Focus On What Matters With Decision-based Analytics

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Sometimes, you just need to make a decision and move on, making improvements once results come in later so that you have better data. I've called that being ⁘directionally correct.⁘ My client's decision-making process had become too complex without significant benefit. It's a classic example of wasting dollars to save pennies. If you're an accountant or business analyst, you have my permission to skip the next three paragraphs of this article.

In business, there's a technique for decision-making known as the cost-benefit analysis. I want to give you a classic business school definition of the concept, so let's turn to the Harvard Business School (HBS). According to HBS, "Cost-benefit analysis involves tallying up all costs of a project or decision and subtracting that amount from the total projected benefits of the project or decision. If the projected benefits outweigh the costs, you could argue that the decision is a good one to make. If, on the other hand, the costs outweigh the benefits, then a company may want to rethink the decision or project." (source: Harvard Business School https://online.hbs.edu/blog/post/cost-benefit-analysis )

The most well-known quantitative measurement of a cost-benefit analysis is ROI (return on investment), which is calculated as follows:

Over my career, I've seen entire projects devoted to nothing but attempting to get an accurate calculation of ROI. Calculating ROI is a form of data-driven decision-making, which aims to improve accuracy, efficiency, and objectivity. Being data-driven, or at least data-inspired, is generally a good thing. It's currently in fashion to say that you're ⁘data-driven⁘. I'm a numbers guy, enjoy stuff like this, and have even referred to myself as being data-driven. Recently, I attended an online webinar by two business school professors who challenged the notion of being "data-driven." I decided to find out more.

"D ecision-Driven Analytics" is a new book by Professors Bart De Langhe and Stefano Puntoni (Wharton School Press, June 2024). In their new book, the authors make the case for a decision-first approach, where analytics initiatives are driven by specific business decisions rather than generic data exploration. In short, it's the exact opposite of being "data-driven." Putting the decision before the data ensures that analytics efforts are directly tied to tangible business outcomes.