How to Measure the ROI of Business Intelligence Projects

Measuring the effectiveness of a Business Intelligence project in terms of ROI can be a difficult task. Oftentimes, the bulk of what is generated from an analytics initiative is intangible. The folks at Better Buys published a post recently on this very topic, which also includes some insight from Eldad Farkash, the CTO of Sisense. Farkash explains: “With BI, businesses greatly increase engagement and agility, transforming their employees from process-driven to data-driven as exploring data and reaching new insights becomes easier and faster.”

Farkash goes on to outline how organizations can measure ROI:

• The speed and capabilities of the tool are important factors in how long it takes users to go from high-level aggregate data to granular data
• Explore the scope of the data that’s being extracted after analysis
• How fast are users gaining access to the data they need?
• How granular does the data end up being?

Other areas that companies should look at when attempting to evaluate ROI metrics are access to centralized data, data integrity, time spent handling data, end-user BI tool adoption, decision speed, and bottom-line impact of specific decision as a result.

Business Intelligence tools do a lot well, and they absolutely help companies uncover insights from the data they collect, but the process is what’s ultimately most important. Business Intelligence is about far more than just hard numbers, and a lot of the time, the real profit comes in the form of transforming the culture of an organization. Long after a company is done using a specific solution, what will remain are the processes that business users were taught when they first used it. In that, BI tools can be great methods of training for those who don’t have a background in data science.

This post was inspired by an article which originally appeared on Better Buys.