Do Organizations Need Data Governance as a Service (DGaaS)?
Once upon a time (i.e., last year) I was concerned that, when it came to improving their data governance, some organizations were paying too much attention to the interests of IT and “data scientists” and not enough to how data governance efforts need to align with the business.
That may be changing, at least when it comes to what people are writing about. Examples are the following:
All three authors emphasize the need for business goals, not technology, to drive data governance strategies. They also discuss involvement of business and operations staff – not just IT — in prioritizing and managing data governance activities including decisions related to master and metadata management.
None of this should come as a surprise. The more we understand how dependent digital transformation and innovation are on good data, the more we realize how important efficient and effective data management really is.
Naturally a lot will depend on why improvements in data governance are trying to bring about. The most obvious and probably the easiest to document are efficiency benefits. Over time, better data governance can reduce errors, misunderstandings, and miscommunication — along with the costs associated with them. If you have ever tried to combine two customer systems that were based on two fundamentally different approaches to defining customer addresses you will know what I mean.
Of course, improved data quality starting at the data life cycle’s source has long been seen as a way to avoid rework. At the same time, an important question has always been, “When is better data quality worth the cost?” This is the simple application of the traditional concept of cost benefit analysis but to do this well you must first understand the costs you’ll be avoiding through improved data quality.
Understanding what these costs will be, especially in large or complex organizations, can take time and effort. Realistically, though, improved operating efficiency and reduced costs through improved data quality can only get you so far. For example, cost avoidance may not translate directly to profitability. This may not appeal to some in management.
You will also need to understand business impacts of improved data governance that extend beyond efficiency to the organization’s relationship to suppliers, vendors, and ultimately the organization’s customers.
This is where IT staff and data scientists can’t go it alone. As suggested by authors such as those cited above, IT and data science teams must engage with the business in the planning and improvement of data governance, regardless of which of the following data application categories they are pursuing:
Anyone who has helped develop a data warehouse to improve data analysis and reporting will understand how challenging just the first two data applications are.
