Supporting your data science team takes a lot more than finding top notch data scientists, data science project managers, and directors. Those things are crucial, but are only the beginning of the story.
When it comes to world class data science operations, you’ve got to be thinking outside of the data science box just as much as inside. For the sake of this article, we’re talking about the adjacent teams; all those people and products that depend on data science, and just as critically, the people and products on which data science depends.
The powers of the magical, mathematical “black box” of a high-end data science team can only be properly leveraged if it’s plugged in to an equally high performing ecosystems of adjacent teams and tech.
Data science magic can’t happen without data. This usually goes without saying. But in the world of data acquisition and delivery, there’s a lot to think about, and a lot of ways that the data stream can run dry, leaving your data science team with a lot of unused cycles, or worse, frustration.
In your own context, who is responsible for things like finding new data, processing incoming data points, and building software for data extraction? Are all these tasks being tackled piecemeal by your data science team? If so, why not have a dedicated team (or sub-team) for it?
By spinning up a committed group of sharp devs to handle your incoming data, you gain some powerful advantages. Chief among these are reliable, consistent, and replicable data sourcing. Rapid experimentation by your data science team isn’t possible if they can’t get truly consistent, replicable data sets to experiment on.
A second, related benefit, is that as your creative folks realize new possibilities for data extraction or input processing, they’ll have (Read more...)
This is a Security Bloggers Network syndicated blog post authored by Hailey Buckingham. Read the original post at: Cylance Blog