I had a stakeholder who was excited about data and ready to champion initiatives.

And I completely blew it.

Back in the day, when “big data” was the latest buzzword (much like “AI” is today), I worked with a stakeholder who was captivated by the idea of harnessing our organisation’s big data.

Every time we spoke, she would ask how we could utilize our big data, and then tell me about the value she could see our big data creating.

The level of engagement was every data scientist’s dream.

The only problem? Our organisation didn’t have “big data”.

Yes, we had a lot of data - enough to do some real good.

But by the standard set by Google and the big banks, our data was just a drop in the ocean.

Rather than harnessing her enthusiasm to create real value, I chose to fight a semantic battle. I put my energy into educating her on what “big data” actually meant - with limited success.

No matter how many times I explained to her what “big data” meant, it never seemed to stick. Because what I now realise is that she was simply using “big data” as shorthand for “our data”.

The label was wrong, but her instincts were right.

I was reminded of this recently, while watching this comedy sketch about a maths teacher who goes into a meltdown over the “Sisyphean futility” of teaching fractions to an endless stream of children.

The punchline is that, for the teacher, this endless cycle is the job.

Watching his meltdown, it finally dawned on me that I was never trapped in the same way.

I wasn’t there as a teacher. I was there as a value creator.

And here’s the kicker: the terminology didn’t matter.

Whether she called it “big data”, “regular data” or “Steve” made absolutely zero difference to any decision she needed to make.

It didn’t change what problems we could solve or what value we could create.

What I had was a senior leader who was excited about data and ready to champion initiatives.

While I was burning energy on semantic corrections that changed nothing, I was missing the chance to partner with her and actually deliver the results she was envisioning.

The lesson here is: understand your job and focus on what matters.

Now to be clear, I’m not saying ignore all stakeholder misunderstandings. If someone thinks your predictive model is 100% accurate or believes you can generate insights from data you don’t have, correcting those errors is critical.

But your job isn’t to ensure your stakeholders use technical terms correctly simply for the sake of correctness.

It’s not to educate them on the finer points of data science methodology until you’ve “taught all the stakeholders in the world or die, whichever comes first.”

Your job is to create business value.

And sometimes that means letting go of being right about things that don’t actually matter so you can focus on things that do.

Talk again soon,

Dr Genevieve Hayes