The buzz around big data has created a widespread misconception: that its mere existence can provide a company with actionable insights and positive business outcomes.
The reality is a bit more complicated. To get value from big data, you need a capable team of data scientists to sift through it. For the most part, corporations understand this, as evidenced by the 15x – 20x growth in data scientist jobs from 2016 to 2019. However, even if you have a capable team of data scientists on hand, you still need to clear the major hurdle of putting those ideas into production. In order to realize true business value, you have to make sure your engineers and data scientists to work in concert with one another.
At their core, data scientists are innovators who extract new ideas and thoughts from the data your company ingests on a daily basis, while engineers in turn build off of those ideas and create sustainable lenses in which to view our data.
Data scientists are tasked with deciphering, manipulating, and merchandising data for positive business outcomes. To accomplish this feat, they perform a variety of tasks ranging from data mining to statistical analysis. Collecting, organizing, and interpreting data is all done in the pursuit of identifying significant trends and relevant information.
While engineers certainly work in concert with data scientists, there are some distinct differences between the two roles. One of the fundamental differences is that engineers place a decidedly higher value on “productional readiness” of systems. From the resilience and security of the models generated by data scientists to the actual format and scalability, engineers want their systems to be fast and reliably functional.
In other words: Data scientists and engineering teams have different day-to-day concerns.
This begs the question, how can you position both roles for success and ultimately extract the most meaningful insights from your data?
The answer lies in dedicating time and resources to perfecting data and engineering relations. Just as it’s important to reduce the clutter or “noise” around data sets, it’s also important to smooth any and all friction between these two teams who play vital roles in your business success. Here are three critical steps to making this a reality.
It’s not enough to simply put a few scientists and a few engineers in a room and ask them to solve the world’s problems. You first need to get them to understand each other’s terminology and start speaking the same language.
One way to do this is to cross-train the teams. By pairing scientists and engineers into pods of two, you can encourage shared learning and break down barriers. For data scientists, this means learning coding patterns, writing code in a more organized way, and, perhaps most importantly, understanding the tech stack and infrastructure trade-offs involved with introducing a model into production.
