Data Governance and Risk Management

Data Governance Overview

• Combines legislative-like (policy definition), judicial-like (issue management), and executive functions (administrative responsibilities).
• Aims to manage risk through a representative governance model, ensuring all stakeholders are heard.
• Governance models must align with the organization's strategy and cultural context; adaptability is essential for meeting new challenges.

Data Governance vs. Data Management

• Data Management encompasses the processes for gathering, storing, organizing, and maintaining organizational data.
• Data Governance oversees Data Management practices, ensuring responsibilities are clear and effective procedures are in place.
• Emphasizes the distinction between "doing things right" (efficiency) and "doing the right thing" (effectiveness), questioning the need for formal structures.

Importance of Documentation in Data Governance

• Essential for staff induction and training, accountability, and compliance with external regulations.
• Clear documentation fosters consistency in data handling and supports organizational accountability.

Key Components of Data Governance

• Encompasses people, processes, and technologies to manage and protect data assets, ensuring data is understandable, trustworthy, and secure.
• Requires a partnership between business and IT to facilitate data distribution, access control, and quality assurance while avoiding redundancy.

Organizational Leadership in Data Governance

• Data Governance should be led by business needs with input from IT, balancing strategic and operational demands.
• Management should establish data principles, emphasizing leadership commitment and recognizing data as a critical asset requiring planning and management.

Data Lifecycle Management

• Data is considered an asset that exists through a lifecycle, requiring effective management strategies that adapt to different lifecycle characteristics.
• Recognizes that data represents both value and risk; organizations must manage ethical considerations and potential misuse within the data lifecycle.

Data Management Principles

• Effective data management hinges on leadership commitment, coordination, and collaboration across the organization.
• Technology should serve strategic data needs rather than dictate them.

Data Management Knowledge Areas

• Data Governance outlines proper actions regarding data processing, decision rights, and escalation processes.
• Key knowledge areas include Data Architecture, Data Modeling, Data Storage, Data Security, Data Integration, Document Management, Reference and Master Data, Data Warehousing, Metadata, and Data Quality.

Importance of Formal Data Governance Programs

• Organizations with formal Data Governance programs can exercise greater control and derive more value from their data assets.
• Effective governance increases intentionality in decision-making regarding data management.

Learning Structure

• Week 1: Concepts of Data Governance (scope, reasons for growth, definitions, DMBOK introductory wheel).
• Week 2: Application of concepts (roles, DG operating models, management strategies).
• Week 3: Implementation strategies (Ladley’s 8-phase DG process, data quality, risk management, models and tools).