This is the second article in an ongoing series on Data Governance sponsored by SAP. You can find the first article in the series here.
Using a Data Governance Maturity Model
Something that Hub Designs recommends early in a new Data Governance program is using a Data Governance Maturity Model to realistically assess how you currently govern data at the enterprise-wide level. This is helpful so that you’ll know where you’re starting from where the four dimensions of People, Process, Technology and Information are concerned, before embarking on your initiative.
Most Data Governance Maturity Models are based on the Software Engineering Institute’s Capability Maturity Model (CMM) for software development.
A Data Governance Maturity Model states what should occur at each level, not how to accomplish the activities. There are many different DG maturity models, but they share some common characteristics: they usually have 5 levels, and they’re just a tool to assess where you’re starting from, not something to get hung up on or on which to spend a lot of time.
The National Association of State CIOs (NASCIO) did a great study of the different Data Governance Maturity Models available from organizations such as IBM Data Governance Council, DataFlux, EWSolutions, Gartner, Knowledge Logistics, the MDM Institute, and Oracle Corporation, concluding that “data governance maturity models can be used as references in communication, awareness building, and the marketing of data governance”.
Where are most organizations today?
A lot of companies are doing governance, just not formally. People are managing data, they just need to figure out who’s doing it, and what and how they’re doing. Then group them and provide some central guidance to get them started.
Here are three things to think about:
- A recent survey of over 100 organizations found that only 10% have been able to move their DG programs beyond the lowest two levels of maturity. IT is still accountable for the data in 63% of organizations. Only 27% have established a data governance council with business representation and formal data stewardship. And 57% of organizations do not measure the performance of data management activities at all.
- Most companies with which Hub Designs has worked have not been successful making the types of organizational, cultural, process and technological changes necessary solely with internal resources.
- Most of the companies with which we’ve worked have a “research & analysis” period of up to two years under their belts before they start making serious progress on their MDM and governance programs.
The reality is that with cross-functional, multi-year programs such as MDM and data governance which involve multiple organizational, process, technology and information disciplines, it’s better to proceed deliberately and have a series of incremental “wins” that show business value, rather than go for a rapid implementation or a “big bang” approach.
Progress in this area is not linear – you make investments, you build competency, you do the hard work (and sometimes even get frustrated), and then by doing a relatively small amount of remaining work, you start to achieve your goals.
Like a baby learning to talk, or an adult learning a foreign language, there’s a steep learning curve that flattens out rapidly at the top. Companies will seem not to make progress for a long time (while they’re making the investment in growing their data governance capability), and then their abilities will come together rapidly, as people get a chance to put their new skills to work in a new Data Governance Organization.