The phrase “data science platform” has been bandied about a lot recently — at conferences, in market research, and in tech publications like this one. Forrester named data science platforms a top emerging technology last year, and companies using data science at an enterprise level are being wooed by offerings in a rapidly expanding marketplace of platform providers. But what is a data science platform, really? And is it more than just a buzzword?

First, a definition: Data science platforms are meant to encompass the whole of a data scientist’s work. That means they typically provide tools that help users integrate and explore data from varied sources, build and deploy models, and make the outputs of those models operational. Essentially, this suite of tools is meant to keep data science work transparent, reproducible, and scalable — and make it easy for a data scientist to push dynamic results (like the predicted outcomes of ad campaigns) to the people who make decisions based on those results, replacing or supplementing static (and quickly outdated) reports.

These platforms are no flash-in-the-pan-product, either. Data science as a profession has blown up — data scientists have had the best job in the United States for two years running according to recruiting site Glassdoor, and data science teams at Fortune 500 companies like Cisco number in the hundreds — and enterprise-grade technology is just beginning to catch up to demand. How do I know? We asked Forrester Consulting to hold a barometer to the industry to find out if — and why — businesses are using platforms*.