Achieving Data Governance Quality

Once an MDM or PIM project is approved, the next question is who runs it. The checklist below is how you stand up a data management department so the model has an owner after go-live.

Identify the need

Name why the organisation needs the department, and which business problems it owns. The usual set is data governance, quality, security, integration, and analytics. Write the problem down before you write the job titles.

Define the scope

Which data sets the department manages, which business functions it supports, and which stakeholders it works with. Put that boundary on paper before hiring.

Assess the current state

Review the practices, processes, and technologies you already have. Mark the gaps and the places that need work. The plan should close those gaps.

Develop the plan

Objectives, timelines, budget, resources, and the metrics you will use to judge success. Include a governance framework, data policies, procedures, and standards.

Build the team

Build a team that can execute the plan. It should include data analysts, data engineers, data scientists, data architects, and data governance specialists.

Implement the technology

Evaluate, select, and implement what the plan requires: data warehousing, data integration, data quality, and data security. The tools follow the scope you already wrote.

Communicate and train

Take the plan and the policies to every stakeholder who creates or uses the data, and train them on the new processes and tools as part of the rollout.

Monitor and measure

Set up a way to watch the department against the metrics in the plan, and adjust when it misses the business need you named first. Standing this up takes time. With the plan and the execution in place, the organisation can manage its data and actually use it.