Don't be a "data medic" trying to fix your company's poor data and business systems processes manually. There is a better way.
When I ran business systems and data analytics for Nortek, we were plagued, as most companies are, with suboptimal business processes that resulted in dirty data. Analysts and executives would encounter this data when compiling reports. "Why is there such a big outlier here? That doesn't seem correct." As a result, they'd busy themselves with excluding erroneous transactions or fixing incorrect entries in the analysis.
Over time, these issues accumulate. And the people who know how to correct them become the critical "data medics" of the business, which depends on them to constantly fix bad data.
Eventually, even the data medics realize that an extremely large part of their own analysis and reporting processes are taken up by the "data medic" phase. And then someone comes up with the bright idea to create a master data warehouse where all of the data can be scrubbed and fixed and normalized so that analysts can pull good, clean data.
Nortek went down this path. Great in concept, but a nightmare in practice. We spent millions of dollars creating, and especially trying to maintain, such a data warehouse. But in the "no good deed goes unpunished" realm, there were four disastrous consequences of this effort:
The “aha! moment” was the virtually instantaneous turnaround time to fix a problem at its source. The ability to achieve instant gratification in this respect was dramatically different from any other process we had previously attempted. With executive support, we focused our efforts on fixing our processes and data at the source.
Through this process, our data medics gave way to data and application innovators who drove incredible transformation at Nortek. I joined Incorta as CIO to help our customers ride the innovation train that Incorta is helping to power today.