This morning, while listening to the radio, I heard the following advice. It was directed at politicians, but could just as easily have applied to data scientists:

“If they want to gain the support of the public, they need to spell out their priorities and explain what they plan to do to address them.”

The commentator’s point was simple - politicians often lose public support not because their policies are bad (although this may be the case), but because they fail to communicate:

• What they’re prioritizing (e.g. cost of living);
• How it aligns with public concerns; and
• Their concrete plans to address them (e.g. lowering taxes).

If voters can’t see the connection between the politicians’ policies and the voters’ concerns, why would they offer their support?

Data scientists make the exact same mistake.

How many times have you seen a data scientist pitch a project like this: “I noticed we have this data. I could build a predictive model with it. Would that be useful?”

You may have even done this yourself.

It’s data science for the sake of data science - a solution in search of a problem, proposed with no clear understanding of what actually matters to the stakeholders who need to approve it.

If you want your projects approved and your work to have impact, you need to flip this approach entirely:

First, understand what your stakeholders genuinely care about.

Second, communicate that you understand - make it explicit that their priorities are driving your work.

Third, propose solutions designed specifically to address those priorities.

Politicians who can’t articulate their priorities lose elections. Data scientists who can’t articulate stakeholder priorities lose credibility, budget, and impact.

Your stakeholders aren’t waiting for another clever model. They’re waiting for someone who actually understands what they’re trying to achieve.

Talk again soon,

Dr Genevieve Hayes