Sunday MBA provides ideas on running better businesses and succeeding in the modern workplace, this week from MIT Sloan Management Review and Roger M. Stein, a senior lecturer in finance at the MIT Sloan School of Management.
Every team needs talented people. In data science, talented people need not only to be good at what they do individually, but also able to challenge their colleagues to create effective new solutions to very hard problems.
How do you build a data science team to attract and retain this type of world-class talent?
There are three main “jobs” that leaders need to take on to manage a first-rate data science group: 1) Build an engaging environment; 2) Make sure the team has access to the resources it needs; and 3) Get their hands dirty — but stay out of the way.
Build an engaging environment
Much of what motivates high-performing data scientists comes down to the outlook of their colleagues and the dynamics of their daily interactions. High-performing team members need to be comfortable giving colleagues their best ideas and collaborating to shape the most promising of these thoughts into real-world solutions.
In the culture of world-class analytics teams, members (and their managers) are excited by what their teammates can do. In such groups, team members err on the side of giving their colleagues too much credit for their work and ideas, rather than worrying whether their own contributions will be recognized. They are able to revel in discord and respect differences.
For analytics teams to be effective, they must have continuity. The longer team members work with one another, the more they get to know the ways in which different colleagues approach problems and communicate results, and the more they learn to trust colleagues in key areas. While it is possible to hire new analysts with excellent skills, there is no quick way to infuse them with this kind of team “meta knowledge.” It is far cheaper to invest in a stimulating and high-performing environment than it is to replace a seasoned analytics professional.
Make sure the team has access to the resources it needs
All business units need resources, and generally there are not enough to go around. For analytics teams, resources include the obvious hardware, software, and support staff. But analytics teams also need access to senior executives, academics, high-performance computing platforms, and specialized data sets for modeling projects.
It can be hard for nontechnical managers to evaluate the importance of, say, access to an expensive database or a subscription to an online research library. The research team manager must be prepared to advocate for the team’s needs.
Get your hands dirty — but stay out of the way
A lesson often learned the hard way is that the best analytics managers need to be able to give their staff the freedom and trust to take control of the research agenda. It is perfectly reasonable for managers to review proposals before green-lighting them, but managers should have enough confidence in their analytics teams to let them take the lead.
This doesn’t mean that managers should only get involved in oversight — quite the opposite. Analytics and data science teams are most successful when managers are “hands on” with the research process and have direct experience with the nitty-gritty challenges of the analytics workflow: data sets that are noisy, infrastructure that is balky, and business units that struggle to articulate their problems in ways that make an analytic response worth having.
In this respect, analytics is different from many other fields. In banking, for example, division managers generally don’t review loan applications. But in analytics, the most successful leaders engage in research and continue to publish regularly even as they move up the executive ladder.
Data scientists seek and recognize strong, visionary leaders. Steve Jobs once observed that A players really like working with other A players — and they don’t want to work with B or C players. Jobs could easily have been speaking of talented data science teams and their managers.
This is good news for data scientists. It means that advancing in the organization does not mean giving up what they value so highly — the intellectual and psychic satisfaction that comes from solving really hard problems.