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Steps taken to develop a mature data program and best practices developed


We are in the process of developing data governance maturity and would like to get insight into the best practices taken.  We have made progress. We have the domain level of stewardship rolling out.

1. We would like insight into what was the roadmap and the journey to achieve maturity?

2. What steps did organizations with mature data programs take to develop data stewards maturity and cultural transformation?

3. What was the lessons learned?

For instance, USAA has the most mature data governance and most established analytics program.  If I was USAA, what did my governance team structure look like in year 1, year 2, and year 3, before achieving data maturity? 

How did a company like USAA start and how long did it take them to "mature"?

What was the structure for their data program in year 1, year 2, and year 3?

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Hi, Florence.  First of all, I would say data maturity is not something organizations ever really fully achieve---it's a process and an ever-evolving one at that.  Data represents your organization, and the organization (as well as the external environment) is always changing---in terms of goals, strategies, business models, regulatory burdens, policies, personnel, ever-proliferating data systems, etc., etc.

That said, I really like the Stanford Data Governance Maturity Model for its thoroughness (attached---whether Stanford is actually doing all this or just has a model I have no idea!)  Most organizations do start with assigning data stewards, because you need delegated staff to do the data governance work.  Developing a data warehouse(s) and creating a metadata dictionary tend to occur together for obvious reasons. The data quality, formalization, and awareness pieces seem to be more difficult.  As far as formalization goes, the people with the power to make policy often don't understand the issues (and won't give policy-making authority to the people who do); data quality relies on processes like validation and verification which tend to fall by the wayside as soon as a someone needs data in a hurry; and of course with awareness we are talking about organizational culture shifts which are always a challenge. 


Hi Katherine,

We are in the insurance space so as a whole our industry tends to be laggards when it comes to a data maturity model. Thank you for your insights into the topic. A lot of what you have expressed above makes sense.

Thanks, Florence. 



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