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Proving the value of data and analytics initiatives is a tricky business, but one that’s becoming increasingly important. In the recent Peer Exchange Series event 35 data and analytics leaders discussed the techniques to use in order to successfully communicate the ROI from data.

Read the key takeaways to understand:
  • Why some companies are not ready to calculate ROI
  • Practical tips to calculating the value derived from data
  • Pre-requisites for successful data valuation

ROI: Proving the value of D&A

This discussion provided fertile ground for many differing views. One group suggested that the problem often starts with the fact that becoming data centric needs the building of an infrastructure which is very difficult to justify through a ROI exercise.

The building of use cases can be associated with a ROI but, through experimentation, there can be significant changes which could render the initial ROI study obsolete.

One possible solution could be to build each use case one after another within a predefined data architecture target. Then each business case can start with a ROI study (benefit, cost, effort). The end use case may have completely evolved into a new idea unrelated to the initial ROI idea study.

The data maturity of the management, being flexible about the ROI, will be the key to success.

The ROI of analytics and data remains an elusive prey…but this session helped me begin to look at the challenge from different and fresh perspectives.”

Whilst it was generally agreed that data literacy among business users is vital to be able to accurately measure ROI, not all organisations could state that they were ready to measure the ROI of D&A.

Among those that were able, one commented that they struggled to identify a positive effect from data or a solution from other changes in the marketplace or processes.

Some felt that it was relatively easier to measure ROI for smaller projects and individual initiatives.

Another suggested that funding D&A on a ROI basis would create too much fluctuation in the analytical base, and that it would be better to first define how much is willing to be invested, build a stable team and then prioritise work that has the largest expected ROI.

Interestingly, someone commented that regulatory requirements such as IFRS/ISO/FDA makes investment a sunk cost.

“Different strategies are needed for those of us in different spaces or at different levels of maturity”

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THE AUTHOR

Laura Bineviciute

The global Peer-advisory community for chief data officers and their teams

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