Keep Kicking Goals (and Other Terrible Data Science Advice)


A data scientist I know once told me of a manager who would end every team meeting with the same advice: “keep kicking goals.”

What does that even mean?

Which goals? With what resources? How do you even measure what a goal is?

Unless you’re actually talking to a football team, “keep kicking goals” is meaningless.

Yet this is what some data scientists face. Well-meaning managers who say “go do data science” without giving direction on what that actually requires.

And then they wonder why projects fail or why their data science teams seem to spin their wheels.

Here’s the thing…

This isn’t about one manager’s communication style.

It’s really about a much deeper issue - many stakeholders simply don’t know how to direct data science efforts effectively.

This is something you can’t control - what you can control is your response.

Don’t wait for your boss to create opportunities for you. Create them for yourself.

When you see your marketing team manually segmenting customers every month, that’s an opportunity.
When finance complains about forecasting accuracy, that’s an opportunity.
When operations asks the same “what if” questions repeatedly, that’s an opportunity.

Instead of waiting for the next assignment, come to your stakeholders with solutions.

Frame it in business terms. Show the impact. Make it impossible to ignore.

“Keep kicking goals” isn’t strategy - it’s noise.

But master this approach and you’ll become the player everyone wants on their team.

Talk again soon,

Dr Genevieve Hayes

Data Science Impact Algorithm

Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.

Read more from Data Science Impact Algorithm

You’re already using optimisation every day as a data scientist - but you probably don’t think about it. Every time you train a machine learning model, you’re running an optimisation algorithm to find the best parameters. But there’s a second type of optimisation that most data scientists never even touch - and it’s where the real business value often lives. It’s called decision optimisation, and it can transform your ML predictions into actionable decisions. Here’s the difference: ML...

Data science exists to support decision making, but within any organisation, there is a hierarchy of decisions - low stakes, high volume decisions at the bottom; high stakes, low volume decisions at the top. Executives care most about the decisions at the top. There’s valuable work at the bottom of that hierarchy. But automating routine decisions is about clearing the path so executives can focus on the high-stakes decisions. It’s not about walking it with them. For years, data science has...

Building a data science model for an academic paper is one thing. Building a model that has to work perfectly during the Cricket World Cup with millions watching is something else entirely. There’s no room for the kind of errors that might be acceptable in research settings or even standard business applications. And if you get it wrong, you get emails… Lots of emails… From people all around the world… The type of emails that begin with: “how dare you you make my team lose?!” Worse still? You...