Improving Data Governance in Your Organization. Faire Co Regional Manger, Information Management Software, ASEAN

Transcription

1 Improving Data Governance in Your Organization Faire Co Regional Manger, Information Management Software, ASEAN

2 Topics The Innovation Imperative and Innovating with Information What Is Data Governance? The Need for Data Governance Insight from the IBM Data Governance Survey Implementing Data Governance Programs IBM s Data Governance Solution Getting Started Data Governance Case Study and Lessons Learned Recommendations

3 The Innovation Imperative and Innovating with Information

4 Innovation and Differentiation Seizing the most promising opportunities IBM CEO Study found: Globalization & technology driving commoditization at increasing rate Operational efficiency and cost reduction providing diminishing returns The way to prosper is through top-line growth (new revenue streams, new clients, new markets, new channels) The way to growth is through differentiation (create new value for customers, employees, partners, shareholders) The path to differentiation is innovation (seizing the most promising growth opportunities

5 Innovation with Information The Electricity Metaphor If innovation is the conduit for growth Information is the electricity Data Governance is the transformer Information on Demand is the switch then Growth is the resulting light. Glo The only source of profit, the only reason to invest in companies in the future, is their ability to innovate and their ability to differentiate. Jeffrey Immelt, Chairman and CEO, GE Innovation is about much more than new products. It is about reinventing business processes and building entirely new markets that meet untapped customer needs. Most important its about selecting and executing the right ideas and bringing them to market in record time. Business Week, The Most Innovative Companies

6 Information on Demand Delivering information in context to optimize business processes, applications and productivity

7 Initiatives in Response to the Business Need Operational efficiency to revenue generation and differentiation Control Costs Risk and Compliance Optimize Business Processes Threat and Fraud Intelligence Improve Data Governance Operational Intelligence Customer Centricity Organizational Objectives Drive Revenue & Differentiate

8 What is Data Governance?

9 What is Data Governance? Data Governance is the orchestration of people, process and technology to enable an organization to leverage data as an enterprise asset DATA GOVERNANCE People Process Technology Executive Executive-Level Sponsorship Data Governance Risk Data Council Bodies Risk Data Governance Office (DGO) Data Governance Data Governance PMA Program Manager Data Quality Reporting Team Project Teams Virtual Teams Line of Business Stewardship Community Data Quality Reporting Liaison (1) Metadata Technical Liaison Liaisons (1) (4) Business Liaisons (4) Lead Steward Extract Data Definition Stewardship Function Data Production Stewardship Function Data Usage Stewardship Function Quality Measurement Stewardship Function Extract Extract The core objectives of a governance program are: Guide information management decision-making Ensure information is consistently defined and well understood Increase the use and trust of data as an enterprise asset

10 Data Governance Dimensions Successful data governance programs will have organization, process and business rules around most of these dimensions Organizational Awareness Data Architecture Data Quality Security / Privacy / Compliance Stewardship Information Lifecycle Management Audit and Reporting Policy Meta Data/Business Glossary Risk Management Value Creation Data governance frameworks are furthering strategic alignment and consolidation across businesses by spanning multiple policy, organizational, customer, product, service, people, process, and technology dimensions. The Tower Group

11 The Need for Data Governance

12 Common landscape hinders data governance Channels Business Units Data Systems Branches Retail Banking DB App DW CRM DB App Core Systems ERP ATMs Corporate Banking DB CRM App DB DB App Core Systems ODS Customer Call Centers Wealth Management DB CRM App DB DW App Core Systems CIF Internet Insurance DB CRM App DB DB App Core Systems Relationship Managers/ Agents Capital Markets DB CRM App DB DW App Core Systems Partners Inconsistent View of Customer Silos of Information Information Locked in Repositories Inconsistent Data No Single Version of the Truth Poor Channel Communication

13 Why is Data Governance Important? Effectively managing all dimensions of enterprise information enables: Timely integrated information delivered to support business opportunities. Accurate and consistent information shared across channels and LOB. Trusted accurate information supporting regulatory compliance. Reliable information for business intelligence and executive dashboards. Aggregate view of total customer relationship across the organization. Faster time to market for new products and services.

14 The Need for Data Governance 84% Recognize the Business Impact of Data Governance Yes Yet - Only 27% have centralized data ownership No Don't know 66% have not documented or communicated their data governance program IBM Data Governance Survey Sample Size- 50 FSI and Non-FSI Executives 73% have no KPI s or measurements of success

15 Insight from the IBM Data Governance Survey

16 Survey: Data Governance Initiative Status Business Initiative within Corporate Governance Program 20% Pilot Program 10% Business Initiative within Operational Risk & Compliance 40% Initiative Within IT 30% IBM Data Governance Survey 2006 Sample Size- 50 FSI and Non-FSI Executives When an organization views data as an enterprise asset, it establishes an executive-level data governance committee that oversees data stewardship across the organization.

17 Survey: Data Governance Maturity Level 50% 40% 30% FSP 55% respondents have only basic or foundational data governance programs 20% Non-FSP 10% 0% Basic Foundational Advanced Distinctive IBM Data Governance Survey 2006 Sample Size- 50 FSI and Non-FSI Executives 75% DO NOT have an enterprise wide data governance policy BASIC ( anarchy ) App-centric approach; meets business needs only on project-specific basis FOUNDATIONAL ( IT monarchy ) Policy-driven standardization on technology & methods; common usage of tools & procedures across projects ADVANCED ( business monarchy ) Rationalized data with data & metadata actively shared in production across sources DISTINCTIVE SOA (modular components), integrated view of compliance requirements, formalized organization with defined roles & responsibilities, clearly defined metrics, iterative learning cycle 54% have only conceptual support or less from senior management for data governance initiatives

18 Survey: Leading Drivers for Data Governance Programs Business Drivers Create value from integrated, high-quality information Increase marketing effectiveness Enable consistent usage of data across the enterprise Reduce regulatory risk 56% 46% 62% 53% 75% 60% 38% 66% Single version of the truth (MDM) and enabling information across the enterprise are the top two business drivers. Non FSP FSP IT Drivers Single version of the truth 88% 40% 0% 20% 40% 60% 80% 100% Developing architecture best practices & standards Monitoring to improve data quality Building governance infrastructure, technology & supporting organization Developing standard metadata mgmt 31% 47% 56% 46% 56% 60% 63% 47% Defining data processes and managing meta data are the top two IT drivers. Defining processes & business rules for ongoing governance Non-FSP FSP 67% 81% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% IBM Data Governance Survey Sample Size- 50 FSI and Non-FSI Executives

19 Survey: Redundant and Multiple Instances of Data n/a Don't know 6% Yes - to a great extent 41% Yes - somewhat 38% No 16% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% IBM Data Governance Survey 2006 Sample Size- 50 FSI and Non-FSI Executives We continue to hear from the LOBs that we provide too much data and not the kind of data they really need. CTO, British Tier 1 Bank

20 Survey: Data Stewardship Data stewardship is a business-oriented function Developing architecture best practices & standards Monitoring to improve data quality Building governance infrastructure, technology & supporting organization 31% 47% 56% 46% 56% 60% 56% have clearly defined and communicated Data Steward roles and responsibilities Developing standard metadata mgmt Defining processes & business rules for ongoing governance Non-FSP FSP 47% 63% 67% 81% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 59% of Data Stewards reside outside of business IBM Data Governance Survey 2006 Sample Size- 50 FSI and Non-FSI Executives Each steward has a mandate to change the process and structure of any business, person, or IT system, if that s what it takes to improve data.

21 Survey: Data Quality - Regular Remediation of Inaccurate Data Ongoing data quality assessment and remediation is a vital component of a successful data governance program Not applicable Don't know No Yes 0% 10% 20% 30% 40% 50% IBM Data Governance Survey 2006 Sample Size- 50 FSI and Non-FSI Executives Although an increasing number of organizations recognize the negative impact of poor data quality, most address the issue in a tactical, reactive fashion. Traditionally a minority of organizations had established a formal program and few such initiatives reached a mature, highly effective state.

22 Data Quality is Fundamental to Data Governance Data quality is recognized as a key enabler for data governance, yet only 35% of companies have data quality initiatives well under way.

23 Implementing Data Governance Programs

24 Data Governance Critical Success Factors Executive Leadership Senior Management Support Capable Implementation and Stewardship Executive support & commitment Direction and involvement Recognition of data as a corporate asset Commitment to resolve data issues Measurable results Manage expectations Committed resources Clear accountability Enterprise stewardship roles and responsibilities Effective and standardized toolset Support processes and technology

25 The Difficulty Implementing Data Governance Programs Implementing a Data Governance process is a fundamental change to the way both business and IT define, manage and use data. How to Fail Not having executive sponsors communicate Data Governance importance early and often Lack of comprehensive communication and training for both IT and business users Lack of performance measures for success. Develop data management measures for evaluation of projects, IT developers, and business users in data governance standards. We cannot do our job without data governance. Sean Nelson, Senior Manager Enterprise Information Management RBC Financial Group

26 IBM Data Governance Solution

27 IBM Data Governance Solution - Overview Leveraging our experience assisting clients implement and sustain Data Governance Programs, IBM has assembled a bundle of accelerators, driven by a proven methodology People Process Technology Executive Executive-Level Sponsorship Data Governance Risk Data Council Bodies Risk Data Governance Office (DGO) Data Data Quality Governance Reporting Team Data Governance PMA Program Line of Business Project Teams Manager Stewardship Community Virtual Teams Data Quality Metadata Technical Business Reporting Liaison Liaisons Liaisons Lead Steward Liaison (1) (1) (4) (4) Data Data Data Quality Definition Production Usage Measurement Stewardship Stewardship Stewardship Stewardship Function Function Function Function Organizational model and role definitions Operational models Executive sponsors and leaders Data Governance experts Data Quality experts Change Management/Training experts Data Governance Implementation Methodology Pre-defined Data Governance Policies and Procedures Pre-defined Data Quality Standards Industry-Standard Business Definitions Metadata Repository, populated with the industry standard business definitions Profiling technology Data Quality remediation technology Industry data, process and integration models Master Data Management applications

28 Data Governance Case Study and Lessons Learned

29 Case Study: Royal Bank of Canada Business problems Operational efficiency was trending down Tremendous amount of duplication silos, data, databases and information toolsets Goals Improve operational efficiency/save money Improve customer intimacy Solution Reorganized bank around customer One group responsible for enterprise-wide Data and Information Management Made enterprise decision to consolidate data Implemented data governance program utilizing IBM Information Server Lessons Learned Obtain executive business and IT buy-in and commitment early Build solutions aligned with strategic goals Create data governance organizational structure to empower data stewards Standardize on toolsets and enforce Think enterprise Result Faster time to market for initiatives Improvement in operational efficiency

30 Getting Started

31 Information On Demand Center of Excellence helps organizations get started. Getting Started Offerings Data Governance Maturity Assessment Information On Demand Workshop Center of Excellence Goals Develop information management best practices based on customer experiences Help clients optimize the business value of their information Take the Data Governance Maturity Assessment now to check your company s data governance maturity level and receive recommendations for improvement

32 Recommendations Recognize information as an enterprise asset that can deliver business value Enterprise data governance is difficult organizations need to embrace a data governance blueprint for success Executive sponsorship is key for data governance success. A mandate is necessary to drive compliance IT and business management must align and understand impact of data governance on their information reliant initiatives Successful companies drive stewardship & business metadata definitions early Master Data Management goes hand in hand with data governance IT is still perceived as responsible for data quality and data stewardship, but business users must take responsibility Be prepared for political and territorial battles

33 Faire Co Regional Manager, Information Management Software, ASEAN