Challenging the Data Foundation ROI Paradigm

Creactives SpA recently published a great article Challenging the ROI Paradigm: Is Calculating ROI on Data Foundation a Valid Measure, which was made even greater by the fact that they are technically a Data Foundation company!

In a nutshell, Creactives is claiming that trying to calculate direct ROI on investments in data quality itself as a standalone business case is absurd. And they are totally right. As they say, the ROI should be calculated based on the total investment in data foundation and the analytics it powers.

The explanation they give cuts straight to the point.

It is as if we demand an ROI from the construction of an industrial shed that ensures the protection of business production but is obviously not directly income-generating. ROI should be calculated based on the total investment, that is, the production machines and the shed.

In other words, there’s no ROI on Clean Data or on Analytics on their own.

And they are entirely correct — and this is true whether you are providing a data foundation for spend analysis, supplier discovery and management, or compliance. If you are not actually doing something with that data that benefits from better data and better foundations, then the ROI of the data foundation is ZERO.

Creactives is helping to bringing to light three fallacies that the doctor sees all the time in this space. (This is very brave of them considering that they are the first data foundation company to admit that their value is zero unless embedded in a process that will require other solutions.)

Fallacy #1. A data cleansing/enrichment solution on its own delivers ROI.

Fallacy #2. You need totally cleansed data before you can deploy a solution.

Fallacy #3. Conversely, you can get ROI from an analytics solution on whatever data you have.

And all of these are, as stated, false!

ROI is generated from analytics on cleansed and enriched data. And that holds true regardless of the type of analytics being performed (spend, process, compliance, risk, discovery, etc.).

And that’s okay, because is a situation where the ROI from both is often exponential, and considerably more than the sum of its parts. Especially since analytics on bad data sometimes delivers a negative return! What the analytics companies don’t tell you is that the quality of the result is fully dependent on the quality, and completeness, of the input. Garbage in, garbage out. (Unless, of course, you are using AI, in which case, especially if Gen-AI is any part of that equation, it’s garbage in, hazardous waste out.)

So compute the return on both. (And it’s easy to partition the ROI by investment. If the data foundation is 60% of the investment, it is responsible for 60% of the return, and the ROI is simply 0.6 Return/Investment.)

Then, find additional analytics-based applications that you can run on the clean data, increase the ROI exponentially (while decreasing the cost of the data foundation in the overall equation), and watch the value of the total solution package soar!