Module 1. Introduction to Data Governance

• Data Governance

1. ?What Is Data Governance
2. Not a One Size Fits All Program
3. Enterprise Data Governance
4. ?What Aspects of Data Can Be Governed
5. ?Why Govern Data
6. ?What Data Should Be Governed

• Business Drivers for Data Governance

1. Types of Data Governance Programs
2. Types of Business Drivers
3. Company Data Pain Points
4. Critical Process Re-Engineering
5. Strategic Business Initiatives
6. Strategic IT Initiatives

Module 2. Implementation Fundamentals

• Selecting Data and Setting Goals

1. Selecting Data
2. Data Quality Goals
3. Access and Handling Goals
4. Safety, Security and Privacy Goals
5. Compliance and Legal Goals
6. Efficiency Goals
7. Translating Goals into Actions

• Policies and Standards

1. Naming Policies and Standards
2. Data Quality Policies and Standards
3. Transfer Policies and Standards
4. CRUD Policies and Standards
5. Archive Policies and Standards
6. Security Policies and Standards
7. Privacy Policies and Standards
8. Architecture Policies and Standards

• Data Governance and Company Processes

1. IT Sub-processes
2. IT Architecture
3. IT Investment Portfolio Evaluation
4. New IT Projects
5. IT Infrastructure
6. Business Sub-processes
7. CRUD
8. Supplier and Business Partner
9. Corporate Audit
10. Security and Privacy
11. Regulatory

• People in Data Governance

1. Who Should Govern Data?
2. Business Roles
3. Business Data Steward
4. IT Roles
5. Internal Operations Role
6. Management Role

• Technology in Data Governance

1. Data Quality Tools
2. ETL Tools
3. Workflow Management Tools
4. Metadata/Enterprise Repository
5. Business Rule Engine
6. Business Intelligence Reporting Tools
7. Other Tools

• Managing and Measuring Data Governance

1. Decision Making, Prioritization, Compliance and Performance
2. Practitioners Forums
3. Ensuring Data Governance Compliance
4. Managing Risk
5. Measuring Data Governance Effectiveness
6. Examples of Popular Maturity Models
7. Data Governance Scorecards
8. Scorecard Categories
9. Managing the “People” Side

Module 3. Case Study: Governing Customer Data

• Case Study Objectives
• Program Steps
• Step 1. Decide which Customer Data Needs Governing the Most
• Step 2. Identify the Most Appropriate Customer Data Leader
• Step 3. Assemble a Team and Assign Responsibilities
• Step 4. Establish Objectives and Guiding Principles
• Step 5. Link Key Customer Data Elements to a Master Data Program
• Step 6. Engage the Team with Quality Projects
• Step 7. Establish a Management System
• Step 8. Measure Your Efforts
• Step 9. Communicate, Communicate, Communicate
• Step 10. Serve as an Example for the Next Governance Team
• Case Study Recap

Module 4. Data Governance of Emerging Data Solutions

• Big Data

1. ?What is Big Data
2. Big Data use cases
3. ?What Aspects of Big Data Can be governed
4. Technical Considerations of Big Data
5. Data Quality and Big Data
6. Big Data Metrics
7. Regulatory or Legal Considerations

• Cloud Applications

1. ?What is Cloud Computing
2. Cloud Deployment Models
3. ?What Does the Cloud Offer
4. What Happens to Data in the Cloud
5. Technical Considerations in the Cloud
6. Data Quality in the Cloud
7. Data Metrics in the Cloud
8. Regulatory or Legal Considerations Part 1 & 2
9. Review

Module 5. Modernizing Data Governance

• Overview
• The Data Quake: From Stable to Volatile
• New Data Governance Challenges

1. Big Data and More
2. New Data, Same Goals
3. The Right Balance

• Curating and Cataloging Data

1. Data Curation Definition
2. ?Why Data Curation
3. Curate Vs. Govern
4. Data Curator Knowledge
5. Curating Through the Data Lifecycle Parts 1-4
6. Curating Big Data
7. Data Cataloging Defined
8. Comparisons Pars 1-2
9. Why Data Cataloguing Parts 1-2
10. Catalog Metadata Parts 1-2
11. Data Catalog Tools

• Rethinking Data Governance Practices

1. The Tensions
2. Policy Enforcement Parts 1-3
3. Policy Complexity Parts 1-2
4. Decision Rights Parts 1-2
5. Process Rigor Parts 1-3

• Technologies and Modern Data Governance

1. Technologies Overview
2. Data Ingestion
3. Data Cataloging
4. Data Preparation