There are few words in our business language as terrifying as change. Change is always uncomfortable, and when you start looking at the implications of changing deeply-rooted practices for data storage, regulation, and compliance, this terror is understandable.
GDPR and Metadata Management
Most of the time, regulation change means chaos. Just look at how companies have scrambled over the past few years in the wake of GDPR (General Data Protection Regulation). This watershed regulation is changing how companies store,
And GDPR is only the beginning. The U.S. has started enacting its own data privacy regulations that present many of the same challenges to business intelligence (BI) and reporting. When change arrives, companies have only two choices: Adapt, or get left behind.
Small Changes Create Big Impacts
It’s normal to be wary of change. Every new regulation is like throwing a monkey wrench into your IT system’s tightly-connected processes, and even small changes can create big problems.
For an example, let’s look at Israel. Just a few years ago, the country determined that there were too many cars on the road and decided to increase the number of digits in its license plates from 7 to 8.
It seemed like a simple change, but even a minor adjustment like this had drastic implications for the country’s government organizations, private enterprises, and citizens. Indeed, calling it a simple “license plate change” underscores the severity of the update. In fact, the change required a significant overhaul of the government’s IT systems, with costs running into
And when you look at it from a BI perspective, it’s not hard to see why. Just imagine how much chaos a simple change like this could cause to an unprepared organization. If an insurance company only had the first 7 digits of the 8-digit field, policies would be inaccurate, records wouldn’t be tied to the correct users, and the overall system would fail.
BI teams would need to find every single field in every single system that contained license plate numbers and guarantee that each was accurate. Just imagine doing this process manually! Calling it tedious is an understatement; try borderline impossible. The necessary metadata would vary across systems, and there are few ways to collectively identify every single location requiring a change.
In short, utter chaos.
Automation Streamlines Change
Now, just imagine the above scenario, only with a different type of BI platform.
This platform would trace data lineages across every system and create a full database of information that BI teams could search. It doesn’t matter if the fields are named differently across systems or if specific metadata points are labeled incorrectly; the platform automates data discovery and creates a comprehensive view of all information.
Metadata management automation would enable the abovementioned insurance company to automatically locate every single license number field across the multi-system BI environment in order to be able to make the required modifications. Machine learning makes accuracy a non-issue – the technology will locate every single location associated with license numbers so that the BI developers can extend the field contents. From a regulatory standpoint, the company would have revolutionized its error-handling capabilities and built a more cohesive system of data discovery more resistant to disruption.
This type of automation is fast becoming essential in a world where data regulations are fluid and ever-changing. Through automation, BI developers can compile metadata from every data asset and understand how to better implement changes quickly and accurately—no matter what new regulations come their way.