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Team management through data

10h11 is not only about exploiting innovative data and interfaces for its customers: internally, the life of the agency is also regulated in this way. Over the course of a new series of articles, we will detail our methods and work tools that allow us to combine agility and efficiency! For this second article, we detail one of the keys to our efficiency: team management by data.



First of all, it is essential to better understand what we expect from a “team”. To do this, it is necessary to be able to distinguish between a “working group” and a real team. Not all working groups are teams, and most of them are not. 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 advice that could be given would be not to fall into the trap of managing a team person by person: you should not manage a team but manage by the team. Transforming a work group into a team can bring many 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.

A quick definition of the team could be: it is a group of people who work together and are mutually committed to a common purpose and challenging objectives related to that purpose.

Why “a common reason for existence”? Every successful team, every 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 the example of Airbnb, it would be the difference between “we connect owners and occasional tenants” and “we change the way people go on holiday”. It is what brings team members together and makes them feel part of something bigger than themselves.

Why “stimulating objectives related to this purpose”? The raison d’être must become concrete quickly or it will disappear. To achieve this purpose, any team must advance through specific, achievable and motivating objectives.

In summary, purpose and objectives are the two pillars that support a team. A reason to be without an objective is just a blurry dream. Without any reason, the objectives are just a useless activity. But even if these two pillars are fundamental, they are not enough on their own. 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: the way in which the team should carry out its work, in particular on decision-making.

• Values, norms and standards that define what a member is entitled to expect from other members: for example, how to resolve conflicts between two team members.

• Feedback on the work and the figures that make it possible to measure the team’s progress.

This may seem obvious, but clarity on these small details helps to maintain an optimal pace and work atmosphere for all team members. Moreover, when all these conditions are met and the work group has become a real team, the members reach a high level of productivity not because their manager or manager has asked them to, but because their colleagues expect this high level of productivity from them: the team manages itself. If someone on the team does not work in 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 bonds that bind members together. When this happens, we say that it is a team management.

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

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

  • Take action 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 his or her colleagues.

We try to divide these indicators into two parts: individual behaviour indicators and individual outcome indicators.

In individual behaviour indicators, for example, we will seek to analyse and measure how the person will address others or how other employees may feel about the 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 team’s feelings and well-being:

We also use the HeyTaco bot, more fun, which allows you to send points to a person if one of his actions seems relevant to you (a little gamification never hurts):

In the individual outcome indicators, we will try to measure all the employee’s outputs that have contributed to the team’s ultimate objective. Examples include the number of errors made by the person, their time log, the number of suggestions or proposals they will make to the project. For this, we use Nikabot (, which greatly facilitates the team’s time log by asking a simple question via Slack at the end of the day. The results are recorded in a dashboard accessible to all.

  • Take action at the collective level (the team)

The analysis of the functioning of a team therefore starts with its members, but it is also possible and recommended to measure the team’s overall performance indicators. Again, these indicators will be divided into two categories: team functioning indicators and collective outcome indicators.

With regard to team functioning indicators, we will try to measure the team’s group dynamics and find out, for example, whether the team is effective at working meetings, whether it quickly reaches a consensus in case of disagreement or the techniques used to solve problems.


In terms of collective outcome indicators, the focus will be on measuring the team’s final output. The indicators will therefore be the number of tasks completed

Why implement data management ?

  • Measure the interaction

Teams thrive when their members have healthy relationships, when they take time to understand each other’s skills in order to exchange and bounce back on each other’s ideas. A simple way to measure interaction is to note the number of positive comments versus negative comments at meetings. Negative emotions being much stronger than positive emotions, high-performing teams have a ratio between 3:1 and 7:1 (positive vs. negative). Anything above 8:1 is a signal of false harmony in the group and a tendency to avoid angry subjects. Anything below 1:1 shows signs of very significant dysfunction.

  • Resolving conflicts

For many, conflicts are frightening and must 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 the issues that need to be addressed. Successful teams discuss these problems and exchange experiences and ideas in an open way to find the best possible solution. It is possible to note the number of team members who are actively involved in the idea generation and resolution process. Successful teams tend to be fair to 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 and validated these decisions. The non-performing teams agree on the decision given by the person above the melee and show no sign of commitment during the steps following this decision.

  • Creating added value

The members of a successful team all meet the same performance standards without the help of their manager. Are the members on time? Did they prepare the meeting? Are distractions, such as discussions between two people, email readings 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 report that they are not sufficiently involved for each other.

Savoir se poser les bonnes questions avant de commencer :

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

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

  • What data must be collected to match the quality measures I want to implement. The data you are going to collect must be relevant to the points you want to improve in the management of your team. If these points change during the year, the data you will collect must also change.
  • What are the sources of this data? It is important to determine 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 these data: the director, team leaders, employees?
  • What methodology should I use 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? Will I integrate them in a dashboard, on an excel, in a database?
  • How much data should be collected? it is not always necessary to collect all the data available. The important thing is to focus on the areas of improvement and relevant points of development within the team in which you work.

How do I know if the data I will collect is of good quality?

You can rely on data governance to do this.

There is an old saying in the business world that says “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 your data. In a way, a list of procedures that help you know what to do when you encounter a problem related to your data.


How to know the maturity of your company regarding data governance?

4 good advices following our experience at 10h11

• Do not become “dataholic”: too much data kills data

• Have reliable data and tools

• Be transparent with the team about the data collected

• Communicate as much as possible face-to-face

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