One of the most important business questions about big data is how do you accurately measure return on investment (ROI)? This question is continually asked by marketers and big data experts alike, yet the answer seems to constantly elude them all.
To calculate big data ROI, we must first understand what big data really is. Opinions about the definition of big data vary widely. CTS prefers using the phrase “accelerating enhanced data,” to describe the practice of mining large sets of data to gain valuable business insights. You can read about how we define big data here.
Secondly, we should define ROI. ROI is basically the measurement of a project’s value. It is the most common technology cost justification method. Before top managers invest in large IT projects, they want to understand a project’s potential payback. When it comes to big data though, that value is derived from the insights discovered in the data sets, which doesn’t come in the beginning of the project. The biggest challenge in measuring ROI for big data is that potential insights are largely uncertain when the project starts.
The methods used to evaluate the ROI of IT projects don’t apply when attempting to measure the ROI of a big data project. Here is an example; a company is considering moving its servers from a hosted solution to the cloud. Using the traditional net present value method, a person can estimate the amount of effort involved, calculate the cost savings across the project’s lifetime, and assess the initial resources required. If the net value is positive, the project goes forward.
With a big data project, the value comes from enabling insight, which comes later in the project. The value is derived from revenue growth or improvements in operations, which means that IT can’t measure it ahead of time. To complicate things more, marketing, which should be able to measure the value of insight in terms of added revenue, can’t do so at the beginning of the project simply because the insight (or data) itself does not yet exist.
If conducted correctly though, big data certainly offers the potential to significantly boost your business’ bottom line. One of the keys to justifying the investment of gaining valuable insight into your company is to get everyone on the same page when it comes to spending money on what could potentially change your business for the better. The only way to get everyone united for the cause is to get everyone to understand the potential of big data. In order to do that you have to first justify the investment before spending a significant amount of money on analytics. The best way to justify the ROI is to start small and get results quickly.
Start the process with one defined use case (such as internal business practices), find expert help, execute one proof of concept, measure, refine, repeat, and re-compute the ROI to ensure that the big data project is worth the investment.
Running a successful big data project really starts with developing a concrete strategy. Once you’re ready to proceed, get IT and business professionals to unite under the same goal. In the long run, organizations that are the most successful in big data analytics will be those that build efficient data R&D infrastructures, and persevere through investigation, execution, tactics, and production.