Data Science: What Is It

Calculating ROI for Your Investment
in Data


Spending & The Hype Cycle

Somewhere around five or so years ago, big data was at the height of the hype cycle, and enterprises - not wanting to be left behind - jumped on the bandwagon, investing in and spending on all at once expensive data teams, infrastructure, and often tools that hoped to improve or increase the velocity and output of the team.

At the time, experts claimed that these investments would take years to pay off. Here we are years later, still with billions and billions of dollars in big data-related costs (from staff to tools), still investing and putting faith in data teams. And yet many enterprises still report not seeing a return on their investment and are left unable to prove that any of it is really worth the time and the money.

A recent Gartner press release says global IT spending is set to reach $3.7 trillion in 2018. It goes on to state, “Looking at some of the key areas driving spending over the next few years, Gartner forecasts $2.9 trillion in new business value opportunities attributable to AI by 2021, as well as the ability to recover 6.2 billion hours of worker productivity.”

Yet without hard numbers pointing to success, it is difficult for executives to continue to invest hundreds of thousands (or millions) of dollars on the latest data efforts. Indeed, any enterprise that has a data team, it seems, is reevaluating their productivity and return on investment (ROI). And anyone looking to spin up a new data division is doing their homework first, closely examining costs and potential ROI before diving in.