Don't be Gerald Lambeau

In the 1997 movie, "Good Will Hunting," Gerald Lambeau is a math professor at MIT and winner of the Fields Medal, generally regarded as "one of the highest honors a mathematician can receive." It quickly becomes clear to Lambeau that he's not as capable of solving complex math problems as Will Hunting and he lamentingly utters the above quote.

A lot of people in the data community recognize the incredible depth and nuance of skills that makes for great data work -- can be data science, analytics, or engineering. I am frequently daunted by the technical chops needed to handle many modern data challenges. I also find myself in awe of a really beautiful insight done with simple analysis (in past articles and posts, I've shared some favorites). But most often, I find myself disappointed with over-complicated analysis or companies reaching for analysis tools outside of their wheelhouse.

In a data space filled with great, thoughtful communities (on Slack, LinkedIn, meetups, etc.), it's very easy to become insecure about our own skills when we see some incredible projects or answers. There are a vast range of data skills and nobody has them all.

To most of our coworkers, the ability to retrieve accurate and timely data is magical. The skill difference between myself and the best practitioners is certainly not appreciated when answering their most pressing questions. But yes, I know there are certainly (way) more capable, speedy, and sophisticated analysis available. But meeting the bar of delivering accurate and timely results and communicating them well is already high performance (maybe not worthy of a Fields Medal).

So how do we not become Professor Lambeau? It starts with eliminating insecurity around ">" for technical work and to praise the insight over the degree of difficulty. This also speaks to how we're rewarded and recognized (internally at a company). Often the best analysis leverages technique you could learn for "$1.50 in late charges from the public library."