October 7, 2022

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Data and Product Team Manager Romain Lotte oversees approximately 15 employees who are in charge of data science projects to develop data, analyze user behavior, monitor the customer base, and develop L’quipe’s digital product. In this in-depth interview, Romain explains what L’quipe has done over the years to lay the foundation for data governance and the way forward in optimizing data management and democratization processes.

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Some statistics L’quipe… 2.5 million unique visitors per day 1.5 billion page views per day 80% of page views on mobile 300,000 customers 5 times a day, average time users spend on the app


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Can you tell us how L’quipe is arranged internally?

Within the Digital Division, there are four business units: a sales, acquisitions and loyalty team; a social media unit; A technical department and product and data team that I have been managing for the past six months. At the group level, there is another team on data, made up of data engineers, whose mission is to structure the data model to make sure the flow works properly and that the data is stored in the right place. Is. The editorial team is the foundation of our brand and is “unique” in both print and digital but is split across soccer and general sports. We work with all of these people on a daily basis to develop L’Équipe’s data policy. We should also include Management, TV, Finance and ASO teams etc.

How did you first get acquainted with the theory of data governance? What was the main issue in the beginning that led you to look for data governance as a solution?

When I joined L’Équipe, I wasn’t particularly well versed in data governance as I was essentially doing web analysis and I was pretty much off-putting to all data science issues. So I learned everything late in the game. In 2017, when I took over the data team, I wanted to put web analytics at the center of our data ecosystem because in a media publication like L’Équipe, the browsing paths and consumption trends of our users are, in my opinion, our most valuable asset.

Since then, I noticed that many teams were doing the “data” themselves and for a given KPI, we could have at least four different results. This situation was not sustainable. It was imperative to make data more reliable by centralizing it within a team. Therefore, we quickly eliminate many of the dashboards being circulated internally. Now, all of L’Équipe’s analytics dashboards have been developed and “labeled” by the data division with the support of the IT department to structure the data.

What exactly is data governance for in an organization like L’Équipe?

Quite simply, it is for all the employees we work with on a daily basis. The goal of the data governance policy is to convince all employees that the data team is at their service and that it can always provide them with all data – as far as possible – and that no data has any value, not authenticity. But if data governance concerns all the teams in the digital division from a very operational standpoint, then management must be involved as well. The latter should be aware that the data may (and may not) come from several people but from a single entity. This is a fundamental point for the success of the project.

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When was the foundation of the data governance policy in L’Équipe laid? What were the reasons for implementing it?

Prior to my arrival, data governance had begun at the group level, at a time when the Les Echos and Le Parisien brands were part of the Amaury Group. The overall group was data governance but, within each unit, we also had web analysts who worked on traffic analysis. After the sale of two media companies, we had to reorganize the data teams. Actually, at the time we had two separate data teams within L’Équipe, one for the customer service and sales teams and the other for web analysis. When I became the data manager, we brought these two teams together.

From the start, our communication with the staff improved and became more fluid as we were getting rid of a lot of repetition. For example, we used to get different results in our analyzes because our calculation methods differed from team to team. There were also a number of rules that were not widely known such as:

Impossible to aggregate visits Web visitors are counted per day

As a result, we were regularly receiving incorrect conclusions, all of which could have consequences for our internal decision-making and the performance of our digital activity. Management also wanted to establish a group data vision – between advertising, print and digital – with the creation of committees for teams to work together and establish common roadmaps.

In practical terms, what were your first data governance actions?

Making data accessible to as many people as possible I find a key element in establishing a data governance policy within an organization. Between 2017 and 2018, we built several dynamic dashboards and made available to teams, notably:

The Global Digital Subscriber Base allows you to view and churn out subscriptions across our entire customer base in real time. Direct Subscription allows you to visualize the share of published articles on the topic per game, the amount of subscriptions generated per game, and the details of the subscriptions generated for each article. Data for total, per game and per article page views.

These earlier dashboards were intended for editorial staff for the purpose of managing all editorial activities. Later, we created other more detailed dashboards that were more commercially oriented: customer base, cancellations and subscriptions, user groups, churn rate, and more.

Plus, there’s a monthly routine involved in integrating all the teams around the data, sharing key statistics, analyzing them, and making decisions for the product.

Do you design analytics dashboards in collaboration with business teams? Can they make them themselves?

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In the beginning, when data was not as democratic as it is today, our employees had fairly basic needs. They had a vague idea, but they didn’t know what they could actually do. So working together was rare. Today, we work more hands-on with them because they know how we work, what kind of data we store and what we can analyze. So our discussion is more constructive than before.

On the other hand, our employees do not have access to more data than what is shared with them through dynamic dashboards. It aims to centralize everything and make the data more reliable in the center of the data center. So, maybe they will need more data in the future, but at this point, we are still in the phase of data adoption and evangelism.

Implementation and control of the tagging plan is critical in a quality data governance approach. How do you organize this important move within L’Équipe?

Today, product teams are well aware that they must inform the data team and get them in the loop as soon as they launch a project or feature idea. My goal would be to involve the data team from the design stage, so that they can think about the KPIs when building the interface. On the operational side, we have a web analyst working almost exclusively on tagging. As soon as the website or mobile app is released, they conduct a test to check that there are no regressions or side effects on our platform.

These tests are done manually, as the automated tests we tried, especially on mobile apps, were never successful. The bots were unable to reproduce typical scenarios on our screens. And, since 80% of our traffic is on the mobile app, we have decided to manually take care of the quality of our tagging. We know that manual tagging is quite tedious, but it is essential for our analysis work. Without reliable tagging and getting the data flowing in the right place, we can’t do anything. Tagging planning ensures that our production processes are as reliable as possible: analysis, scoring, personalization and materials management.

Do you have a committee or body dedicated to the validation of data projects?

No, at the moment no decision has been taken at the group level. We validate the launch, or delay, of a project with the Head of the Digital Division. Every data project must meet our objectives: to generate engagement or membership.

What resources did you mobilize to complete your project?

First, we invested in a data visualization tool and a platform. Plus, we’ve recruited a lot of people to expand the data team. We’ve grown our team with one person working on tagging, experts in the SQL language, and a more hybrid SQL and marketing profile that allows us to talk data. Lastly, one person works on data science and this has allowed us to work on many subjects faster.

Do you have any reference documents or resources that formalize all internal processes?

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We started this project during last year’s lockdown. Until now, we had no functional documentation of our data flow. Sometimes we even gave them names that were too unusual or not meaningful enough for someone new to the product. For example, we have a flow called Data Flow Event. If you are new to AT Internet, then apparently such a name will not make any sense to you.

In mid-2020, we established a glossary on the Confluence shared workspace, which allows us to identify and present each of our flows by indicating the data schema involved, the service provider involved, the time of reception, the creation of associated intermediates or not . Definition of tables and fields of all our data sets etc. This is a titanic task. We are still a long way from getting it done, but we are spending a lot of time working on it.

How do you ensure the quality of your data on a daily basis?

On the site side, as I mentioned earlier, we have a person who ensures the quality of the implementation of our tagging plan. We also have people who take care of the reliability of the input data flow on the large query side. Data cleaning is done by our DPOs in consultation with data, marketing and technical teams, and includes a regulatory data purification. In addition, we also have monitoring metrics to identify potential bugs or system crashes. For this purpose, we use New Relic on the web and Crashlytics on the mobile apps; But these tools are more for technical teams.

In this environment, what are the objectives of the data team?

Our team’s goals only align with the publisher’s business model, i.e. generating more page views, thus more display ads and more subscriptions. All of our data projects should align with this goal. Today, data is used to drive the right article to the right user, but it won’t particularly affect production. We can actually sometimes identify a user’s interest in a topic (eg, recent, MMA or Formula 1) and share this trend directly with editorial teams in the form of recommendations. But the data does not drive the newsroom’s editorial line.

What are the upcoming data governance projects?

At L’Équipe, I think we are quite mature from a data point of view, but there is still a lot of work to do to structure us at the data governance level. In order to make data collection more reliable we need to establish actual processes to build our data model. We also need to set up dev and production environments for the data because today, anyone can create a table or a view within our ecosystem. Soon, we are also going to take over all our data subjects shared with other divisions (Advertising, TV, Print) at the group level to move from a publisher data vision to a global data approach for the brand Can you

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