Data Governance

Data is a valuable organization asset, however it can go to waste withuut effective management. Lack of effective management is a major reason that Data And Analytics (DAA) projects. Data Governance (DG), the overall management of data and information, is the answer. It includes

• Planning: Creating data blueprints and roadmaps.
• Organizing: Determining the organization units, roles, and responsibilities required to manage the data. It can also include a communication plan.
• Staffing: Attracting and retaining the right people. This often means bringing existing employees to the team.
• Directing: Making the appropriate requests of the staff.
• Controlling: nsuring that the requests are carried out in the requested quality manner.

A step by step approach is an effective way to establish a data governance program. In many ways, data governance is about managing change. It involves setting standards, determining policies and deciding on technologies that impact enterpriselevel data management. The steps of data governance are outlined below. Data Governance is an iterative process, where data governance reaches new levels of maturity over time.

1. Assess Data Governance Maturity: Assess areas such as management oversight, strategy, policies and procedures using a maturity scale (often 0 to 5).
2. Design Data Governance Structures: Organiza teams (DG Board) and roles (data owner, data steward, sponsor).
3. Create Data Governance Strategy: Align data and information roadmaps to enterprise vision, mission, objectives and plans.
4. Create Data Governance Policies: Document policies and procedures that will facilitate execution of the Data Governance Strategy.
5. Monitor and Maintain Data Governance: Make sure that the DG effort is sustain through on going oversight.