Data Governance

Data governance can be initiated without having a MDM practice. However you can't have an effective MDM practice without data governance.


Summary of Data Governance


Plan:

1. Strategy & Approach
2. Charter & Process
3. People Involvement
4. Quality Management
5. Control Targets

Implementation

1. Implementation Plan
2. Priorities
3. Measurement
4. Training & Readiness
5. Communication

Govern

1. Manage
2. Maintain
3. Improve
4. Communicate
5. Mature

Starting a Data Governance Model

• Initial assessment and groundwork to drive a sound charter and implementation proposal
• Business driven initiative sponsored at VP level
• Led the program and initiative by Senior Director/Manager
• Data Governance Council (DGC) to have sufficient influence to ensure that data standards, validation rules, and quality control expectations are actively involved in business process areas.

Ten mistakes to avoid

1. Failing to define data governance
2. Ready, shoot, aim: Failing to design data governance
3. Prematurely launching a council
4. Treating data governance as a project
5. Ignoring existing steering committees
6. Overlooking cultural considerations
7. Prematurely pitching data governance
8. Expecting too much from a sponsor
9. Relying on the big bang
10. Being ill-equipped to execute

High Level Data Governance Process Design and Implementation Approach

• Planning and Design Phase

          1. Establishing the Charter

• Distinguishing the Charter
• Agreement on Mission and Objectives
• Define Scope and Jurisdiction
• Identify Roles and Responsibilities
• Set Top Priorities
• Committed Resources and Budgeting

          2. Policies, Standard, and Control

• Define Key Policies, Big Rules, and Quality Standards
• Establish Key Metrics, Monitors, and Improvement Targets
• Identify Data Entry Points and Team Leads
• Establish Quality and Service Level Agreement
• Define Metadata Management Plan

• Implementation Phase

          3. Process Readiness

• Communication of Charter and Implementation
• Readiness of Processes, Tools, and Baseline Measurements
• Completion of Training and Readiness Plans with Core Teams and sub-teams
• Launch the Process

          4. Implement

• Conduct Regular Council Meetings
• Manage Priorities, New Issues, and Requirements
• Review Key Metrics and Performance Indicators
• Communicate Status of Projects and Improvements
• Keep Sub-Teams and Regional Teams Actively Engaged

          5. Maintain and Improve

• Complete Key Improvement Projects
• Identify and Address Negative Quality Trends
• Monitor and Correct Negative Data Entry Process Behavior
• Manage New Data Integration Requirements and Quality Impacts