Data Governance Handbook A Practical Approach to Building Trust in Data
2.5 quintillion bytes! This is the amount of data being generated every single day across the globe. As this number continues to grow, understanding and managing data becomes more complex. Data professionals know that it’s their responsibility to navigate this complexity and ensure effective governa...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
[2024]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009826133306719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright
- Dedication
- Editorial Reviews
- Contributors
- Table of Contents
- Preface
- Part 1: Designing the Path to Trusted Data
- Chapter 1: What Is Data Governance?
- What you can expect to learn
- What's driving the increasing need for data governance?
- What is data governance?
- What data governance is not
- The objective of data governance - create business value
- A brief overview of the data governance components
- Policy and standards
- Roles and responsibilities
- Governance forums
- Reporting on governance progress
- Related teams and capabilities needed for success
- Defining value
- Who to meet with
- Crafting a powerful why statement
- Customizing the message
- Data governance as a strategic enabler
- The mission of the chief data and analytics office
- The mission of the data governance program
- Building a business case for your company
- When and why to launch a data governance program
- Why you should launch now
- Why you might want to wait
- How to build your delivery timeline
- Conclusion
- References
- Chapter 2: How to Build a Coalition of Advocates
- Building relationships with impact
- Building trust one relationship at a time
- Identifying stakeholders
- Building a stakeholder map
- The case for building trust in data
- Landing an executive sponsor
- Identifying and assessing sponsors
- Building a business case to land a sponsor
- A note on translating to business outcomes
- Establishing feedback loops
- Key roles to support you
- How to gain the support of the masses
- Conclusion
- References
- Chapter 3: Building a High-Performing Team
- Optimizing for outcomes
- Common outcomes
- Defining core functions
- Incorporating product management in organizational design
- Three common data organization models
- Establishing the office of the CDO.
- Maturing and empowering through the hub and spoke model
- Driving consistency through the centralized model
- How to select the right model for your organization
- What roles are needed
- CDO versus CDAO
- Data management roles
- Data solutions leader
- AI considerations
- How to structure the team for results (and why)
- Building the rhythm of the business of data
- Enterprise data committee
- Enterprise data council
- Functional roles
- Executive data domain leader
- Business data steward
- Technical data steward
- Talent development
- Recruiting talent
- Growing the pipeline of talent
- Upskilling and reskilling
- Conclusion
- References
- Chapter 4: Baseline Your Organization
- What is a data management maturity model?
- Overview of process
- Why you should baseline data management maturity
- Foundational reasons to baseline
- Executing a data management maturity assessment
- [#1] Defining the scope
- [#2] Identifying stakeholders
- [#3] Selecting a data management maturity model
- [#4] Execute the assessment and collect data
- [#5] Analyzing the data
- Alignment and agreement
- [#6] Communicate the results
- Communicating disaggregated results
- Communicating aggregated results
- Program baseline
- [#7] Develop a plan
- [#8] Implement the plan
- [#9] Monitor progress
- [#10] Reassess your maturity
- Measuring success
- Conclusion
- Chapter 5: Defining Success and Aligning on Outcomes
- Capabilities versus outcomes
- Capabilities
- Outcomes
- Business outcomes and data capabilities
- You need both
- What is success?
- What is the definition of value?
- Defining success
- Aligning on outcomes
- Step 1 - Aligning on the business outcome
- Step 2 - Defining data capabilities
- Step 3 - Defining data capability deliverables
- Step 4 - Aligning on value measurement.
- Step 5 - Delivering iteratively
- Step 6 - Reporting on progress iteratively
- Step 7 - Measuring success in data outcomes
- Step 8 - Measuring success in business outcomes
- Summary
- Barriers to achieving business value
- Building value measures into your stakeholder map
- Conclusion
- Part 2: Data Governance Capabilities Deep Dive
- Chapter 6: Metadata Management
- Metadata management defined
- What is metadata management?
- The value of metadata management
- Why does metadata matter?
- Core metadata capabilities
- Metadata standards
- Business glossary
- Data catalog
- Building optimal metadata management capability
- What is a data marketplace?
- What's in a data marketplace?
- Why does a data marketplace matter?
- Measuring outcomes and return on investment
- Setting up metadata management for success
- Conclusion
- References
- Chapter 7: Technical Metadata and Data Lineage
- Technical Metadata
- Why does it matter? What matters?
- How do you measure the value?
- Which metrics should be used to measure maturity?
- Who manages it?
- What does maturity look like?
- How should you use it?
- Data Lineage
- Why does it matter? What matters?
- How do you measure the value?
- What metrics should be used to measure maturity?
- Who manages it?
- What does maturity look like?
- How should you use Data Lineage?
- Building an optimal Data Lineage capability
- Conclusion
- Chapter 8: Data Quality
- Data quality defined
- Data Quality Strategy
- Data quality enablement
- The value of measuring data quality
- Core capabilities
- Data profiling
- Data cleansing
- Data validation and standardization
- Data enrichment
- Feedback loops, exception handling, and issue remediation
- Building an optimal data quality capability
- Certified data
- Transparency
- Setting up data quality for success.
- The real-time request
- Integrations with other systems
- Conclusion
- Chapter 9: Data Architecture
- Data architecture defined
- Simple wins
- The value of data architecture
- Why data architecture is often overlooked
- Measures of success
- Core capabilities
- Establishing a data architecture program
- As-is and to-be modeling
- Building an optimal data architecture capability
- Establishing design principles
- Developing architectural standards
- Tight integration with business architecture and IT architecture
- Building data architecture into the systems development life-cycle process
- Setting up data architecture for success
- Conclusion
- Chapter 10: Primary Data Management
- Defining Primary Data Management
- Reference Data
- Primary Data versus Reference Data
- Types of Primary Data
- Customer
- Product
- Vendor [or Supplier]
- Contact
- Building an Optimal Primary Data Management Capability: Core Capabilities for Success
- De-duping or Deduplication
- Common Definitions
- Golden Source Attribution
- Hierarchies
- Trust Logic
- Integration
- Quality Third-Party Enrichment
- Consumption Model
- CRM vs. PDM
- What is CRM?
- Key Differences
- The Value of Primary Data Management
- Building the Business Case
- A Note on Scope of Program
- Capability Statements
- Conceptual Architecture
- Directional Objectives &
- Specific Measures of Success
- Business Benefits of PDM
- Conclusion
- References
- Chapter 11: Data Operations
- Defining data operations
- Data operations versus IT operations
- IT and data operations partnerships
- Data operations capabilities
- The value of data operations
- The unsung hero of data governance
- Making data operations more visible
- Building an optimal data operations capability and setting up for success
- Conclusion.
- Part 3: Building Trust through Value-Based Delivery
- Chapter 12: Launch Powerfully
- Assessing readiness for launch
- Performing the assessment
- Common baseline
- Simple and strong core messaging
- Crafting a compelling vision
- As Is versus To Be (aka current versus future state)
- Getting crisp with your messaging
- Writing a narrative memo
- Design based on outcomes
- Creating a repeatable process
- Designing feedback loops
- Setting and meeting expectations in the program launch
- Conclusion
- Chapter 13: Delivering Quick Wins with Impact
- Finding quick wins
- Identifying areas of need
- Rationalizing the list
- Prioritizing the list
- Short-term versus long-term wins
- Organizational readiness considerations
- Investment/funding models
- Follow through
- Communicate effectively for support
- Why policies, standards, and procedures can generate buzz
- Data ownership
- Applying a product mindset to data capabilities
- Product management for data
- Products versus non-product solutions
- Building momentum through a continuous delivery model
- Continuous delivery model
- Follow through
- Conclusion
- Further reading
- Chapter 14: Data Automation for Impact and More Powerful Results
- What is automation?
- What is data automation?
- Types of data automation
- Advanced data automation capabilities
- Benefits of data automation
- Measuring the benefits
- How to determine which type of automation to use
- Step 1 - Identify your goals
- Step 2 - Identify the existing process and pain points
- Step 3 - Agree on the problem statement(s)
- Step 4 - Align on the approach and ROI calculation
- Step 5 - Execute
- Step 6 - Measure and report
- Third-party enrichment
- Data solution examples powered by data automation
- Customer domain
- Operations domain
- Conclusion.
- Chapter 15: Adoption That Drives Business Success.
