Hi ,
As a data scientist, you've probably done this before...
Imagine you've just got a raise and to celebrate, you decide to treat yourself to that fancy gadget you've been eyeing
off for months.
You order it online and eagerly track the movements of the package every day - watching its journey from halfway around the world to you.
But when the package finally hits your city, something goes wrong. And instead of delivering it to your door, as
you expected, the post office informs you your parcel has been dropped at a delivery centre across town, and you have to come and collect it for yourself.
Your life is busy enough as it is, but suddenly you have one more thing to do.
What started as a treat is now
turning into a chore.
You're now regretting ordering the gadget at all.
This is exactly what many data scientists do every day in their job.
They
pour their efforts into performing complex analyses that might have a business impact. But after doing all the "hard" work, instead of going the last mile and taking their results to their stakeholders - by communicating the results in a way that resonates - they make their stakeholders come to them and interpret their results as is.
This creates one more chore for the
stakeholders and reduces the likelihood of making an impact with the results to close to zero.
Most data scientists are hired first and foremost for their technical abilities - so it's easy to fall into the trap of believing that the technical work is where your role begins and ends. Someone else can pick up your results and run with them from there.
But the reality is, senior business leaders don't always have the time and training necessary to work with technical results. This can lead to your results being ignored - or even worse, someone else who will go that last mile, taking credit for all your work.
Supply chain managers know
that the most critical part of any delivery is getting the parcel to its final destination, so focus a disproportionate amount of effort on planning those final miles.
Data science requires the same mindset.
Next time you finish an analysis, try allocating time for
the following and see what happens:
- Identify 2 or 3 insights from your analysis that address the business questions your stakeholders are currently trying to answer.
- Translate any technical results into the language of business (dollars and cents, rather than precision and recall).
- Package your insights into a clear narrative that explains to stakeholders the business implications
of your results.
Data science is as much about delivering results as it is about creating them. By mastering the final miles of delivery - and taking your results directly to your stakeholders - your analysis will transform from yet another stakeholder chore to an invaluable business asset they eagerly anticipate.
Your results won't just reach their destination - they'll drive real change when they arrive.
Talk again soon,
Dr Genevieve Hayes.
