5 Key Considerations for Data Governance
Here are five important considerations in planning for a data governance program, as outlined by Infosys expert Santosh Sardesai.
Basic groundwork for data assessment starts with identifying key data elements for assessment and their best source. Extracting data from a source and getting the access to live data is another big hurdle. Transformation programs in large organizations may require access to 100 percent live production data.
Business SMEs and application SMEs should be aligned on metadata management, defining data validation rules and additional data analysis required to support data cleansing or data transformation during the resolution of issues. Care should be taken that the resolution, tactical or strategic, supports the policies and is signed off by the data governance forum. Tracking and monitoring progress on these issues should be done with care.
Alignment of a cross-functional team for data governance workshops is another big challenge. Availability of SMEs is important and can be a major issue. Business managers should be informed of resource requirements for workshops. Carefully planned workshops should have a clear agenda and involve key decision-makers. Clarification of queries and issues should be well-documented and signed off by key business stakeholders.
It is necessary to coordinate with the IT team for the availability of the hardware platform, installation of required software licenses and application of latest patches. Vendor support can be another big challenge and must be closely managed. If care is not taken in mitigating these risks, data governance program timelines can be impacted.
It is necessary to inform the stakeholders about their roles and expectations upfront. The data governance program needs to align with key business stakeholders and business leaders regarding the roadmap, approach, high-level plan, scope and key dependencies.
Click here for Sardesais full article, entitled Enterprise Data Governance: A Practical Approach to Information Quality Management.
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No project is without challenges, but data governance has its own particular breed of obstacles that can wreck timelines, costs and scope.
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