Data-driven leadership

How can you tell if a team performs or not performs?

You can certainly judge if a team’s results are “good enough”, but results don’t tend to come very frequently. A few companies release daily or couple of times per week, others less frequently. So, by the time you’re seeing a detoriation in results, it’s an indication that the team has been traveling down the slope for a while.

I’ve been co-founder of CX-Ray, trying to answer this question via a team’s social network. We assumed that a social network with good work connections makes a team productive, which may be the foundation of collaboration and productivity.

The piece I was missing in this approach is that it’s not considering what the team actually does, but rather relies on what individuals say about each other in a survey. It’s a good approach for leaders to find bonds and pressure within teams, but doesn’t look beyond the human connections.

A study that I’m working on right now, takes a different approach. Let’s look at all the data a team produces and find signs of how they correlate with each other. In my experiment, I divide data into 3 categories: Chatting, Collaboration and Creation. For these 3 categories, teams use various tools:

  • For Chat: email, Slack, HipChat, Yammer and a sea of other tools,
  • Collaboration: Asana, Jira, Trello, BaseCamp, Google Drive and so on,
  • Creation: for engineering teams, these are code repositories and CI tools like GitHub, BitBucket. For non-engineering roles, this can be any other tool where they store the work produced.

Understanding the extent of how each of these channels used and how traffic between these 3 categories relate to each other, may be part of the answer I’m looking for.

For example, look at these 4 teams — can you get a sense of their culture? Their productivity and their potential results?

Look what happens with the following team over time… All data points are relative to one another, but looking at a month or two worth of stats, you can have an idea of direction: they may have finished a phase of engineering work and now moving into ideation? Or stuck at an issue and debating a solution?

For you as a data-driven leader, these signs are key indicators of how your team performs. Relying on rumors, subjective judgement or short-term results gives a false picture of what’s happening in the overall trend.

If you’re interested to sign up your team for the experiment, ping me at dszabosf at gmail.com. I’m looking for likeminded explorers to join my mission.

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