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Developing & Deploying Effective Data Governance Framework
The document outlines an enterprise-wide data governance framework presented at the Customer Privacy & Data Protection India Summit 2019, emphasizing the alignment of data governance with enterprise data architecture. Key topics include the importance of data quality controls, compliance, and the integration of data management processes within overall business strategies. It highlights the roles of various stakeholders and technologies while noting the necessity for continuous monitoring and improvement of data quality to enhance operational efficiency and trust.
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Developing & Deploying Effective Data Governance Framework
- 1. Developing & Deployingan Effective Enterprise-wide Data Governance Framework CUSTOMER PRIVACY & DATA PROTECTION INDIA SUMMIT 2019 1 Kannan Subbiah Sr. VP & Chief Technology Officer MF Utilities India Pvt Ltd
- 2. Topics discussed 2 Data Governance Framework - Overview Aligningdata governance with enterprise data architecture Implementing robust data quality controls Driving data compliance across the organization
- 3. The Why, What, How 3 Dataeverywhere it is excessively expensive to manage, and you cannot find it, make sense of it, or agree on its meaning. It is Not Data Management DG organization is not there to do information management. It is there to guide and monitor. The Mark of Success the organization treating its information as it treats its factories, supply chains, vendors, and customers One clear goal is - to disappear. to fade into the business fabric It becomes part of the fabric of business, like financial controls.
- 4. 4 CorporateDrivers Customer Focus Mergers & Acquisitions At-Risk Projects Decision Making Operational Efficiencies Regulatory Compliance DataGovernance Organization Principles Policies Functions Metrics Functions Technology & ToolsOrganization Data Management Data Security Data Quality Data Warehouse Business Intelligence Steering Committee Governing Council Data Stewards Data Architects Data Monitoring Data Collection Data Integration MDM Tools BI & Analytics Data Protection Enterprise Data Architecture
- 5. People, Process & Technology 5 • DataOwners, Stewards • Data Creators & Consumers • Data Flow, Controls People • Standards • Data Quality, Metrics • Controls Processes & Policies • Meta Data / Data Quality • Reference, Master Data • Classification, Control Tools & Technology • Scope & Mandate • Centralized / Federated • Funding / Budgeting Execution Models
- 6. ALIGNING DATA GOVERNANCEWITH ENTERPRISE DATA ARCHITECTURE 6
- 7. Goals of EDA 7 Understand CurrentState Reduce Redundancy & Fragmentation Eliminate Inefficiencies in Data Flow Integrated view of Data Optimize Technologies involved Improve Data Quality Improve Data Security
- 8. 8 The Synergies Data ArchitectureData Governance Critical Data Defines Data Helps building Business & IT Consensus Identifies Data & business impact Establishes Accountability Data ImprovementIdentify areas of improvement Prioritize implementation Data StrategyMaps out Blueprint of Data Flow Links to Business Goals Architecture DeliverablesAids Governance on Focus, Priorities … Validates & help evolve OthersAids Communication across functions Helps build a business case Acts as a Business Sponsor
- 9. 9 The Relationship Data ArchitectureData Governance Strategies, Standards and Architectures Monitor Strategies and Standards Design the Architecture Common Data Requirements Better Quality Data Resolve Data Issues
- 10. Common Alignment issues 10 Failure to align withother governance initiatives Random point fixes - creates silos Lack of cross- functional standards
- 11. ROBUST DATA QUALITYCONTROLS 11
- 12. Data Quality –Why? 12 Misguided business decisions Legal and monetary penalties Financial inaccuracies and mistakes Negative company image Missed opportunities Loss of customers
- 13. Data Quality Vs Data Control 13 DataQuality Data quality is an Outcome Measure and Fix Data Quality Issues Data Control Data control is about ensuring Quality Validate at source and prevent
- 14. Sources of BadData 14 Different types of systems in use Transfer of data between different (often incompatible) systems Accidental/intention al removal of data Improper data governance Lack of responsibility and authority for managing data Lack of awareness of value of information Lack of integration between IT and business processes Lack of training and motivation
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- 16. Data Lifecycle -Controls 16 Creation • Input Validation • Meta Data conformance • Referential integrity Ingestion • Contextual Quality • Integrity Checks Storage • Data Changes • De- duplication Consumption • Access Controls Disposition • Integrity Check Audit
- 17. Data Quality – Continuous Process 18 DefineThresholds Different Thresholds Data Quality Rules Assess Quality Data Profiling – Run the Rules Resolve Manage as Defects Track it to closure Monitor & Control Continuous Process Responsibility with Data Owners Revise Revise Thresholds – Changing Needs
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- 19. Data Quality –Focus Areas 20 The Team Fix Ownership and responsibility Encourage collaboration across data domains Tools - Data Profiling Use Meta Data Use Statistical Tools / Models Rules & Metrics Quality Rules aligned to Strategy & Goal Define data quality measures and thresholds Reporting Data Quality Dashboard Exception Reports Issue Tracking Track & Resolve as Defects Address Root Cause
- 20. DRIVING DATA COMPLIANCEACROSS THE ORGANIZATION 21
- 21. A Business Opportunity 22 Improved Data Quality Moreaccurate analysis Facilitated / Federated Data Services Process Efficiency Enhanced Consumer Trust
- 22. People, Process & Technology 23 People Role, Title, Responsibility PositiveTeam Dynamics Culture Process Effective Workflows Effective Control Review and Monitoring Technology Enterprise wide Integration Contribute towards Data Quality
- 23. Get it Right 24 Mustbe Forward Looking – Align with Strategy Avoid Boiling the Ocean Designed and Managed by Business – Not IT Buy-in from Senior Management Keep it Simple, Implementable Value Delivery
- 24. Status quo – Notan option 25 Regulators expect More Internal pressure – Do more with less Cultural pressure – hard and fast rules Risk fatigue Technology – double edged sword
- 25. Thank You 26 Follow meon Twitter: @kannagoldsun The only thing we know about the future is that it is going to be different. -- Peter Drucker