leaders are making the mistake of hiring their problems away. The problem
isn’t with the approach — it’s with the attitude.

Don’t get me wrong, as a
consultant and expert, I’m usually the benefactor of this way of thinking;
however, on more than one occasion, I’ve had to educate my client on their
responsibilities in the consulting process. It’s fallacious to reason that an
expert can solve your problems without your involvement. Unfortunately, with a
subject as mysterious and obscure as data science, it’s too easy to make this

Many people believe that once they’ve hired the best and brightest
data scientists, their investment in data science competence are over, but that’s
not true. You should attract and retain the best
data scientists for your data science team, though everybody on your
data science team (including you) needs some education in data science.

up your game theory

the best data science leaders are also data science experts, but it’s not
a hard-fast requirement. What leaders bring to the table is their ability to
inspire and motivate the team and make important decisions about where this
data science team will be taking the company. If, in addition to these
qualities, the leader has a strong background in data science, this is
terrific. There’s nothing more inspiring for a data scientist than to work
with another brilliant data scientist. And although the leadership/data science
combination seems rare to find in one person, I’m seeing data science leaders
emerge more today as big data and data science becomes more of a mainstream
conversation in corporate America.

a minimum, the leader should know data science terms (e.g., algorithms,
programming languages, and methods), outcomes, and how those outcomes will
benefit the organization. For example, a leader should know what a cluster
analysis is, why it’s important for customer segmentation, and how customer
segmentation drives customer loyalty. A leader should know what a neural
network is and how a neural network can be used to predict the behavior of the
customer segment you discovered with your cluster analysis. These are basics
and if you don’t have the time or inclination to understand these concepts, you
really shouldn’t be employing these techniques in your corporate strategy.

other people on your data science team that need a good base of data science
education are the ones who round out your leadership team: change leaders and
coaches. Some experts today are espousing a T-shaped skill set, wherein
resources have deep expertise in one skill (leadership, change, team
development) represented by the vertical line and a general, conversational
knowledge of other areas (data science) so they can work more effectively with the
team. I think you should take it one level up from there if you’re on a data
science team. You don’t need to be an expert in data science, but you need more
than just a conversational knowledge.

Managing by example

is a different story. I’m a lot more tolerant of leaders who aren’t strong in
data science than managers, though many people disagree with me. If you’re managing a data science team, you need to know your stuff.
Managers are chiefly responsible for keeping things under control, and there’s
no way to do that on a data science team without knowing data science.

In Six
Sigma, the project’s Black Belt is not only the project manager, but also the
most advanced statistician (aside from the Master Black Belts, but technically
they’re not on the project team). The worker bees on a Six Sigma project are
Green Belts, and as you might guess, they’ve had a good deal of statistical
training, but not as much as the Black Belt. It’s best to run your data science
team the same way. By the way, I’ve seen some companies do Yellow or White Belt
training for leaders, which is 
nonsense; your leaders should at least be the
data science equivalent of a Six Sigma Green Belt.

rest of your management team — quality control and governance — should have the
same stringent requirements for data science education. How can you ensure a
high quality of anything if you don’t understand it? How can you make sure the
team is staying within the lines if you don’t understand where the lines are or
what they mean? Your entire management team needs to have a high level
of expertise in data science, and of course the other areas they’re responsible
for (management, quality, governance). Earlier we talked about a T-shaped skill
set; I feel managers need more of an H-shaped skill set: deep expertise in both
data science and management, and a solid base of knowledge in everything else.

key danger with this philosophy is micromanagement, so install some sort of
control for this early on. It’s important to let the data scientists do their
job, even if others on the team have data science expertise. Unfortunately,
when you have management experts that are also data science experts, they
inexorably feel the need to sanction the content (e.g., analyses) based on
their own values, beliefs, experience, and favorite methods. This might work on
a Six Sigma project, but it won’t work on a data science team. This behavior
will very quickly shut down the team’s creativity, so you must guard against
this from team inception.


effective data science team is comprised of people who understand data science,
including leaders and managers. Although it’s tempting to just hire a bunch of
bright data scientists and call it a day, it doesn’t work that way. Ensure your
leaders (including yourself) have a very good base of data science education,
and your managers are experts. Survey your team today to see where each
person’s skill level is with data science. It may be time for some to hit the