A decade or so ago, when we would introduce the Data Cookbook as a product an organization might be interested in, we generally had to make the case for data governance first, before demonstrating any of the product's features or describing how it helped solve common data-related problems. Frequently, data governance was experienced as an unwelcome, externally derived pressure from legal ("we've got to comply with these regulations") or information technology ("we've got to secure these systems"), rather than as an internal response to market conditions or, as we often described it, as a recognition that data is a strategic asset and ought to be treated like one.
Now, we know that the plural of anecdote is not data, and we know we're as susceptible to recency bias as anyone else, but it does seem that the tide has turned. We don't have to make the case for data governance like we used to; nearly all of the data stakeholders at our client and potential client organizations understand the need for and the value of data governance. Instead, what we hear is more along the lines of, "We don't have the time/tools/resources we need." Including:
- We’d really like to bolster our organization’s data intelligence by cataloging all its data assets, but we don’t have anyone who can administer that survey, or who can follow up with the business units who are slow to respond.
- We’d really like to inventory all of our data flows, reporting deliverables, dashboards, ETL processes, and semantic layers, but we’re too busy patching old/broken integrations, authoring new reports, and/or adding elements to our warehouse/lake/moat.
- We’d really like to settle on a shared data vocabulary and terminology, but our subject matter experts are too busy with their regular work to contribute to this knowledge base. Or our business analysts don’t know enough about the data or its functional usage to feel comfortable signing off on data definitions.
Sometimes, this is obviously code for "I don't want to do this work at all," which we understand. The actual practice of data governance or data intelligence is neither glamorous nor exciting, and particularly at the early stages of organizational data maturity, it's labor-intensive. We might think of this as analogous to a person who desires to be healthy or get in shape, but not enough to stick to the appropriate diet and exercise regimen.
Indirectly, this is a reason we've long advocated for "just in time" data governance, where you publicly and visibly apply the principles of data governance when you're dealing with a data issue you have to deal with anyway. Our library of recorded webinars and blog posts is full of suggestions governing data during a migration, or during the implementation of new analytics tools, or as part of the process of publishing new KPIs and management metrics to internal dashboards. We’d also like to suggest, quietly but firmly, that data governance is not somehow separate from data management or data usage.
Sometimes, "I don't have time" is code for “I don’t want to do this extra work,” which we understand. No one wants to do extra work, especially if they’re not going to be paid more for it! We have observed that many data practitioners already consider themselves underappreciated, for good reason. For many people, the formal application of data governance practices seems like it’s something they don’t have training for or experience in, so that’s additionally daunting. Ironically, nearly anyone who touches data has been informally applying data governance principles, whether that’s classifying data, granting or refusing access to data sets or systems, evaluating reported data to gauge progress in one’s work, fixing or cleaning data prior to analysis, etc. What happens when your people realize how much time they already spend trying to govern data?
We have long advocated for writing data management duties into job descriptions. For one thing, this provides cover for data people who want to incorporate data governance activities into their regular work; for another, this provides a set of skills to screen for when a position becomes vacant. Many of the problems our clients encounter with their data derive from seat-of-the-pants decisions and ad hoc workarounds that date back years or decades; this is one way to help future-proof data operations.
Sometimes, crying resource poverty scans as “This work isn’t helpful (enough) (to me),” which we also understand. If you’re a data consumer, you don’t want to do more work than is absolutely necessary. You don’t want to search for data products, you don’t want to read the annotated details of a dashboard, and you don’t want to contest what the data means you should or shouldn’t do. If you’re a data provider, you’re already overburdened authoring reports, or maintaining data models, or creating views into data; you don’t want to further curate that content for consumers, or wait for additional review, instead you want to hand over the data product and move on! And if you’re a data producer, you’re busy collecting, maintaining, updating, perhaps occasionally archiving or deleting massive amounts of data; you understand why you do what you do, and you don’t really want to explain yourself to people who only care when the data they examine “looks funny.”
Now, we’ve pointed out for years that investing in data governance now will actually save money and time in the future. People won’t have to do duplicate work, units won’t purchase new tools and applications if they know that the functionality they seek is already available, time won’t be wasted arguing about whose numbers are correct, and fewer decisions based on incorrect assumptions will be made. But we understand that it’s difficult for humans to plan ahead, to delay gratification, even to think in timeframes that go much beyond the immediate future.
Still, we continue to be optimistic, and to see reasons to be hopeful. As we noted above, the tide has turned, and organizations that want to use their data effectively understand that they need to know more about their data, and that they need a framework to govern and manage data. Increasingly, data intelligence tools like the Data Cookbook allow for some level of automation and integration. And as we pointed out in the last of our Data Intelligence Best Practices webinars, there are many roles to play in your organization, as it increases data intelligence and ramps up data governance. Even those roles that require much effort don't require that effort forever, and so a planned and patient approach allows for short bursts of intense effort as well as sustained periods of less effort. You may not have all the resources that you want, but we suspect that you might indeed have the resources you need, if only for short periods. So do your best to use them wisely!
IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance, data intelligence, data stewardship and data quality initiatives. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.
(image credit: StockSnap_1O72HIXUNS_longwaystill_openroad_BP #1261)