When it comes to calculating return on investment (ROI), it always seems to be easier said than done — especially when it comes to measuring data analytics ROI. 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. Where should you start?
First things 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 for Analytics
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 for analytics is to define “success” for the particular business and considering all the ways — both 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 driving success.
Here are the top five ways to measure data analytics ROI, which you can read about more in depth and get sample calculations for in the white paper:
How to Increase ROI for Analytics
One logical question after working out how to calculate data analytics ROI, no matter the results of that calculation, persists. 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 organizational change (from high-level management down to each individual contributor) to get true value from data.