Questioning the question

They say there are no stupid questions; I think this saying
was invented by stupid people. I hate to be the one to break it to you, but
there are definitely stupid questions. I hear them all the time. However, since
I have social decorum, I usually keep my thoughts to myself. That doesn’t make
these questions any less stupid. As the leader of a department or organization,
you have the responsibility of asking the right (i.e., non-stupid) questions.
If your data scientists come up with the right answer to the wrong question – that’s
your fault. It’s very important that you carefully consider the questions and
problems that you focus your big data strategy team on.

Thoughtless Execution

There must be at least an equal amount of time on defining a
problem or question as there is on solving it. This is not intuitive for most
groups – especially ones that have an engineering slant. I did some consulting
for a large telecommunications firm that needed some help understanding the big
picture of what they were trying to accomplish. It was a monstrous challenge to
bring this group out of problem-solving. We would spend hours building detailed
flowcharts – and they weren’t even sure what problem this process was trying to
solve! Their rationale was that if they could work through all the small
details of the solution, it would help them understand the big picture. I’d
never heard anything so backwards in my life.

There was an obvious lack of leadership in this group and
it’s a very common mistake in all organizations – not just engineers. In almost
all of the troubled operations that I’ve been hired to rescue, there was
something wrong with the original question: no question was asked, the wrong
question was asked, or the right question was only vaguely suggested.
Nonetheless, there were troops on the ground scurrying to do something they
felt was important. This is a condition I call thoughtless execution.

The root cause of thoughtless execution is an
under-appreciation of the problem-defining
process, and an overwhelming obsession with the problem-solving process. This is a leadership issue – leaders
define problems, managers solve problems. Fortunately, the task of properly
defining the problem is not your sole responsibility.

You are not alone

You should have a lot of help defining the right problem,
but you must understand and respect the process. Only under extreme
circumstances should a leader make autocratic decisions for their department or
organization. So, when you bring a group of people together to define a good
question; you should brainstorm, organize, and then come to a consensus on the
right question – all before you start
answering anything. This takes strong leadership, as the tendency is to start
solving and answering shortly after the problem-defining brainstorm begins.

Of course you have your cabinet of CxOs around you; however,
don’t forget about your data scientists – they can be of great help also. If
you succeed at keeping them out of problem-solving mode and force them into
problem-defining mode, they can bring clarity around the problem and help you
ultimately articulate the right question.

For instance, consider a general manager who’s asking about
the markets who are most likely to buy widget A. The big data strategy team
could go right to work on answering this question – but it may be a stupid
question. Let’s back up for a moment. A question like this is best suited for
an organization that’s following a products-offered strategy. But is this
leader clear on what strategy drives the organization? Maybe a better question
is, “Should I follow a products-offered strategy, or a market-driven
strategy, or something else?”

If the data science team is sent down this track, they may
discover with statistical certainty that your best route is to follow a
market-driven strategy that centers on a vertical market segment that
represents your best customers. And, by the way, this market does not like
widget A, they prefer widget B. So the right question is, “What other
widgets and services should I create for my best customers?”

Bottom line

There’s more value in defining a problem that there is in
solving it. You can avoid the perils of thoughtless execution by exercising
strong leadership, and forcing the organization to clearly define the right
question before you send in your big data analytics team to come up with the
right answers. And there’s no reason to exclude your big data analytics team
from this problem-defining exercise. Data scientists can bring guidance from an
ill-conceived notion and clarity to an unfocused question. Think about the
problem your big data strategy team is trying to solve right now and ask
yourself if you’ve clearly defined the right question. After all, what value is
the right answer to the wrong question?