data Governance Data governance is the new reality “Data governance and compliance is the new reality, many attendees said, changing the way they work. The emphasis on governance gives data management more visibility in the corporate world. Data quality is taken more seriously, data integration is a necessity, and security is an imperative, not a luxury, attendees said. A competitive global marketplace and laws such as Sarbanes-Oxley bring the promise of increased resources -- but the pitfalls of higher stakes.” Source: DAMA International Symposium and Wilshire Meta-data conference, April 2006

6. Adverse reports on the decline 16% to 7% Marked (>10%) improvements Entertainment & Media Industrial products Retail & Consumer Technology Lowest % of adverse report ‘05 Banking & Capital Markets Pharmaceutical Real Estate Sarbanes-Oxley Adverse Reports over Internal Control Decreased in Year 2 Source: PricewaterhouseCoopers Webcast, May 06

7. Heightened Need for Data-Driven Approach • Applying Six Sigma Concept for Certifying Data • Bring rigor and measurements in data management • Cornerstone for corporate performance management • Increased Layering of Frameworks for Auditability • Increasing use of ITIL, CobiT, COSO, and ISO 9000/17799 • Refining accountability and transparency to drive organization-wide participation • Attempt to Link IT Investments to Compounding benefits – Institutionalize Data as Strategic Asset • Participation in revenue-driving activities beyond traditional IT cost reductions and risk management • Off-shore/onshore IT outsourcing prevalent with large companies

8. Continued Challenges in Linking Data to Business Value Data Governance Metric Business Value-Driven IT Issue? Business Issue? RevenuesCostRisk Or Both? Reports • Customer Campaign • Access Control • Fraud Detection • Reconcilability • Supply Chain Costs • Audit Trails • Distribution Management • Legacy Data • On-Demand Availability • Regulatory Compliance • Accuracy • Privacy Risk

9. Focus 1 Focus 2 Focus 3 Dynamic Blueprint – Agility as Part of DNA Dynamic blueprint - value-driven approach to data governance validated through incremental project progression tuned to business demand

10. Expanding the Definition of Data Governance

11. Customer Data Product Data Supplier Data Finance Data Employee Data Governance: Historical Context Corporate Governance The set of processes, customs, policies, laws and institutions affecting the way a corporation is directed, administered or controlled. IT Governance The leadership and organizational structures and processes that ensure that the organization’s IT sustains and extends the organization’s strategies and objectives. Data Governance The processes, policies, standards, organization and technologies required to manage and ensure the availability, accessibility, quality, consistency, auditability and security of data in a company or institution. Business Processes CRM System ERP System Order Mgmt System Finance System HR System

12. Service-Oriented Architecture– Data Services Data Integration Platform Integration Competency Center (ICC) Architecture Technology Approach Data Definitions & Taxonomies Master/Reference Data Data Definition Monitoring & Measurement Roles & Responsibilities Organizational Structure Enterprise Data Model Technology Standards Data Access & Delivery Data Change Management Planning & Prioritization Org. Change Management Expanded Data Governance Framework to Underscore Importance of Technology Corporate Governance The set of processes, customs, policies, laws and institutions affecting the way a corporation is directed, administered or controlled. IT Governance The leadership and organizational structures and processes that ensure that the organization’s IT sustains and extends the organization’s strategies and objectives. Data Governance Data Accessibility Data Availability Data Quality Data Consistency Data Security Data Auditability Standards Policies & Processes Organization Data Integration Infrastructure

13. Challenge Solution Results Financial Services Customer Case StudyEnabling Enterprise Integration via Metadata Management • Key Business Requirements: • Meet statutory requirements – BASEL II, Sarbox, etc. • Improve reporting and management decision • Facilitate future development of analytical applications Go to the Data Governance Tool Readiness Assessment • Approach: • Provide a consistent and integrated data integration mechanism for management and reporting • Allow impact analysis before project initiation • Simplified reporting & reconciliation processes • Improved management decision processes and outcomes • Mitigated cost/impact from potential non-compliance • Improved estimates for change costs • Informatica PowerCenter • Oracle, SQL Server, Teradata, Sybase, SQL servers, DB2, Cognos, Erwin • PowerCenter Metadata Manager 2.1 • Metadata directory, search, lineage and where-used reports • Inability to automate metadata source handling • Inability to retain knowledge even with IT staff departures and project completions • Lack of clear KPI definitions • Uncertainty with project costing

14. Financial Service Customer Case StudyData Governance Self-Assessment Map Data Quality Lifecycle Management (Scorecard, Monitoring, and Remediation) Data Profiling Data Cleanse and Match Server Grid Push-Down Optimization Data Federation Real-Time Partitioning Data Quality Data Consistency Data Availability Metadata Management Dashboard, Data Lineage, Impact Assessment and Data Dictionary/Business Glossary Data Accessibility Data Auditability Unstructured Data Mainframe Legacy Data Security Team-based Deployment Encryption Support Privilege Management Data Classification

15. Best Practices for Securing Endorsement and Program Initiation

16. Guiding Principles for Program Initiation • Begin with a clear top-down mission statement and key performance indicators that will be boosted by the program • Make data management as an integral part of the corporate governance and oversight process – not a separate new initiative • Embed the new standards, practices and processes into existing functioning framework where applicable • Seek to align with stakeholders and business owners to dissolve resistance and accelerate approval cycles • Drive “visible” wins through “selected” subject areas or data governance metrics according to value and risk levels

17. Focus for the second half • Internal selling example 5 Phases of the Data Governance and Management Program • Dynamic Blueprint approach Phase 1. Establish Vision, Framework and Metrics Phase 2. Institute Policies and Design Principles Phase 3. Conduct Readiness Assessment Phase 4. Secure Program Endorsement Phase 5. Conduct Initiative Engagement • Policy • Integrated planning cycles • Foundational architecture • Stewardship • Usage validation • Data standards and quality • Audit processes • Design Principles • Information classification • Record retention and disposal • Functional areas • Metadata management • KPI measurement • Risk management • Training & communications • Shared services Step 1: Establish Practice Development Strategy Step 2: Design End State and Conduct Gap Analysis Step 3: Identify Quick Wins and Design Project Plan Step 4: Establish Resource and Team Model Step 5: Measure and Control Goals Step 6: Transition to Expanded Scope • Vision • Mission statement • People, process and technology • Deployment scope • Phased delivery strategy • Governance metric • Availability • Accessibility • Auditability • Consistency • Quality • Security • Value proposition • Linking investments to returns • Steering committee formation • Assessment model • Cultural and behavioral • Tool usage maturity • Control design • Preventive vs. detective • Automated vs. manual • Assessment results • End-state goal setting • Gap analysis • Role-based mapping • Stakeholder analysis • Communication and training • Program Planning • Identification of areas most prepared • Exec sponsorship • Early adopters and supporters feedback • Community of practice • Business Case • LOB initiatives/pain points • Dynamic blueprint • Regulatory compliance • Revenue boost • Cost reduction • Risk mgt • Financial and op. analysis and buy-ins • Value/risks defined • Proposal/Approval

18. Financial Services Customer Case Study For Data Governance and Management

19. Financial Services Firm Best Practices:Phase 1: Establish Vision, Framework and Metrics - 1 Vision • The firm manages information as an integrated enterprise asset • Organizations must plan their future needs, and effectively utilize and manage information to support decision making processes • Corporate standards and governance must be established in conjunction with the IT transformation Guiding Principles • Data must be managed as an integrated business asset • Data standards, policies and processes must be institutionalized • Standards for corporate governance, IT governance and data governance are to be re-established Key Success Factors • Launched by CFO and supported by finance and LOB • Business leadership provides oversight and day-to-day support for key subject areas • IT governance committee and other leaders guide architecture and tool selection process in concert with directives from business

20. Financial Services Firm Best Practices: Phase 1: Establish Vision, Framework and Metrics - 2 Scope • Areas for financial planning, budgeting, allocations, forecasting, and regulatory reporting Phased Delivery Strategy • First Year – Enterprise-data warehouse • Mid- Master data/Data governance certification • Latter stage – Linking to business KPI Data Governance Metrics • Initial focus on Quality • Accessibility improved through master data approach • Auditability and Consistency considered crucial • Access control and classification key to Security • Availability tuned to reporting cycles Value Proposition Gain more accurate and reliable forecasting, and the reporting architecture to ensure timely response to business changes Linking Investments to End-State Goal World-class organization through business and IT innovation; Reinforced value of data People • Identification of existing programs • Accountability mapped to functional areas and processes • Key stakeholders apprised of project deliverables, milestones and gating factors Process • Integrated, planned and coordinated – lifecycle approach • Regular and ad-hoc work activities structured to manage in support of business objectives • Operating model and rollout defined Technology • Implementation of end-to-end financial reporting system • Enterprise-wide data warehouse • Common infrastructures, standards and interfaces Steering Committee Formation Executive Sponsor Business Partners and Domain SME Technology / Project Leadership

21. Financial Services Firm Best Practices: Phase 2:Institute Policies and Design Principles -1 Data Governance and Management Policies (Operating Guidelines and Rules) • Integrated planning cycle • Data management as formalized discipline • Planning for acquisition, creation, transformation, usage and retention lifecycle • Stewardship • Accountability for data management to treat data as an asset • Business definitions and standard guidelines • Consistent interpretation of information • Data standards and quality • Standard descriptions and common libraries • Monitoring, reporting and anomaly prevention • Accuracy, conformity, completeness, consistency, duplicates and integrity as ‘data quality’ solution considerations • Audit processes • Walkthrough and testing guidelines according to control and risk levels • Classification of preventive versus detective, and manual versus automatic measures • Certification workflow • Foundational architecture • Organizational, solution and IT architectures designed to maximize value • Enabler to formalized data management and governance practice • Usage validation • Data usage patterns defined and validated • Tasks performed by authorized individuals • Data in custody managed in compliance with privacy security, compliance and other legal requirements

22. Financial Services Firm Best Practices: Phase 2: Institute Policies and Design Principles - 2 Data Governance and Management Design Principles (Structures and Methodology) • Information classification • Information inventory • Supporting resources • Functional and subject area • Domain use/reuse • Record retention and disposal • Retention period by class • Secure disposal according to biz, legal and regulatory mandates • Record keeping • Functional areas • Subject area model • Boundaries and accountabilities • Process integration • Common and reusable structure • Metadata management • Integrated repository • Data flow validation • Reconciliation across formats, categories, and types • KPI measurement • Target metric and definition • Prioritization and categorization framework • Review model • Alignment to organizational goals • Risk management • In/out of scope • Indicators and impact • Likelihood analysis • Control designs • Preventive / detective -testing • Automation • Training & communications • Data treatment cultural assessment • Gap analysis • Foundational messages • Logistic and frequency • Shared services • Service definition • Resource design • Model design – mix of distributed and centralized • Business partners • Practice development

23. Financial Services Firm Best Practices: Phase 3: Conduct Readiness Assessment Assessment results • End-state goal setting • Unified process, infrastructure and format for GL • Timeliness and precision for monthly, quarterly and annual reporting • Full change management capture and traceability • Gap analysis • Completeness and consistency in documentability – key risk areas • AP handling/legacy retirement • Enterprise risk model/reporting integrity • Excessive low/no value-added activities • Role-based mapping • Workflow control and exception handling • Stakeholder analysis • Impact and risk areas for regular reporting cycles and Sarbanes-Oxley walkthrough • Communication and training • Part of the career development program Assessment model • Cultural and behavioral • Interviews of selected employees and management • Tool usage maturity • Quantitative and qualitative • Deployed and planned • Control design • Control selection • Evaluation metric for controls • Preventive vs. detective • Data asset inventory • Assign risk class and resulting control type • Automated vs. manual • Kept open initially • Policy-based mitigation for control that cannot be automated

24. Focus 1 Focus 2 Focus 3 Financial Services Firm Best Practices: Phase 4: Secure Program Endorsement - 1 Progressive Expansion of Focus - Focus 1: High Priority Segments → Focus 2: Cost Reduction→ Focus 3: Enterprise Risk and Revenue Optimization

25. Financial Services Firm Best Practices: Phase 4: Secure Program Endorsement - 2

26. Executives – Share holder value, earning and compliance accountability - CEO/CFO - CIO/CTO - BU General Managers and VP • Finance, Legal and Operations- Financial integrity, liability and productivity measure - Auditors / Analysts - Controllers - Compliance officers - General counsel • Line of Business –Revenue, product and customer - Sales operations - Marketing - Customer analytics • IT Team – Productivity and cost containment - Enterprise architect. - Dir. Of IT - IT Analyst - Data modeling - Data warehouse manager Financial Services Firm Best Practices: Phase 4: Secure Program Endorsement - 3 • Compliance • Demonstrate adherence to internal control through clear workflows and system design • Risk-driven approach to manage audits • Control related policy and enforcement practice in place Focus Area 1 Goal: Justify High Priority Segments Steering Committee Executive Sponsor Business Partners and Domain SME • Revenue • Better, more targeted pricing model, differential to segments and customer behaviors • Developing customer master data to ensure completeness for cross-sell and upsell Technology / Project Leadership Relevant benefits articulated to each segment • Program Planning • Identification of areas most prepared Selected corporate IT andFinance Dept • Exec sponsorship CFO/CIO • Early adopters and supporters feedback Reflected in the vision, policies and design principles • Community of practice Practice development phase • Cost • Stop non-value added activities for agents related to invoicing, billing and credit management • Remove unnecessary documentation and codes that require maintenance cost • Risk • OUT OF SCOPE

27. Executives – Share holder value, earning and compliance accountability - CEO/CFO - CIO/CTO - BU General Managers and VP • Finance, Legal and Operations- Financial integrity and liability and productivity measure - Auditors / Analysts - Controllers - Compliance officers - General counsel • Line of Business –Revenue, product and customer - Sales operations - Marketing - Customer analytics • IT Team – Productivity and cost containment - Enterprise architect. - Dir. Of IT - IT Analyst - Data modeling - Data warehouse manager Financial Services Firm Best Practices: Phase 4: Secure Program Endorsement - 4 Focus Area 2 Goal: Drive Cost Reduction • Compliance • SUSTAIN FOCUS AREA 1 EFFORT Steering Committee Executive Sponsor Business Partners and Domain SME • Revenue • SUSTAIN FOCUS AREA 1 EFFORT Technology / Project Leadership Relevant benefits articulated to each segment • Program Planning • Identification of areas most prepared Added supply chain and partner management • Exec sponsorship Added VP and partner execs • Early adopters and supporters feedback Domain SME integrated • Community of practice Reuse existing best practice within subject areas • Cost • Provide metadata-driven supply master to handle complex network of supply chain relationships • Unify the partner merchant negotiation data systems so that agents can us • Risk • Lay foundation for business partner risk management • Model data flows and dependencies associated with business relationships • Assess risk impact and likelihood

28. Executives – Share holder value, earning and compliance accountability - CEO/CFO - CIO/CTO - BU General Managers and VP • Finance, Legal and Operations- Financial integrity and liability and productivity measure - Auditors / Analysts - Controllers - Compliance officers - General counsel • Line of Business –Revenue, product and customer - Sales operations - Marketing - Customer analytics • IT Team – Productivity and cost containment - Enterprise architect. - Dir. Of IT - IT Analyst - Data modeling - Data warehouse manager Financial Services Firm Best Practices: Phase 4: Secure Program Endorsement - 5 Focus Area 3 Goal: Secure Enterprise Risk and Revenue Optimization • Compliance • Increased automation versus manual control for cost containment and liability mitigation • Align treatment of confidential data with security and privacy practice Steering Committee Executive Sponsor Business Partners and Domain SME • Revenue • Increased oversight for partner management with the use of metadata management • Add reference data from sales distribution to leverage customer and product data optimally used for planning Technology / Project Leadership • Program Planning • Identification of areas most prepared Mobilized corporate IT and selected lines of business • Exec sponsorship Expanded to include major BU • Early adopters and supporters feedback Formal survey and training in place • Community of practice Reestablishing best practice Relevant benefits articulated to each segment • Cost • SUSTAIN FOCUS AREA 2 EFFORT • Risk • Launch an integrated risk management tied to financial and asset management • Initiate automated correlation and verification for risk assessment data for future expansion

29. Initiative Engagement – Start to Finish and Expand Scope

30. Resource Model Integrated with Data Governance and Management Initiative Integral to all aspects of practice development, sensible strategy design and execution • Enterprise Integration Strategy and Development Services • Enterprise Architecture • Data Integration Services • Business Process Improvement • Data Warehouse Development • Reporting Services • IT Security • Practice • Policy, Standards and Guidelines • Corporate Standards • Tools • Training • Implementation Support • Operations • KPI Measures • Reporting Key Subject Areas / Lines of Business Extended Partners Financial Reporting Audit Corporate IT Integration Competency Center (ICC) Governance Steering Committee Legal Compliance Departmental Privacy Risk Management BU

31. Phase 5: Conduct an Initiative EngagementOverview of Six Steps • Step 1: Establish Practice Development Strategy • Step 2: Design End State and Conduct Gap Analysis • Step 3: Identify Quick Wins and Design Project Plan • Step 4: Establish Resource and Team Model • Step 5: Measure and Control Goals • Step 6: Transition to Expanded Scope

32. Phase 5: Conduct an Initiative EngagementStep 1: Establish Practice Development Strategy -1 Accessibility Auditability Availability Consistency Quality Security Management Infrastructure Data Valuation Data Governance Metric Existing Practice Project silos dominate without organization-wide standards Information classification and controls designed Departmental readiness evaluated – quality considered major Areas for Improvement People, technology, process misalignment Valuation incomplete; Stakeholders with different lists and metrics No enterprise-wide program formalized Developmental Goals Integrated, reusable architecture; Formalized stewardship Unified data asset valuation with common vocabulary and classes Institutionalized data governance and management monitoring and tracking • To succeed, data governance and management program must include practice development strategy and plan in place

33. Phase 5: Conduct an Initiative EngagementStep 1: Establish Practice Development Strategy - 2 STEP 1: Checklist • Review existing templates and documents to pinpoint deficiencies • Identify and interview key affinity groups and business users • Identify key business initiatives that will gain benefits when practice is developed • Determine what areas of data governance metric improvement provide accelerated value to those initiatives • Fully understand development needs • Identification of key subject and functional areas • Individual or group-level educational requirements • Design a stewardship development plan • Objectives, scope and tasks • Identify educational vehicle • Create a progressive plan to adapt to changing infrastructure • Practice development tasks

34. Phase 5: Conduct an Initiative EngagementStep 1: Establish Practice Development Strategy - 3 Enter here based on interviews <Example Stewardship Plan> - can take different forms but important to assess existing roles and activities

35. Phase 5: Conduct an Initiative EngagementStep 2: Design End State and Conduct Gap Analysis -1 Example: Focus Area 1 – High Priority Segment

36. Phase 5: Conduct an Initiative EngagementStep 2: Design End State and Conduct Gap Analysis -2 STEP 2: Checklist • Enumerate pain areas for the focus area • Complete gap assessment sheet through walkthrough and interviews • Examine both tangible and intangible factors impacting the results • Identify key affinity groups, supporters and champions who will support the cause • Conclude this step with a proposed master plan • Pragmatically select “Gap” areas can be used as an “Exemplary” case • Areas of visible governance issues • Combined use of policy and guidelines • Characterization of before / after in hours/work impact • Test / prototype solutions/suggested changes • Small areas that can be tested short term • Validate stewardship model • Identify areas for elimination or retirement • Removal of non-value added activities

37. Phase 5: Conduct an Initiative EngagementStep 3: Identify Quick Wins and Design Project Plan - 1 IMPERATIVE - Disciplined Approach to Balancing Strategic Agenda and Tactical Activities. Choose Nature and Degrees of Involvement According to Value Delivery Initiative Engagement Program Involvement Operational

38. Phase 5: Conduct an Initiative EngagementStep 3: Identify Quick Wins and Design Project Plan - 2 Process for evaluating new initiatives as well as qualify and stage them in the overall master plan. • Internal selling of the data governance and management program for ‘Business Value’ delivered • Overview of automated, reusable solutions vs. hand-coded alternatives • Proof of usability and validity • Continued supporting during project lifecycle Initiative Lifecycle

39. Phase 5: Conduct an Initiative EngagementStep 3: Identify Quick Wins and Design Project Plan - 3 STEP 3: Checklist • Conduct initial projects either with policy / guidelines or ideally with add-on solutions • Assess the results within the core team • Design a pragmatic project plan for 3-6 month cycle with the vision for 2-3 years • Conduct small team meetings to refine a plan • Seek an approval of a proposed project plan with initial results • Demonstrate the value through early projects • Hours saved, dollars collected, more strategic assignments, etc. • Shut down non-value added components • Get proof points on validity, applicability and recommended areas for future implementation • Anecdotal stories about paybacks • Perception-building through active dialogs • Position to extend value through an extended pool of resources • No major full-headcounts yet! Early adopters and champions to grow the extended team

40. Phase 5: Conduct an Initiative EngagementStep 4: Establish Resource and Team Model - 1 For Initiative Engagement, while investment returns vary by environment, gradual move toward Shared Services may often yield better results Integration Competency Center Models Project Silos Best Practices Technology Standards Shared Services Central Services Benefits Project Optimization Leverage knowledge Consistency Resource optimization Control

41. Phase 5: Conduct an Initiative EngagementStep 4: Establish Resource and Team Model - 2 Inner working of the data stewardship activities • Steering Committee nominate resources to work with team lead and assign stewards • Data Stewards perform tasks with team leads • As needed, stewards work with team members directly • Analysts, SMEs and Metric Experts (HA, security, quality, etc.) work as a team • Data Integration provides resources and work with IT strategy and architect team IT Strategy and Architect Team Steering Committee Data Stewards Data Integration Expert (s)/ Resource (s)/ ICC

42. Phase 5: Conduct an Initiative EngagementStep 4: Establish Resource and Team Model - 3 STEP 4: Checklist • Develop task descriptions and qualification guidelines • Informally interview or ask for referrals to identify advocates • Look for champions who are both business and technology savvy (all areas of IT) • Identify skill gaps • Seek approval of a proposed resource plan including skill development • Design a team model and resource plan • Emphasis on initiative engagement • Previous experience and problem-solving mindset plus • Alternative approaches to be presented • Provide scenario assessment • Pros and cons of specific resource model and requirements • Risks and open issues clarified • Get endorsement for a small team • Secure baseline to demonstrate focus area value • Communication and training plan in place

43. Phase 5: Conduct an Initiative EngagementStep 5: Measure and Control Goals STEP 5: Checklist • Get updated on businesses about their current directions • Verify whether the current data governance initiatives are generating intended results • Clearly document root cause analysis results if the results are less than what you expected • Make a call whether you proceed with the current scope or alter – don’t make a huge change – incremental ones only • Ensure ongoing communication • IT investment defined – tangible/intangible • Value – revenue, cost, compliance and risk • Particular components –worked/worked less • Make small incremental changes tuned to business needs • Delivery of results and incremental changes reflective of ongoing business changes • Positive organizational impact highlighted • Get support for developmental areas • Reinforcement for people, process and technology • Communication and training plan in place

44. Phase 5: Conduct an Initiative EngagementStep 6: Transition to Expand Scope - 1 STEP 6: Checklist • Use the initiative engagement results as a guide to approach target BU or functional areas • Project prospective results ‘what if’ you expanded scope to the next areas • Examine all metrics that are to be affected by the expanded scope • Revise a project plan with an expanded scope • Step up to evaluate and use tools to automate and move preventive • Perform rigorous assessment on the initiative phase • Reassessment on architecture, tools, skill sets, processes, training, and communication • Organization dynamics • Get departmental/functional buy-ins to expand scope • Current major objectives defined • Find “small” ways to make a difference • Progressively automate with an expanded scope • Incremental value add defined – with less risk • Preventative, automated measure in place

45. Phase 5: Conduct an Initiative EngagementStep 6: Transition to Expand Scope - 2 Key Subject Areas / Lines of Business Operational Areas • Select specific areas of implementation Financial Reporting • Re-alignment • Buy-in • Resourcing • Role augmentation • Deployment • Training • Hand-off Audit Legal Compliance Privacy Risk Management Assess Assess Governance Steering Committee Implement Implement Go Live Realign Go Live Realign Measure Measure Program Direction Integration Competency Center (ICC) Technology Enablement Departmental Corporate IT Extended Partners BU

46. Lessons Learned • Achieve sponsorship and organizational alignment with a compelling business case quickly • Linking the data governance to a major business initiative such as SOX or Basel compliance, or merger consolidation becomes a thrust for executive buy-in and funding approval • Utilize supporting tools and methodologies to accelerate approval and implementation cycles • Maturity assessment tool and economic value of data framework raise the profile of data governance and management • Progressively increase automation to reduce personnel or culturally driven issues, as well as to normalize changes • Preventive measures help mitigate cost impact and risks • Ensure communications and training to promote a new mindset and vigorous approach toward data • Making data “asset” management as part of the DNA – keep it simple and robust

47. Concluding Remarks

48. PowerCenter 8 - Platform for Automating Data Governance and Management Tasks Applications Processes BI Tools Portals Applications Web Services WebSvc JDBC SQL JMS Web Svc Delivery Services Web Services, Messaging, JDBC, ODBC Integration Services Data Profiling, Data Cleansing, Data Transformation, Data Movement, Data Federation Metadata Services Metadata Repository (Semantic Catalog) Exchange, Data Lineage, Impact Analysis, Data Stewardship Infrastructure Services Security, High Availability, Scalability Tools Admin Tools, Developer Tools, Metadata Tools, Analyst Tools Access Services Packaged apps, Mainframe, RDBMS, Msg. Systems, Flat Files (Structured, Unstructured & Semi-structured Data) Applications Databases Messages Flat Files XML Unstructured Data Mainframe DATA CONSUMERS INTERNAL EXTERNAL DATA SOURCES

49. Harnessing the Power of Data through an Automated Approach Exploiting Data Management Technology for Business Performance • Take a unified approach to data integration • Ensure data standards as the cornerstone of an effective data governance and management program • Institutionalize your data • Applications come and go, but the data largely stays the same • Data governance and management decisions you make today will have profound impact on your business

50. Q&AOpen Discussions