As a kid, I loved watching the comedy team of Abbot and
Costello. One of their most famous routines was, “Who’s On First?
where they had a long and humorous dialog about the people on a baseball team.
What’s not so humorous is coming into a data science team and not knowing who’s
doing what; and the gross amount of efficiency that’s lost because of this
confusion. At the risk of sounding recursive, the who of managing who
should be doing what
is unequivocally the accountability of the team’s
manager. For your data science team to function effectively, you must have all
roles clearly defined and governed.

That’s not my job

The toll on efficiency that unmanaged roles and
responsibilities can take is somewhat misleading. On the surface it may seem,
if roles aren’t actively managed, something may be missed. This is rarely the
case. The group will quickly figure out what’s missing and fill the gap in its
own informal way. Just below the surface, it may seem there would be
duplication of work. You’re getting close; this is true. However, it’s not the
biggest drain on team efficiency.

The biggest problem with under-managing roles on a data
science team (or any team for that matter) is how quickly the group dynamic
breaks down and takes your productivity with it. Social psychologists have
studied this for some time now. The group as a whole has a basic dependency for
the all the people in that group to understand what their role is. Until that’s
reconciled, the group will have a difficult time functioning.

The first step in avoiding group dysfunction from role
ambiguity is getting all the functions and roles straight, and then assigning
expected involvement to each function/role combination. This is typically done
in a Roles and Responsibilities Matrix – commonly known as a RACI chart. Once
done, you’re in a good position to assign people to roles. This will help
clarify who’s doing what, as long as it’s controlled properly. We’ll cover that
in a bit; let’s first talk about setting up roles and responsibilities.

Also read: A
successful big data team has to have strong supporting players

How can I help?

For every function a data science team needs to perform,
there are a number of ways a person or role can be involved. If you’ve ever
been on a project team, you probably know what a roles and
responsibilities matrix
(RACI) is. In brief, this is a matrix with team
functions (e.g., leadership, management, analytics, business experts) as rows,
and roles (e.g., business leader, analytic manager, lead data scientist) as
columns. The following role assignments – or methods of involvement – complete
the cells of the matrix:

  • Responsible: The person or people who are executing
    the function.
  • Accountable: The person who is governing the
  • Consulted: The people who provide input and advice.
  • Informed: The people who need to receive updates.

I recommend adding a Support category to the
traditional RACI, making it a RASCI matrix:

  • Support: The people helping the Responsible person.

Adding this type of assignment to your matrix implies there
should only be one person assigned as Responsible for each function the team

Once the framework is in place, we can start adding in the
pieces. The primary function of any data science team is of course analytics.
If you have one data scientist, then you’re job is easy; however, there may be
multiple data scientists on your team. Data science can be broken down into
sub-functions if you have a large enough team.

For instance, you may need both qualitative and quantitative
analysis if you’re taking a mixed-method approach. Or, you might run parallel
streams for a while then converge to synthesize best findings. Helping data
scientists understand the business need are business experts. Again, depending
on how your organization is structured, you may have different business experts
that represent different lines of business or functions.

Data scientists and business experts won’t manage themselves,
so you need leadership and management functions as well. The leadership
function is responsible for dealing with change and the management function is
responsible for dealing with complexity. The leaders in your group will set the
vision, navigate uncertainties and adjust when necessary, manage the group
dynamic and motivate the team, and help the rest of the organization adopt a
more analytic culture. The managers in your group will build plans and
schedules, clarify objectives, manage risk, ensure the team is delivering what’s
expected, and yes, make sure people understand their roles and are performing
their functions as prescribed.

A matrix is only as good as its last

When I was new to management consulting, I couldn’t
understand why so many teams where dealing with role ambiguity, when there were
clearly documented roles and responsibilities. Before long, I realized what was
happening. In most cases, the RACI (or RASCI) matrix is developed, signed-off,
then never seen again until somebody like me comes along and asks where it is.
This is another interesting but insidious phenomenon of group sociology: an
informal norm will often overrule a formal policy.

It’s very important for the management function to control
for this. This is where the physicians heal themselves with their own medicine.
When constructing the RASCI, list role disambiguation as a sub-function
of the management function and make sure you have roles and role assignments
(i.e., RASCI) to cover the function. There must be at least one role
responsible and one role accountable (they could be the same role) and it’s
best to have support and consultation assigned as well. The function should be
performed on a regular basis: weekly, bi-weekly, or monthly. The responsible
and supporting people should survey the team for who’s actually doing what, and
if everyone’s clear on their role.

Oddly enough, although the person responsible should be part
of the management team, they may need some consultation or support from the
leadership team. If simple controlling measures don’t correct the problem
(e.g., reminding people what their roles and responsibilities are), you may
need a cultural intervention. Members from the leadership team who understand
group culture would be best suited to advise on this; it’s not fair to expect
managers to have competence in this area.

Bottom line

Although the concept of documenting roles and
responsibilities has been around for a long time, role ambiguity still runs
rampant, and it can destroy the productivity of your data science team. Take
some time to clearly articulate: 1) what functions your data science team will
need; 2) what roles will cover those functions; 3) what method of involvement
each role will have in each function; and finally 4) who will be assigned to
each role. Once in place, make sure you constantly monitor the team to make
sure everybody continues to understand and perform their role (their whole role
and nothing but their role). Otherwise, instead of building great analytic
solutions, your team will be busy trying to figure out who’s on first and what’s
on second

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