Perhaps the hardest part of being a
leader is making decisions. That’s why the luckiest leaders are the ones
cradled in the comfort of a data science team. It’s no secret that big data
analytics can dramatically improve the quality of your decisions; however, it’s
pretty easy to mess this up with poor leadership.

Part of the issue lies in the
belief that simply having more information–and the resources to analyze
it–automatically improves your decisions. That’s not true. Data science is a
power tool not a magic wand. And although data science and big data analytics
can make a significant difference in your ability to make effective strategic
decisions, the fundamentals of leader decision making must be in place before
data science can make a valuable contribution. With this in mind, it’s
vital that you actively engage your entire data science team in
important strategic decisions–and that includes you.

The decision-making spectrum

As you’re executing your strategy, you’ll face decisions that are critical to the success of your
strategy. And since your strategy involves big data analytics in some way, and
you’ve already assembled a data science team, it only makes sense to use these
valuable resources to help you make better decisions. The leadership
challenge you face is how your data science team will participate in the
decision-making process.

On one end of the spectrum, you make all of the decisions without input from the team. On the other end of the spectrum, you
define the problem and set parameters, and then turn it over to the team to make
the decision for you.

In a general sense, for these types of leadership
concerns, I subscribe to a contingency theory: It depends on the
situation. For leaders who are working with a data science team on a
corporate strategy that involves big data, in almost all cases I can prescribe
the appropriate participative style: facilitative.

case for facilitation

A facilitative approach is one notch
away from turning everything over to the team to make the decision. Victor Vroom, a professor at the Yale School of Management and well-respected author
and thought leader in management and leadership, has a brilliant model that
explains this continuum from autocratic decision making (we’ll call it far
left) to autonomous group decision-making (we’ll call it far right).

problem with going all the way to the right with a data science team is that it
alienates you from the team, which won’t work well. Furthermore, data science
teams don’t self-organize well, which is why your leadership is needed for the
group dynamic to work properly.

Although this approach is not on the
extreme right, it’s still positioned on the right-end of the spectrum. This
style favors group freedom over leader influence–just remember that
you’re part of this group, so you need to stay involved.

Guidelines for a successful decision

Before we get into the mechanics of
facilitating a decision with a data science group, I’ll set up guidelines for this participative style to be effective:

  • Only
    involve the group in decisions that are critical to the success of your
    strategy. For less important
    decisions, either automate the decision-making process or quickly make the
    decision yourself and move on.
  • Don’t
    view critical decision making as an opportunity for employee development. When your strategy is on the line,
    this is not the time to grow leaders.
  • Make
    sure your data science team is already committed to you, your strategy, and the
    team. Ulterior motives can
    derail your facilitation to a contaminated decision. That is why you need good
    change management to fortify followership, commitment to the corporate
    strategy, and dedication to the team.
  • Make
    sure they fully understand the strategy. Your data science team should be part of your strategic
    council. They need to understand the corporate strategy as well as you do if
    they’re going to add value to your strategic decision-making process.

leader’s role in making the decision

For the purposes of this facilitation,
consider yourself an equal member of the team. As the leader, you should be
involved in the decision-making process, but you should not facilitate it–that’s not your responsibility or your strong suit. Although you’re
responsible for the overall decision-making framework, the execution should be
handled by your analytic manager or someone from that part of the team.

a good foundation is in place, you’re in good shape to handle that critical
issue when you need to. For example, let’s say your strategy relies on the
engagement of a critical market segment, though they’re not responding to
your outreach as anticipated. After you clarify the problem and outline the guidelines and constraints for successful resolution, you should have the analytic manager
perform this basic three-phase facilitation: collect, analyze, and decide.

data science for effective decisions

The first step is to collect the data
that’s relevant to the issue. Your data science team is obviously very good at
this, but they need the right direction.

One thing data science is good at is
separating relevant from irrelevant data. In data science terms, this is called
feature selection; this is especially important when dealing with big
data, as analysis paralysis sets in quickly when the data are overwhelming.
Once you’re comfortable with the feature set, it’s time to analyze–obviously, another strength with your data science team. If the problem is clearly
defined, the team shouldn’t have any problem with the analysis techniques.

our example, we’re doing a root-cause analysis on why our target market isn’t
engaging. When you have enough information (analysis transforms data to
information), it’s time to make a decision. Your manager should tease out a
short list of next steps (run an experiment, change the target market, adjust
assumptions, etc.) and draw the team to a consensus. If the team is truly
deadlocked (which is rare), just step up and make the decision so everyone can
move on.


Making decisions on
critical strategic issues is an inescapable part of being a leader. Having a
data science team to help you make decisions gives you an enormous
advantage; however, you must make sure to include them in the right way. Make
sure they’re an integral part of your strategic council and solve critical
issues together as a team.

It’s amazing how a little data science can go a long
way in making insightful decisions. Don’t spoil this opportunity with ineffective leadership.