Hi ,
In my previous two emails, I shared how the skills that help you land a data science job aren't the same ones that help you advance your career. Last week, we looked at the "skills collection trap" - where data scientists continuously gather technical skills instead of developing business
acumen.
Today, I'm tackling the second career-limiting mistake: falling into the order-taker trap.
Mistake #2: Taking Orders Instead of Taking Charge
"If you're a waiter taking orders, that's fine... But if you want to do interesting work and really solve problems and do amazing things and advance your career, then you need to acknowledge that your stakeholders don't always know what they need. And the only way to help them figure it out is to have these conversations with them." - Bill Shander
It was the mid-2010s and data science was brand new - to my organisation and the rest of the world, too.
To raise awareness of our new data
science team, we held a drop-in session.
We commandeered a corner of the office kitchen; handed out flyers with examples of our work and a list of what we could achieve - highlighting our shiny, new technical skills, of course; and asked any passers-by what we could do to help.
At the time, we thought we were showcasing our expertise. In retrospect, we were inadvertently positioning ourselves as the data science equivalent of McDonald's.
We might as well have started asking stakeholders: "would you like a regression with that?"
If you find yourself asking stakeholders "what would you like me to build?", rather than "what business problems are you trying to solve?", then you've fallen into this trap, too.
Becoming a data science order-taker is an easy trap to fall into because most professions work this way - from plumbers to tax accountants and lawyers.
Before becoming a data scientist, I worked as a pricing actuary and my role was clear - come up with prices for new insurance contracts. It was in my job description, so I knew exactly what to do.
But whereas an insurance company knows they need prices for their insurance contracts and can
clearly articulate that requirement; most stakeholders don't actually know what they need when it comes to data science.
They might know that they have a business problem, but they lack the knowledge and experience to envision how data can create a solution - a gap that the data science role exists to fill.
In my experience, asking non-technical stakeholders for their data product needs tends to lead to two types of requests:
- Ones that are trivially small and barely worth the time - such as simple reports and Excel formatting requests; or
- Ones that are so complex as to be impossible given the data and tools at hand - such as predicting insurance claims with perfect accuracy up
to 5 years from now.
At our drop-in session, we got both types of request. And we spent the next 6 months bouncing between underwhelming projects and doomed moonshots.
The result? Six months of under-utilised skills and a wasted opportunity to prove we could
do more.
We failed to deliver on the more challenging requests while establishing a reputation as Excel support.
When author of Stakeholder Whispering Bill Shander says "your stakeholders don't always know what they need," he's describing exactly what went wrong with our drop-in session approach.
We acted like order takers and expected our stakeholders to come to us with predefined data science problems to solve; when really, we should have acted like the strategic partners we
wanted to be and diagnosed their problems for ourselves - asking the right questions to understand their challenges, then proposing data solutions they might never have considered.
It's the data scientists who position themselves as strategic problem-solvers who reap the rewards of greater income, impact and influence.
So, ask yourself, where would you rather be: the drive-through window or the decision-making table?
What to Do Instead?
The high-impact projects you dream about aren't just going to land on your desk. If you want to advance your career, you need to take control and uncover them for yourself.
Step away from your desk and start asking questions, such as: what strategic objectives are your stakeholders trying to achieve?
Understand the business context, your stakeholders' real concerns and how business value flows.
Then use that knowledge to identify the highest-value areas for improvement and propose data science solutions that can get the business there.
This approach isn't just a one-time effort, but a whole new way of doing work. And it's the mindset shift that this approach involves that sets the data science leaders apart.
Watch for next Monday's email, where I'll cover Mistake #3: Building Shiny POCs That Never Make It To Production - a critical oversight that prevents many brilliant data science
solutions from ever delivering real value.
Have you ever been stuck in the order-taker trap? Reply and share your experience.
Talk again soon,
Dr
Genevieve Hayes.
p.s. If you missed the previous emails in this series, or if you prefer to read the complete article now rather than waiting for the weekly instalments, you can access it in its entirety HERE.
