Peter Ku recently published a blog entitled, “Demystifying Data Governance.” In the article, Ku discusses the common myths associated with a Data Governance initiative. As someone who spends quite a bit of my time talking to organizations about how they can jumpstart their data governance initiative, I am all too familiar with the myths Peter discusses.
In particular, Myth #1, “Data Governance has to be done across the company all at once” is one that I run into the most. In the data governance workshops that I conducted, I often discuss the three entry points into a data governance initiative that are documented in our Professional Services Velocity methodology.
These entry points are:
- Enterprise Initiative
- Top-Level Directive
- Scale-up Projects
Although there is an entry point for the enterprise initiative, I often find that the third entry point around scale-up projects is where most organizations begin their governance initiative. Although there is a need for top-level support for governance to succeed across the enterprise, leveraging the successes and outcomes of a specific project is a logical starting point. In today’s economy, funding and resources continue to be constrained.
Thus, building the momentum and support needed for enterprise data governance can be achieved by leveraging the results of a specific project. This approach is a great way to provide immediate value and build a repeatable foundation for other initiatives.
This being said, where is a good place to start? As I have discussed in previous posts, a Data Quality Assessment is a recommended starting point, as it allows you to not only uncover and resolve data issues, but it is also a key component of each of the three pillars of data governance – people, processes and technology.
Through engaging the data stewards (people), by examining a subset of the data to uncover issues and begin to build an initial set of data standards (processes), and through the use of data profiling and data quality software to resolve the uncovered data issues (technology), you will begin to build an initial foundation that can be replicated both from a strategic (governance aspect) as well as a tactical standpoint to resolve immediate data related needs in the organization.
For those of you that have embarked on a data governance initiative – which of the three approaches did your organization take? If you are considering data governance, which approach seems to be the best fit for your company?
