Why do you need me?I know why I didn’t ask these questions.
I was afraid that it would kill the enthusiasm.
I was afraid that it would lead me into a career of doing simpler, less sexy projects.
I was afraid that my technical skills would go to waste.
Growing pains hurt, it’s right there in the name.
And it’s taken me some time to come to terms with the fact that I’d need to put away technical solutions in order to solve actual problems.
For the past few weeks I’ve talked to clients in a different way.
I’m having the conversations I should’ve been having all along.
It’s like I’ve looked up from the screen and made eye contact with them for the first time.
By asking the deeper questions, I’ve taken them further back in the conversation to the point before they made up their mind to do one particular project, one particular way.
This means that I get to understand what they really want.
Nobody wants a complex, expensive, difficult-to-maintain machine learning system for the sake of it.
They want to reach more customers (or lose less), make staff happier by reducing the amount of menial activities they have to do, or automate what they can to improve efficiency and the bottom line.
Sometimes these things need machine learning and sometimes they don’t.
Even if machine learning is the best way to attack a particular problem, the chances are slim that a client has picked the right project to start on, picked the right metrics to measure, or allocated the right amount of resources.
Machine learning solutions don’t start in a text editor or a Jupyter notebook— they start in a conference room with senior executives pouring over the annual report.
You forget this at your client’s or employer’s peril.
By being steadfast in this new approach of asking why, I’ve been able to have entirely new conversations — it has completely changed the way my clients view me.
Now I’m someone who can be trusted to tell the truth.
Now they know I’m not only interested in doing the next new thing.
Data scientists are going to be key employees in organisations for years to come and that’s not up for debate.
But there’s a vacancy that hasn’t been filled yet and that’s the data strategist.
Companies need someone who’s been through the ringer, seen analytics succeed, seen it fail.
Someone who knows when to kill a project.
Someone who knows what the cheaper alternatives are when everyone else is gushing about the potential of this or that algorithm.
They need someone who’s aligned with the business at the expense of their own technical expertise, at the expensive of not playing with the shiniest, newest things.
We data scientists owe it to our employers and clients to solve their actual problems, and in order to do that we’re going to have to grow in to data strategists.
When all you have is a hammer, everything looks like a nail.
So data scientists, please think strategically, think honestly, and don’t let your eyes be drawn to the shiny things.