Introduction to Data Governance

Emilio D'Souza

2022-02-15

Introduction to Data Governance Course

Authors: Nicola Askham & Rob

Learning objective

Understand how to design & implement a data governance framework.

  • Module 1: Data Governance Strategy
  • Module 2: Data Governance Framework
  • Module 3: Data Governance Deliverables and Implementation
  • Module 4: Delivering Data Governance as a Consultant
  • Module 5: Implementing a Data Catalogue

What is Data Governance?

Laymans definition: Proactively managing your data, to support your business achieve its strategy and vision.

Business definition: The capability to design, build and use high-quality data assets; by making it possible to take decisions over and control its data.

Technical definition: Overall management of the availbility, usability, integrity and security of enterprise data.

Data Governance is not:

  • Data Protection
  • Data Retention
  • Data Security
  • Big Brother

Data Governance benefits

  • Manage risks and compliance
    • Correct business stakeholders are involved to make decisions about data
    • Understand the impact of data breaches
    • Enforce accountability, policies, processes and standards (internal)
    • Protect reputation with customers and suppliers
    • Enables compliance with regulatory requirements (external)
  • Support quality and efficiency
    • Understand your data
    • Supports Master Data Management (MDM)
    • Enable a single source of truth / customer view
    • Reduce time spent finding and fixing data
  • Support business strategy
    • Enable innovation at pace

Key Challenges

  • Cultural change
    • Must not be seen as a one-off project
    • Change Management is needed!
  • Data is not sexy
    • Focus on the outcomes of what data governance enables
  • Not the top priority
    • Explain the value of Data Governance as it relates to top priorities
  • Lack of scope and roadmap
    • Resource and implement in a phased, iterative approach
  • Legacy systems
    • Identify the constraints and limitations of legacy systems early!

Relationship with other data management disciplines

Data management is the umbrella term for everything shown on the wheel. Data Governance is one of the data management disciplines.

Data Governance is shown in the centre as it supports the other disciplines.

DAMA DMBok Wheel

Data Governance shares a very close relationship with Data Quality and Master Data Management (MDM).

Business definition:

  • Data Quality: Ensuring that data is accurate and fit for its intended use or purpose
  • MDM: The capability to establish and maintain high quality, shared master data assets through clear design and implementation of governed data management

Laymans definition:

  • Data Governance is the framework, policies, roles and processes
  • Data quality is the monitoring, validation and correction of data
  • MDM is the management of master data using processes to match, merge and manage data quality

"Data Quality without Data Governance is tactical at best" - Nicola Askham

Data Governance Strategy

  1. Why Data Governance?
    • What are the drivers for doing Data Governance?
    • What is the client's corporate strategy? i.e. their objectives
    • We need to establish how we will interest senior stakeholders
    • Align their interests with the the outcomes of data governance
  2. What's our End Goal?
    • Base realistic end goals against where they are in the maturity curve
    • Develop a roadmap