The collection of policies, processes, and standards around how your organization captures, manipulates, stores, and accesses data makes up your data governance approach. Whether you’ve been working on data governance for months or years, you might be asking yourself: Is it working? Is our investment in data governance paying off?
More likely, someone else is asking you this question. And it’s probably your CFO or CEO. How do you give them the answer they need?
Fortunately, you can test your data governance just like you might test a pipeline, an API, or a web app. The trick is to remember that data needs a constant infusion of intellectual and emotional energy to maintain its value. Here’s an analogy to explain what this means. If you leave a house untouched for years, you’ll come back to dust, cobwebs, and possibly broken or unusable appliances and household items. Just like houses need continued attention and maintenance to ensure that they remain livable, collections of data need ongoing attention to remain viable.
To identify your test cases, do this thought experiment. Imagine what would happen if your entire workforce dedicated to data ceased to exist. Your pipelines will keep running (for a while), your data lake will keep filling up, many of your reports and dashboards will still be prepared and used (thanks to the automation you’ve skillfully developed over the years), and your data suppliers will continue operating as usual (remember, only your workers disappeared). So new data, in new formats that you may or may not understand, will keep flowing into your ecosystem.
- What will break first, and who will suffer?
- What critical business capabilities will you lose, and why?
- What parts of the data ecosystem will be difficult or impossible to understand?
- What silos will begin to form?
- Where will confusion block the ability of your business to use the data?
- What themes around poor quality data will start to emerge?
- Where will you experience lack or loss of control, for example, in maintaining GDPR compliance?
Your answers to each of these questions will provide at least one valuable test case. As an example, let’s look at the last question. When posed to a retail client, three business leaders quickly recognized that without a data team in place, a European user’s right to be forgotten would be impossible to satisfy at their company. They established policies, standards, and practices to coordinate their efforts and regularly audit their effectiveness.
To test the effectiveness of governance frameworks for that case, the retailer performed a what-if exercise. Members of the data team and its stakeholders imagined how they might address the right to be forgotten from a process perspective, and determined whether this governance approach would keep them in compliance with GDPR regulations.
If governance works, an army of coordinated invisible hands will be protecting your company’s data as it flows in, is transformed, and is translated into business value. Without data governance, the quality and utility of your data will degrade.
Anticipating these outcomes by imagining the consequences of a vanishing data team can help you tune your approach to governance, ensuring that your most important business goals are met. You’ll also have an easier time explaining the financial value of data governance in terms of the losses that you’d be incurring without it.
Ultranauts helps companies establish and continually improve data quality through efficient, effective data governance frameworks and other aspects of data quality management systems (DQMS), especially high impact data value audits. If you need to improve data understanding at your organization, Ultranauts can quickly help you identify opportunities for improvement that will drive value, reduce costs, and increase revenue.
Brous, P., Janssen, M., & Krans, R. (2020, April). Data governance as a success factor for data science. In Conference on e-Business, e-Services and e-Society (pp. 431-442). Springer.