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Introduction to Data Governance and Assessing Data Maturity (DATA-1292)

3 days
DataBig DataSQL

Learn data governance fundamentals and how to assess data maturity using ISO/IEC 38505. Build a vision, framework, and roadmap for better data outcomes.

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$2,729.30
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Course Overview

This course introduces the fundamentals of data governance and provides practical guidance for assessing your organization’s data maturity. Using the IEC/ISO 38505 governance of data standards and related ISO governance concepts, you will explore how good and bad data affects the business, clarify governance versus management responsibilities, and develop a vision, framework, and roadmap to support a data strategy.

Course Benefits

  • Explain what data is and how data quality impacts business outcomes
  • Differentiate data governance from data management, including roles and responsibilities
  • Describe relevant governance standards, including IEC/ISO 38505 and related ISO governance guidance
  • Assess organizational data maturity and identify maturity levels and drivers for improvement
  • Define data governance principles, components, and common barriers to success
  • Build a data governance business case, including costs, benefits, and implementation planning
  • Select and apply an appropriate data governance model for your organization
  • Outline a data governance framework and develop an actionable roadmap
  • Address governance considerations across the data lifecycle (collection, storage, decision-making, reporting, distribution, and disposal)

Delivery Methods

Public Class
Live expert-led online training from anywhere. Guaranteed to run .
Private Class
Delivered for your team at your site or online.

Course Outline

  1. Data Foundations and Data Quality
    1. What is data? Examples (health and census)
    2. Traditional and modern data-heavy industries
    3. Data quality disasters and business impact
    4. Why poor-quality data exists: drivers and causes
    5. Components of a data solution
    6. Data products and terminology (OLTP, OLAP, dashboards, scorecards)
    7. ETL and reporting tools
    8. Big data and digital transformation
  2. Data Governance vs. Data Management and Standards
    1. Data governance or data management?
    2. Data governance standards and definitions
    3. Responsibilities and who manages data governance
    4. ISO standards in this course (including ISO/DIS 37000 and ISO/IEC 38500)
    5. Data governance focus areas and use cases
  3. Trust, Regulation, and Data Maturity Assessment
    1. Trust issues and impact on directors
    2. Opportunities for data-driven business and risks of unintended outcomes
    3. Regulation of data
    4. Creating the vision for maturity assessment
    5. Data maturity levels (1–5): Data Aware, Data Capable, Data Adept, Data Informed, Data Pioneer
    6. Business drivers and driving data initiatives forward
    7. Top-down analysis and enterprise information architecture
    8. Metadata design and bottom-up source data analysis
    9. Source selection issues and ETL rule specification
    10. Data technologies, data warehouse development, denormalization
    11. OLTP vs. OLAP; OLAP terminology and value
    12. Enterprise data storage; structured vs. unstructured data and infrastructure issues
  4. Principles and Components of Data Governance
    1. Principles and components
    2. The data governance paradox and the hidden data factory
    3. Barriers, failure modes, and setting governance goals
    4. Creating a data governance vision
  5. Organizational Change, Metrics, and the Business Case
    1. Key data challenges and data management metrics
    2. Business culture, communication, and selling data governance
    3. Cultural change planning and communications strategies
    4. Motivation models
    5. Tools and technology considerations
    6. Building a data governance business case (drivers, costs/benefits, implementation plan)
    7. Data governance models: options and pros/cons
    8. Selecting the right model for your organization
  6. Data Governance Framework
    1. Framework examples (DGI, McKinsey)
    2. Framework overview: vision, strategy, organization and people
    3. Implementing a framework
    4. Benefits, deliverables, and actions checklist
  7. Roadmap and Data Management Operating Model
    1. Roadmap and capability mapping
    2. Roadmap checklist and practical tips
    3. Organizing data management: processes, ownership, and stewardship
    4. Stakeholders and requirements errors
    5. Defining goals and identifying key data
    6. Measurement, quality measures, baselines, and improvement targets
    7. Toolset, improvement plans, and the role of IT
    8. Issue logging
  8. Governance Across the Connected Data Lifecycle
    1. Connectivity challenges in a connected world
    2. Accountability map and data accountability
    3. Data collection and quality of collected data
    4. Storage governance
    5. Governance of decision-making (bias and automated decisions)
    6. Reporting strategy, classification, and distribution models (including ENISA Traffic Light Protocol)
    7. Data security considerations
    8. Data disposal policy, triggers, permanent removal, and archiving
    9. Balancing value, risk, and constraint

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

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