Root canals and machine learning models have more in common than you think.

A few weeks back, I went to the dentist because of a mysterious pain in my jaw.

I wasn’t sure what the cause was, but after mentioning it to family and friends, the term “abscess” came up a few times. By the time of my appointment, I was convinced I was going to need a root canal.

It took my dentist all of 5 minutes to conclude that the pain was purely muscular and everything would be fine in a week or so.

That 5 minute diagnostic check was far more valuable to me than the root canal I expected to need - and at a fraction of the cost.

Here’s the thing…

Many businesses hire data scientists believing they need fancy ML models to solve their business problems.

Yet, fancy ML models are a lot like root canals.

Invaluable, if you need one, but if not, you can be setting yourself up for a whole world of pain.

How can you tell the difference?

By diagnosing your stakeholders’ business problems first.

Before jumping into fitting algorithms, ask questions like:

• What specific business decision will this analysis inform?
• What would success look like, and how would we measure it?
• Why has this become important now?

Sometimes the answer is a sophisticated ML model.

However, it might be that a cheaper solution, like a set of business rules or even just better communication would suffice.

Just like my dentist saved me from an unnecessary (and painful) procedure, the right diagnosis can save your stakeholders from expensive solutions they don’t really need.

That’s why the real value lies in the diagnosis, not the cure.

Talk again soon,

Dr Genevieve Hayes