I posted an older article from HBR on data translators yesterday and I wanted to share some more of my thoughts on the subject.

I immigrated to the states when I was 10 years old. In my early teens, I spent a lot of time translating for my parents and family as they were still learning English. My young brain just soaked it up and I became bi-lingual almost magically.

As I reread this article from HBR, I started to think about my own translator experience. And as I did, it helped me understand what a data translator really does.

From the simplest question about a cable bill fee to the most complicated Medicare application form. My family and the system simply spoke different languages, like actually different languages, Russian vs. English. Even easy tasks were made difficult or impossible because of that. Someone had to translate. There’s another word for translator that perhaps is even more appropriate. Interpreter. It wasn’t just about translating every word, you had to get the essence of the meaning across, interpret. Perhaps we should be using the term Data Interpreter instead, but terminology is not the point.

The need for folks that can do that translation and interpretation in our current data-driven world is the crux of it.

We must admit to ourselves that the business and data people fundamentally speak different languages. But doesn’t mean that one or the other is less capable or talented, take the ego out of it. We need a way to bridge that language gap.

I think one of the main reasons for my personal success as an analytics developer and the success of our Bluetree Analytics team is our ability to become our own data translators. A lot of the time there’s no one dedicated to this task, so by necessity we learn the business and its processes at a near-native level. That allows us to build tools that actually generate the valuable insights. Of course, learning a new business at that level requires time. Time that’s not always there as the business has urgent analytics needs now.

I suggest, in healthcare and really every other business today, we need to invest in developing more folks that can bridge that language gap. Whether it’s taking the operational stakeholders that know your business well and increasing their data literacy or taking data gurus and teaching them all of the intricacies of the business. When doing the former, find people that are ‘good at excel’, that’s always a good signal for data savviness. This will take investment, in people and programs, but if done right, the insights this investment generates will pay for itself over and over.

Thoughts?