Definition

According to DAMA (Data Management Association) International, “Data governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.”

This definition focuses on authority and control over data assets.

Modern and contemporary definition – “Data governance is the formalization of behavior around the definition, production, and usage of data to manage risk and improve quality and usability of selected data.”

This definition focuses on formalizing behavior and holding people accountable.

Definition and importance of data governance has changed over time and there are hundreds of resources available everywhere.

Traditional Approaches

The traditional and centralized approach:

• Data-first approach to data governance represents a command control.
• Defensive strategy focused on enterprise risk mitigation at the expense of the true needs of those who work with data.

The Data Governance Office:

• Develops policies in a silo and
• Promulgates them to the organization.

This approach

• Adds to the burden of obligations on data users throughout the enterprise who have little idea of how to meet the new and unexpected responsibilities.
• Only 20% of projects are successful (Source: Gartner Analysis)

Contemporary Approaches

The modern and active approach:

• People-first approach to data governance
• Empowers people with a
• Balance of access and guidance.

This approach:

• Guides the people who work with data on compliance best practices.
• Users are empowered with access, but nonetheless expected to contribute to the repository of knowledge about the data and follow guidelines, rather than rigid, prescriptive procedures.
• Engages the community and drives broader adoption.

A Hybrid Approach to Data Governance

• Formalizes what already exists.
• Starts with people and Data Governance Office
• Actively guides people to govern data.
• Data governance represents a command control.
• Collaborative, not bureaucratic
• Iterative, not waterfall

Key Characteristics

• Pragmatic, not theoretical
• Data and Business-centric with Risk Model Incorporated
• Continuous Improvement
• Intelligence
• Incremental, not ‘big-bang’, implementation
• Community driven but committee supervised and controlled.
• Guided, not gated, but supervised participation.
• Employee value, not steward & IT centric value.
• Data usage, not documentation

Objectives

• Remove data silos within organization.
• Create a group with an enterprise data architecture in mind.
• Standardize common cross-functional terms and processes.
• Ensure that data is used properly and protected.
• Improve data quality and integrity to decrease manual activities.

Guiding Principles

• Do not create overly complex and bureaucratic processes. Balance adherence to framework with operational flexibility.
• Set operationalize guardrails but do NOT impede growth.
• Ensure both current AND future state data is governed.
• Automate governance where appropriate and ensure ability to measure progress.

Hybrid Data Governance Framework Implementation

Establish Governance Framework

• Being established now.
• Living Document and Framework
• Publish and share established library.
• Establish, publish and share input, request, project, intake process and tools.

Populate Data Catalog

• Begin populating now.
• Continue development and publish.
• Publish and share established library.
• Review periodically.

Empower Business Stewards

• Invite and share libraries of framework assets.
• Educate business users.
• Encourage active feedback and act on them.

Curate Assets

• Establish Inventory of assets.
• Establish asset and business relationships.
• Curate data continuously.

Apply Policies and Controls

• Implement policies and controls.
• Risk assessment and mitigation.
• Apply Request/Project Intake and governance processes.

Drive Community Collaboration

• Collaboration through sharing, educating and actively engaging in practical project activities and data decisions.

Monitor and Measure Curation and Success

• Establish data driven KPIs to measure data quality, data usage, performance, business value and confidence level and business impact.
• Monitor and improve KPIs.

Happy cataloging!