When it comes to calculating return on investment (ROI), it always seems to be easier said than done - especially so when it comes to measuring ROI for data initiatives. Businesses invest in data teams, infrastructures, and tools for all different reasons and are executing different projects at various stages of maturity, so it makes sense that it’s not a uniform calculation. But where to start?
First thing’s first: if you’re looking to start quantifying your investment in data with practical advice and step-by-step calculation worksheets, look no further - get the white paper Data Science: What is it Worth? Calculating ROI for Your Investment in Data.
Different Avenues to ROI
The reality of measuring the return on investments in data teams and projects - and especially for data tools and technologies - can be particularly challenging. Therefore, the first step in calculating ROI is to define “success” for the particular business and considering all the ways - directly and indirectly - that data, or a data department, has made contributions. Value can come in many different forms, so part of the work involved is considering all the possible ways that data could be bringing success.
Here are the top five ways to measure data science ROI, which you can read about more in depth and get sample calculations for in the white paper:
How to Increase ROI
One logical question after working out how to calculate ROI, no matter the results of that calculation, is: how can the business increase ROI from data science tools, platforms, technologies, projects and initiatives?
The fact is that simply purchasing a tool or hiring a team to do data science will not magically bring ROI - there is no silver bullet. It takes organization change throughout (from high-level management down to each individual contributor) to get value from data.
So if you're ready to dive in and get started learning about the challenges but also solutions for increasing ROI as well as see sample worksheet calculations, get access to the free white paper for more.