Use case.
It’s one of those phrases which has swept across the business lexicon, hot on the heels of ‘low-hanging fruit’, ‘agile’, and ‘freemium’!
I now hear it almost every day, normally within the question:
What is the best use case?
Use cases were introduced as a method for defining requirements for IT solutions in the late 1980s by Ivar Jacobson and the concept/tool has spread like wildfire since.
In my world of strategic data consulting, the phrase means pretty much the same, except we use it to describe the advance analytics solutions that could be applied to a variety of business problems.
In truth, ‘what is the best use case?’ is a question that Adam and I deflect with regularity. Largely because we believe there is a real danger in jumping to a potential solution without first deeply understanding the business problem at hand. So, just as we don’t talk about ‘data’ in our preliminary client engagements, we also don’t talk use cases.
However, we have found that describing common use cases is an extremely powerful way to enable executives to visualise how data science could impact their business, as opposed to the post-apocalyptic images that AI and machine learning so frequently elicit in peoples’ minds!
A fabulous example of a highly tangible use case is the Churn Prediction Model – which aims to estimate, at the level of individual customers, the propensity (or susceptibility) they have to leave. But how does it work? And why is it so useful?
This article is a wonderful resource for any business leader/owner to better understand what a churn model is useful for. What do they need for the model? And what does the model ‘look’ like?
And yes, it’s a fabulous use case for many businesses - across sectors and maturity. In fact, it’s one of the most frequent solutions we design with our clients - but only once we’ve grappled their actual business problem to the ground!
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