Team management through data

Team management through data

10:11 does not content itself with exploiting data and innovative interfaces for its clients: internally, the life of the agency is also punctuated in this way.

First, it is essential to better understand what we expect by “team”. To do this, you must succeed in distinguishing between a “working group” and a real team. Not all working groups are teams, and in fact the majority of them are not teams. It is therefore very important for a manager to know how to create a team first, before being able to manage it.

As such, the first piece of advice we could give would be not to fall into the trap of managing a team person by person: you must not manage one Team but manage through the team. Turning a workgroup into a team can bring a lot of benefits. Teams produce better, faster, and more innovatively. This is because we tend to be more effective when we feel that we are part of a concrete whole.

Team management through data

A quick definition of the team could be as follows: It is a group of people who work collectively and who are mutually committed to a common purpose and stimulating goals related to that purpose.

Why “a common reason for being”? Each successful team, each revolutionary start-up is convinced that it exists for a clear and precise reason and that the world will change thanks to it. If we take Airbnb as an example, that would be the difference between “we connect landlords and one-time renters” and “we're changing the way people go on vacation.” It's what brings team members together and makes them feel like they belong to something bigger than themselves.

Why “stimulating goals linked to this reason for being”? The purpose must become concrete quickly or it will disappear. To achieve this purpose, any team must move forward with specific, achievable and motivating goals.

In short, purpose and goals are the two pillars that support a team. A reason for being without a goal is just a flimsy dream. For no reason, goals are just a useless activity. But even though these two pillars are fundamental, they are not sufficient by themselves. A team also needs clarity, especially on the following points:

  • The roles and responsibilities of each team member : no one can do everything.
  • Methodology and/or work process : how the team should carry out its work, especially on decision-making.
  • The values, norms, and standards that define what a member is entitled to expect from other members : how to resolve conflicts between two team members, for example.
  • Feedback on work (feedback) and numerical data that make it possible to measure the progress of the team.

This may seem obvious, but clarity on these small details makes it possible to maintain an optimal pace and working environment for all members of the team. Moreover, when all these conditions are in place and the working group has become a real team, members achieve a high level of productivity not because their manager asked them to do so, but because their colleagues expect this high level of productivity from them: the team manages itself. If someone on the team is not working the right way, the other members will let them know. In this way, performance is guided not by the expectations of the manager, but by the emotional and social ties that link the members together. When that happens, we say it's a management through the team.

Focus on a key point in team building: feedback and numerical data (team management through data).

We can find two levels of intervention in team management through data.

  • Take measures at the individual level (the employee)

As mentioned above, good team management does not focus on individuals. This level of intervention therefore seeks to measure how the person will work with colleagues.

We try to divide these indicators into 2 parts: individual behavior indicators and individual performance indicators.

In the individual behavioral indicators, for example, we will try to analyze and measure how the person will address others or how other employees may feel about this person. These are qualitative data that can still be measured quantitatively using internal survey tools such as:

At 10h11, we use a Slack bot called Polly (, which allows us to obtain quick and quantified feedback on the feelings and well-being of the team:

We also use the HeytaCo bot, which is more fun, which allows you to send and give points to a person if one of their actions seems relevant to us (a bit of gamification never hurts):

In individual performance indicators, we will try to measure all the employee's productions that contributed to the team's final objective. For example, we can cite the number of mistakes made by the person, their time log, the number of suggestions or proposals that they will bring to the project. To do this, we use Nikabot (, which makes it much easier for the team to log their time by asking a simple question via Slack at the end of the day. The results are recorded in a dashboard accessible by all.

  • Take measures at the collective level (the team)

Analyzing the functioning of a team therefore first involves its members, but it is also possible and recommended to measure the overall performance indicators of the team. Again, we will divide these indicators into two categories: team functioning indicators and collective result indicators.

Concerning The team's operating indicators, we will try to measure the group dynamics of the team and find out, for example, if the team is effective during work meetings, if it quickly reaches a consensus in case of disagreement or even the techniques used to solve problems.

As for collective performance indicators, we will try to measure the final production of the team. The indicators will therefore rather be the number of tasks completed.

Why implement data management?

  • Measuring interaction

Teams thrive when their members have healthy relationships, when they take time to understand each other's skills in order to share and build on the ideas of others. A simple way to measure interaction is to note the number of positive versus negative comments during meetings. Since negative emotions are much stronger than positive emotions, high-performing teams have a ratio between 3 to 1 to 7 to 1 (positive vs. negative). Anything above 8 to 1 is a sign of false harmony in the group and a tendency to avoid topics that are annoying. Anything below 1 to 1 shows signs of very significant dysfunction.

  • Resolve conflicts

For many, conflicts are frightening and should be avoided at all costs. This is only true in the case of negative or offensive conflicts, where team members are attacked personally, where some members must win at all costs. However, avoiding conflicts is a sign that the group is not addressing issues that need to be addressed. Successful teams discuss these problems and exchange experiences and ideas openly to find the best possible solution. It is possible to note the number of team members who are actively involved in idea generation and the resolution process. High-performing teams tend to be equitable among all team members, while unbalanced teams have only one or two people who dominate the discussion.

  • Making decisions

Important decisions should only be made after all team members have participated, agreed upon, and validated these decisions. Non-performing teams agree on the decision given by the person at the top of the fray and show no sign of commitment during the stages following that decision.

  • Creating added value

The members of a successful team all respect the same performance standards without the help of their manager. Are the members on time? Did they prepare for the meeting? Are distractions, such as discussions between two people, email updates, or text messages kept to a minimum?

When team members do not support these performance standards and rely on the rules given by the manager, they signal that they are not involved enough for each other.

Know how to ask yourself the right questions before you start:

How do I identify the data I have at my disposal?

To do this, it is enough to do a simple data collection, taking into account the usual factors:

  • What data should be collected to correspond to the quality measures I want to put in place. The data you are going to collect should be relevant to the points you want to improve in managing your team. If these points change during the year, the data you are going to collect must also change.
  • What are the sources of this data? It is important to determine what are the sources from which you will collect this data: do they come from software, a web service, surveys, manual entries? Who is in charge of this data: the director, the team leaders, the employees?
  • What methodology should I put in place to collect this data? Will I set up APIs that will allow me to collect data directly? Will I have to collect this data daily, monthly, annually? Am I going to integrate them into a dashboard, on an excel, in a database?
  • How much data should be collected?It is not always necessary to collect all the available data. The important thing is to focus on areas for improvement and relevant areas of development within the team in which you work.

How do I know if the data I am going to collect is of good quality?

To do this, you can rely on “data governance”.

An old business saying is “you can't manage what you can't identify.”

The official definition of data governance, given by the Data Governance Institute, is as follows:

“Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

Data governance is therefore a process that helps to manage data. In a way, a list of procedures that helps you know what to do when you encounter a problem with your data.

How to know the maturity of your company in terms of data governance?

4 good tips following our experience at 10h11

• Don't become a “dataholic”: too much data kills data

• Have reliable data and tools

• be transparent with the team about the data collected

• Communicate face-to-face as much as possible

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