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I wrote an article a year ago about the Business Glossary Prime Directive for TDAN.com. Simply stated, the Business Glossary Prime Directive is “to eliminate semantic confusion across the enterprise.” There are many implications involved in achieving the elimination of semantic confusion; for data governance, it means that each business term has a unique name, a single definition, value set, set of business rules, authoritative source, and accountable party identified. For our analytics program it means that we have a single definition, value set, set of business and quality rules, and authoritative source for all our business dimensions and facts. Thus, having data governance involved in governing our analytics dimensions and facts is critical to the success of the analytics projects.
From my 20+ years as a consultant implementing analytics projects, I have found that a significant root cause of the challenges in BI analytics success is directly related to the lack of agreement in the definition of the dimensions and facts. Oh, sure, we all think we agree on what a customer is or how to compute customer-lifetime-value, but often we don’t. That is why we have conflicting numbers on our analytics reports for what is seemly the same metric. Yet, none of our differing definitions are wrong, per se, they are just coming from different views leading to semantic confusion. While the problem can be simply stated, the solutions are often very complex and that is why we struggle to achieve success with an analytics project without data governance.
Your business glossary should focus on enabling all analytics processes and people to easily find, understand, and trust the data they should be using, and not the data they shouldn’t be using. Effective governance allows the right people to use the right data for the right business purpose, at the right time with the right technology. We could use data incorrectly if the definition, value set, business rules, authoritative source, and usage limitations aren’t clear. We could make erroneous decisions that increase the risks in doing business. Without a good understanding of the data and its usage, we could create very sexy, technically correct but very inaccurate reporting.
I find that the effective and expedient approach is to begin with the data we have or want to have, both dimensions and facts, on a set of analytics reports. Let’s call this data our critical data elements or CDEs. It’s easier for our business and analytics teams to talk about the CDEs that are needed for each report. You can use this approach with data analysts, business managers, and data scientists. The type of source for the data does not matter to the approach—the data can be from a data lake, a data mart, an application, or even a spreadsheet.
We look at the CDEs for determination of a scope of effort, the scope of the iteration to implement governance. The CDEs at this point can be discussed as the data on the report, the organization and filtering for the report, the columns, the computations of each, and the summarizations of the report. I suggest we label the data governance processes as “governance as you need it.” You need to govern the CDE that will be included in a report or set of reports. This is a very practical approach that seems to resonate with business teams. Using this approach, you can control the scope of the governance project. Try to keep the scope to 50-75 CDEs. This should allow you to complete an implementation in two to four months.
You want relatively short implementation timeframes to:
Again, the data governance objectives are to leverage the business glossary to help data and reporting consumers to find, understand, and trust the data under governance.
Okay, now you could say, “well great, Lowell, I have some CDEs, but now what? How do we get approved data assets under governance and certified BI/analytics reporting?” Well, here’s your answer.
The CDEs provide a scope for the analytics and data governance teams, working in parallel to complete an implementation. I’m going to focus on the activities of the data governance team, but both teams must work together.
Once we have a list of CDEs, then the data governance team can execute a top-down governance effort similar to the following:
Wow, that was easy to put on paper; however, it is not so easy to organize, communicate, educate, and have the resources complete data governance activities in a timely manner. Yet, if you can get the governance efforts completed in coordination with the BI/analytics project, then you should provide significant value and likely be considered much more successful. Just don’t forget that this maturity of alignment is required before you can achieve the Prime Directive. And, remember: stay calm and allow your business glossary to prosper.
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