A planned and structured approach is essential for successful operationalization of a data governance and privacy programme. The objective is to create a scalable approach that supports the growing acceptance and expansion of the DG initiatives.
Data governance and
Laying down a fully operational data governance framework can take months if not years of well planned tactical moves. It is important to tackle the initial obstacles such as a lack of shared knowledge of data across the organization and isolated interpretations of data.
The following steps are to
- Identify and prioritize focus areas
- Bring participants onboard
- Develop initial operating procedures
After designing the foundational layout, the pilot processes are executed. The organization undergoes certain changes in terms of technology and culture.
The success of a data governance initiative is dependent on the fruitful collaboration of business users, data leaders, and data governance experts. Onboarding the appropriate resources at the right time is critical.
Identifying the requirements for a certain phase in the data governance journey and deploying the right assets need to happen like clockwork.
Roles such as application specialists, business project managers, data architects, and data stewards, among others hold seminal importance in the data governance journey.
Data governance has to be justified by qualitative and quantitative measures of potential benefits and the risk involved or a business case. It is important for the stakeholders and project sponsors to have a clear picture of metrics like cash flow, net present value, return of investment, and payback period.
Creating inventories of
reusable objects and
If a scrutiny of the business requirements show some common routines or modules that may appear in multiple data movements, these routines can be treated as reusable objects.
Factors such as the complexity and usage frequency are assessed to qualify a reusable object. Its scope is determined by whether the objects need to be shared within or across repositories.
Key Performance Indicators measure a definition of what makes a business successful. Building data quality solutions around these KPIs helps stewards quantify the data quality related problems as business costs. This helps them build a data quality business case and also contributes to the solution itself.