Datalab: the small labs that give rise to big projects

Data is the new Eldorado of business. It's a phenomenon that everyone, from GAFAMs and social networks to SMEs, is now aware of. Data can be a source of innovation, revenue and improved products and services.

Hence the emergence in recent years, within organisations, of structures dedicated to exploiting them. More or less advanced, they have gradually been integrated into the existing organisation chart. Several may coexist in parallel, as may the technologies that exploit this data.

How can we get human resources and infrastructure to work together more effectively on this central theme of Data? How can we innovate and implement new applications as quickly as possible?

This is the purpose of a cross-disciplinary laboratory dedicated to data: the Datalab. We explain what a project of this kind entails, how it can be carried out, and which companies are taking advantage of it.

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What is a Datalab?

Labs are all the rage. In a world where agility has become paramount, they bring flexibility and responsiveness to organisations. And they do so without the need to overhaul the hierarchy or the organisation chart.

Their aim is to encourage innovation by allowing experimentation.

A lab can be defined as " a structure that encourages the emergence of disruptive ideas, by isolating uncertain projects so that they can be implemented without disrupting the existing organisation" (Olivier Laborde).

This laboratory concept takes many forms. It's only natural that it should have been transposed to the field of data exploitation.

Today, a company can have many Data professionals. These include, but are not limited to, the Chief Data Officer (CDO), the Data Scientist and Chief Data Scientist, the Data Analyst, the Big Data Architect and Engineer, the Master Data Manager, the Business Intelligence Manager, the Data Miner, the Data Protection Officer, the Machine Learning Engineer...

This list immediately pinpoints the problem. All these functions are not attached to the same department, they do not necessarily have the same objective or the same culture. They do not use the same software tools. They are not in constant contact.

What's more, other functions generate, handle, use or benefit from data. Marketing, for example.

How do you bring all these skills together, and combine their expertise, in an agile and efficient Data structure? By creating a Datalab.

The creation of a Datalab is also an opportunity to integrate new skills and talents. Some projects include a large percentage of new recruits from the outset.

The Datalab will have the advantage of operating like a start-up, or an incubator, within the organisation itself. It is not a substitute for what already exists, but aims to enhance it in a different context.

Define a strategy beforehand

Even if the initial idea is to encourage flexibility and creativity, setting up a Datalab is best done with certain prerequisites in mind.

Strategically, if possible, priority objectives should be defined. This is primarily the task of management. Even if the aim of the Datalab is to encourage intellectual ferment and the emergence of creative projects, it is possible to direct its efforts. It's best to specify from the outset whether your objective is to diversify your business, improve customer service, collect new data, etc.

However, maximum freedom can also generate greater commitment and disruptive innovation.

Technically, you will probably have to deal with the complexity inherent in Data Management and Big Data. In a large-scale enterprise, there may be multiple independent 'silos' dealing with data, for historical, geographical or technical reasons. The technologies used to collect, store, process and use data may therefore be multiple and redundant, and require adaptation.

One of the advantages of a Datalab is that it can encourage technical harmonisation, perhaps even beforehand. The Datalab can lead to the creation of a Data Lake, if this is not already in place. What's more, since data quality and validation are of paramount importance, this can be an opportunity to check these prerequisites.

From a legal point of view, in-depth work is also necessary. But it is always necessary when you want to use data on a large scale. The heterogeneous origin of the data means that it has not been collected in the same contexts, with the same objectives, and therefore not necessarily with the same initial legal constraints. This is a classic difficulty, which should not be underestimated from the outset.

Don't overlook human resources

Beyond these three pillars to be taken into account, there remains another difficulty. It's inherent in setting up a cross-functional structure: it's the human factor.

You are suddenly going to bring together, within a sometimes informal entity, employees who have one thing in common: Data. But they may also be radically different. Nothing predestines them to form a happy band of friends united around a common objective.

In the Datalab, you may find profiles as different as a marketer, a data engineer, a salesperson and a coder specialising in Machine Learning. That's what a laboratory is all about.

It may therefore be a good idea to plan some preliminary work, or follow-up, with a consultant specialising in change management.

All the more so as there may be resistance to this change, which is quite logical. Employees may be more inclined to keep their ideas, skills and inputs to themselves, and use them within their own department. So we need to ensure that the results of Datalab are rewarding for everyone.

One detail, but not the only one: the premises

As you will see from the examples that follow, most companies that set up a Datalab (and probably other types of laboratory) do so in dedicated ^premises1.

There are several reasons for this:

• It is important that the premises reflect the freedom of the organisation. They should be open, bright, flexible and fun, with a minimum of constraints.
• It may also be important for the premises to be cross-disciplinary. This means that they should not be physically attached to one service or department (which would then take precedence over the others).
• Ideally, the premises chosen should be completely new, designed for this project, even if this is obviously not within the reach of every company.

Drawing inspiration from success stories: 3 examples of Datalabs

According to Les Echos, two-thirds of CAC 40 companies already have a Datalab, so it's easy and useful to take an interest in them, to copy their model or avoid making the same mistakes.

Here is some feedback from a range of sectors.

Axa: an insurer at the heart of data

Axa's Data Innovation ^Lab2 was designed around an R&D team of... 4 people. Launched with 15 employees, it now has more than 70, to which must be added around thirty external or one-off participants.

Created in 2014, one of the Lab's objectives was to create car insurance policies whose price would change according to the driver's behaviour. It has set itself 5 research objectives: fraud, claims management, analysis of driving behaviour to reduce premiums for virtuous drivers, connected health and marketing.

Axa signed a contract with Facebook in 2014 at the time of the launch. As a result, the insurer no longer only manages its own data flows, but also external data with Open Data.

The projects that come out of this laboratory are considered by Axa to be "agile satellites", platforms that are independent of IT at the outset. Subsequently, if they are sustainable and prove their ROI, they can be integrated into the existing system.

AXA's Datalab project is highly significant in that it includes a 'new talent creation' dimension right from the start. The insurer has released a budget of €180 million to train future employees. This has led to the opening of a "Digital Strategy and Big Data" chair at HEC Paris and another "Data Science for Insurance Sector" course at Polytechniques.

Finally, to take its innovative approach to the next level, Datalab has also created an incubator. The incubator relies on the AXA Strategic Ventures (ASV) investment fund to support promising projects.

A total commitment to the digital transition. The insurer is thus in a position to help new projects emerge, to make them a reality, and to integrate employees trained for these tasks. A must.

The SNCF, a Data Company that didn't know it was there

Would you have spontaneously cited the SNCF as an example of a Datalab? Probably not, and yet this creation is perfectly justified.

It was on 29 August 2018 that the heads of the various entities of the SNCF group presented the new stage of the digital strategy: building the company of tomorrow thanks to ^data3.

The historic company has a wealth of data. We just need to be aware of it and make the most of it. Our data assets include historical data such as timetables, service data for 15,000 trains, 30,000 kilometres of track and 3,000 stations.

But more recently, it also includes the data that customers entrust to us through the services offered in stations, and the on-board experience (3G/4G connectivity, Wifi, etc.). The aim is to use all this data to support decision-making, business management, performance and safety.

This has led to the creation of a Datalab. Here, the aim is not so much to innovate as to give everyone access to the data. The laboratory is a vehicle for sharing data. Potentially, any agent can connect to the DataLab and access datasets . The potential value creation will come from manipulation by the agents.

The laboratory has been operational since 2018. In 2019, it had 350 datasets referenced.

It should be noted that, since 2010, there have already been Minilabs developed with École des Mines Paristech. Some of these Minilabs are also dedicated to the use of Data. One of them, for example, aims to anticipate the impact of climate change on rail ^networks4.

BNP: using Artificial Intelligence to innovate

As well as being 100% dedicated to data, the Datalab can also be an accelerator for Artificial Intelligence. And in particular for the development of Machine Learning projects.

This is the approach taken by BNP. In the particular context of the banking market, it was the need for confidentiality that led the Group to bring this structure in-house. This means that research, data manipulation and the creation of new applications can be carried out in complete security.

Among the projects that have come out of this laboratory is an astonishing translation application called "Translate". Initially, only three data scientists were needed to develop the PoC (Proof of Concept). Then it was a reinforced Machine Learning team that helped finalise it. This new tool, designed for professional documentation (contracts, reports, technical documents, etc.), quickly became a firm favourite in-house.

At least a dozen other projects have already been launched. These include a system for automatically analysing contracts, a search engine, a chatbot, a tool for analysing emotions, image analysis and character recognition.

BNP is making no secret of its ambition, through this Datalab, to acquire and develop extensive experience in AI-related fields. This will enable it to position itself as a credible player in this sector in the future.

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💡 Key points to remember

• A Datalab is a structure dedicated to innovation around Data.
• Its purpose is to bring together a wide variety of human resources and infrastructures.
• It can be a transversal structure, or give rise to a more integrated creation in the organisational chart.
• It has been adopted by a large number of CAC 40 companies.
• It can be extended to related areas, such as Artificial Intelligence.