This is part 4 of a 4 part series on Data Governance, taken from a paper written by Manager Partner Virginia Flores.
Why do the majority of data governance initiatives fail? Some of the most common barriers to data governance include the following:
- No senior level sponsor to act as a champion for data governance. In an initiative as broad reaching as data governance, there has to be a senior person responsible for making data quality a strategic priority; assuring that data quality will be used to enable business processes; and finding ways to make data quality attractive.
- Lack of business buy-in at all levels. The departments involved in the initiative have to be committed to not only initiate the data governance project but also live it on a day-to-day basis.
- The cultural of the organization. In the majority of organizations, data has been segregated into data islands or system-specific stovepipes by departments wishing to either protect their data from internal/external influences or simply because the department is “turf” conscience. If data cannot be accessed or shared, there no comfortable level by any other department that data which is shared is current, correct, relevant, secure from inappropriate use, yet readily available to those who should have it to take timely and appropriate action.
- Underestimating the amount of work to be done, both initially and on an on-going basis. Even if you have high-level commitment, organization structures and processes, you still have to get someone to actually do the work. Who determines the data fields and business algorithms (data transformations) that need to be defined and documented? Who prioritizes the list? Who meets with all interested parties and gets agreement on the definitions? Who makes the business decisions when there is the inevitable disagreement? Who documents it all? Who implements these definitions in the applications, databases and reports? This is a lot of ongoing work. Often the right people are not assigned to the tasks, they are not given the proper authority, they do not have the time, or not enough people are assigned.
- No roadmap. The way you would never take a long trip without a map, embarking on a data governance initiative without a plan in place to address the following will have dire consequences for the initiative:
- Existence (whether the organization has the data)
- Validity (whether the data values fall within an acceptable range or domain).
- Consistency (for example, whether the same piece of data stored in multiple locations contains the same values)
- Integrity (the completeness of relationships between data elements and across data sets)
- Accuracy (whether the data describes the properties of the object it is meant to model)
- Relevance (whether the data is the appropriate data to support the business objectives)