May 14, 2013
There are multiple maturity models and associated assessments for data governance on the market. Some are from software vendors, or from consulting companies, which use these as the basis for selling services. Others are from professional groups like the one from the Data Governance Council.
They are all good — but frankly not adequate for the data economy many companies are entering into. I think it is useful to reshuffle some too well established ideas.
Maturity models in general are attractive because:
- Using a maturity model is nearly a “no-brainer” exercise. You run an assessment and determine your current maturity level. Then you can make a list of the actions which will drive you to the next level. You do not need to ask your business for advice, nor involve too many people for interviews.
- Most data governance maturity models are modeled on the very well known CMMI. That means that they are similar at least in terms of structure/levels. So the debate between the advantages of one vs another is limited to its level of detail.
But as firms move into the data economy — with what this means for their sourcing, analyzing and leveraging data, I think that today’s maturity models for data governance are becoming less relevant – and even an impediment:
- Maturity models are useful for very stable and well known capability domains. But with big data, cloud, BI self-service, extended enterprises, personal information management, and open data, to cite a few big changes, data governance will evolve drastically to a more business oriented governance reflecting a new set of business decisions.
- Maturity models tend to focus on the “quality and automation of process.” The business rarely sees the correlation between maturity levels and the impact for their own objectives — and on business results.
- While adopting these different initiatives or technologies the data governance maturity level of enterprises will unavoidably decrease. Getting next maturity level was often the only justification for investments. How then to justify to the business that the maturity level is drastically decreasing?
In the data economy, there is not a single data governance framework which works for all firms. Each will be different and highly tailored to the (data) business model. As data governance advisors, we should recommend our customers start now to learn a different approach: derive data governance objectives from business and IT objectives, and use that to drive governance structures, processes, and criteria. This brings coherence between objectives, organization design and data governance architecture.