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Introduction to Data Governance



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This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.

• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors

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Introduction to Data Governance

  1. 1. Introduction to Data Governance John Bao Vuu Director of Data Management
  2. 2. Director of DM Consultant | Advisor John Vuu SPECIALTIES ✓ EIM Strategy & Solutions ✓ Data Governance / DQ ✓ Business Analytics ✓ Data Warehouse INDUSTRIES ✓ Banking ✓ Insurance ✓ Ecommerce ✓ Healthcare • 18 years experience in Data Management • Founder of 2 technology companies • Former Accenture BI Consultant • DM Director at EIM Partners • BA degree in Finance – Western WWU, Washington, USA • BS degree Information Systems – WWU, Washington, USA About the Speaker
  3. 3. • IM Foundational Disciplines • Cross-functional Workflow Exchange • Key Objectives of the Data Governance Framework • Components of a Data Governance Framework • Key Roles in Data Governance • Data Governance Committee (DGC) • 4 Data Governance Policy Areas • 3 Challenges to Implementing Data Governance • Data Governance Success Factors Presentation Outline
  4. 4. IM Foundational Disciplines DATA WAREHOUSE ✓Data integration ✓Enterprise data model ✓Common data dictionary ✓Data standardization ✓Data mapping / ETL ✓Applications support BUSINESS INTELLIGENCE ✓Web portal BI ✓Decentralized reporting ✓Authorization & security ✓Applications development ✓Data marts ✓Advanced Data Analytics* DATA GOVERNANCE ✓Policies & procedures ✓Stewardship / Ownership ✓Metadata management ✓Business glossary ✓DQ management ✓Data remediation / cleansing ✓MDM Bank’s fragmented Information Management architecture and poor data quality can present operational risks, strategic uncertainty and the potential for loss of revenue. Inconsistent and improper handling of data across departments can also contribute to lower staff productivity, workflow inefficiency and additional cost of maintenance and support for lack of robust enterprise information and data governance strategy.
  5. 5. Example: Cross-functional Workflow Exchange Department A provides reports & data to Department B Department B checks Department A work; validates accuracy of data, makes or requests corrections Department B takes action on approvals, scoring, rating, campaigns, cust. offerings, commissions, etc. Department B deals with the consequences of any errors in reporting and from poor data quality ACTION Poor data quality cost as much as 25% of an organization’s revenue each year. TDWI – The Data Warehouse Institute Typical workflow exchange between departments: ✓ Reports become more accurate ✓ Greater confidence in decision-making – lower risks ✓ Increase productivity and efficiency across business functions ✓ More effective campaigns programs – cross & up selling ✓ Reduce operational costs – increase in revenue
  6. 6. Key Objectives of the Data Governance Framework Data Governance framework baseline components: 1. Establish accountability by defining key roles and responsibilities within the DG framework 2. Define DG procedures and the methodology used to execute them 3. Define guidelines for data policies, data quality, data provisioning, metadata and reference data 4. Provide guidance for data management practices to maximize business value such as redundancy and improving data consistency 5. Provide guidance for creating and maintaining standards and tools for managing corporate data
  7. 7. Components of a Data Governance Framework
  8. 8. Key Roles in Data Governance Data Owner: person that has direct operational / business responsibility within a business unit for the management of one or more types of data Data Steward: person that assigns and delegates appropriate responsibility for the management of data to respective individuals Data Custodian: person that is responsible for the operation and management of systems and servers which collect, manage, and provide data access
  9. 9. Data Governance Committee (DGC) 1. Oversees the execution of vision and objectives of the Data Governance program 2. DGC is responsible for taking data architecture decisions, data remediation, setting up and enforcing data standards / policies and driving data quality 3. DGC acts as the point of escalation and decision making for Data Governance related policies and procedures 4. DGC helps to define KDE (key data elements), standards and metrics, conduct root cause analysis of issues and propose solutions Executives Cross-functional Team Members (Owners) Cross-functional Stewards / Custodians Business Consumers DG Council DG Steering Committee Data Stewards End Users Data Governance Organization Structure
  10. 10. 4 Data Governance Policy Areas DG policies are principles or rules that guide data-related decisions. Four primary DG policy areas: 1. Data Quality: establish how to define, measure and improve DQ 2. Data Provisioning: providing guidelines and best practices for provisioning data in efficient and effective ways 3. Metadata: organizing and classifying data for reference and use in the right business context 4. Reference Data: management of define values, i.e. KDE (key data elements), data dictionary, glossary, business rules, reference code values, etc. Data Quality Completeness Consistency Conformity Accuracy Integrity Timeliness 6 Data Quality Dimensions
  11. 11. 3 Challenges to Implementing Data Governance Organization Fit – the strategy and approach are not clearly defined and do not take into account resource involvement and their level of understanding Ignored Efforts – lack of stakeholder buy-in and cross-functional collaboration leading to independent workarounds by departments looking for quick solutions to their problems Lack of Perceived Value – the value of data governance is often not as explicit as in other projects. Internal bureaucracies and seemingly intrusive efforts of the DG program can impede progress and muddy value
  12. 12. Data Governance Success Factors Data governance is a discipline that evolves over time as part of the organization’s data-driven culture Requirements for achieving an effective Data Governance program: 1. Enforced policies and standards 2. Development and execution of processes and procedures 3. Involvement of senior leadership 4. A clearly define DG framework structure (governing body) Business Value The goal of Data Governance is to provide appropriate guidance for the organization to enable business effectiveness. People ProcessTechnology
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