Help! My boss doesn’t speak “data”

If there were a Top 5 playlist for things data scientists gripe about, this would be Track #1:

How can I get my boss to understand the value of data science?

We all know data science has done, and continues to do, amazing things. The movie Moneyball was about how data science can improve baseball recruiting. Las Vegas knows how data science can improve casino revenue. Meteorologists know how it can help predict the weather. Governments know how it can predict security threats. Doctors know how it can help diagnose and treat patients. Wall Street knows how it can help make money. Our clients know how it can help predict business survival or failure. Need I go on?

Despite all this, it’s frustrating for anyone who appreciates data science to end up with a boss who doesn’t.

Confusion-Information drawing

When this happens, pro-data science employees often find themselves saying things like “my manager is a politically-minded, incompetent idiot who refuses to recognize facts when staring them straight in the face.”

On the flip side, when faced with data science-frenzied employees bosses can find themselves saying “my employee is too easily distracted by shiny new objects that promise the world but mostly end up as expensive, impractical toys that fail to consider the full extent of their implications and quickly peter out.” For more old-school bosses, it can be more like “I don’t get all this new fan-dangled data science stuff, I don’t like it, and it never mattered before so why in Sam Hill should I give a damn now?”

For employees, it’s often about seeking reward – the reward of improving performance in whatever function they have dominion over.

For bosses, it’s often about avoiding risk – the risk of wasted resources, distraction and perhaps even embarrassment.

While I won’t pretend to have this whole issue worked out, in trying to overcome resistance to data science – assuming the science is sound and holds genuine practical value – a good step is to try and see things from your boss’s perspective. Too often I find myself succumbing to frustration when I ought to be listening harder. I can default to technical righteousness when the real unspoken objection has nothing to do with it.

Bosses like data. They always have. In fact, they demand it. Bosses want to know their team’s decisions have good justification. “What makes you think we should raise prices? Show me the numbers?” Yet the traditional relationship bosses have with data is inherently different from the new relationship they’re being asked to have in the era of modern data science. 

Element of confusionIn the past, data was “supporting-data.” It was prior information, gathered up to inform (but not determine) a decision. While supporting-data has always had some weight, in a supporting-data world the final decision continues to rest with the human decision maker – the boss – who sort of swirls data around like wine in a glass, takes a sip, then says what ought to be done based on personal judgment.

Now things are different because data has moved up in the world. Not only do we have gobs more data, but software and technology lets us understand it in ways that were only recently the stuff of science fiction. Modern data science isn’t about “supporting-data,” which bosses are comfortable with. It’s about “deterministic-data.” In a deterministic-data world, the science itself demands to make the final decision. Not the boss. No more swirl. No more sip.

This is a true source of tension for data science advocates and wary bosses. So for employees who want data science to take hold in their organizations, take a deep breath and map out how you’ll lower the risks of wasted resources and team distraction if a new data tool is implemented. Figure out how to minimize potential embarrassment for your boss if things go wrong. You depend on your boss for political air-cover, so nobody wins if your boss’s political capital takes a hit.

For bosses who are considering a data science tool but aren’t quite comfortable with the idea yet, don’t be afraid to embrace the details. You don’t have to be a computer whiz or statistician, think about it like any other proposal: what’s the problem, what’s the proposed solution, what are the risks, rewards and alternatives?

Make your team explain “so even a 6th grader can understand” what they’re trying to do, why they want to do it, and how it works. One of the worst things that can happen is to miss real performance gains just because you’re being too proud or mentally lazy. There’s no shame in admitting what you don’t understand, and don’t worry about hoarding control or glory over your decision-making powers. If the data science really works you’ll be amply rewarded in time as your team increases its success rate. Data science didn’t eliminate baseball recruiters, casinos, meteorologists, doctors, investors or innovators, it just made them better.

Also keep in mind your data science-advocating employees may not be doing it because they want a fun distraction. Their reputations are on the line as well.  Odds are, they’re genuinely passionate about improving performance in whatever function they have dominion over. They’re doing their best to fight the good fight and hoping – desperately – that you’ll have the guts to lead them into the future instead of anchoring them to the past.

 

 

This Post Has 4 Comments

  1. Jessica

    Mine doesn’t either 🙂

  2. Tonya

    “For employees, it’s often about seeking reward – the reward of improving performance in whatever function they have dominion over.

    For bosses, it’s often about avoiding risk – the risk of wasted resources, distraction and perhaps even embarrassment.”

    Too true.

  3. Don

    Data isn’t the answer. It’s what the data tells us. The boss doesn’t need to be bogged down in data – he/she wants to know the implications of the data/analysis. When I was a VP of a Fortune 100 company, I tossed people out of my office who came loaded with reams of data and expected me to figure it out. If data (big or small) doesn’t inform decision-making, it’s a waste of someone’s time.

  4. Jamie

    That’s like saying tools don’t matter, it’s the house they build that matters. Well, yes, but; no tools, no house. It all matters.

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