"Data is a heritage asset which we can build a robust innovation strategy upon"
In March 2026, the CNRS set up a department dedicated to data analysis and foresight for innovation to deepen its understanding of its innovation ecosystem and thus support the transfer of research into the socio-economic sphere more effectively. The new department's director, Anne-Laure Thomas Derepas, outlines her ambitions.
Key takeaways
Analysing innovation data helps detect weak signals in terms of partnerships and themes and feeds into the CNRS’s innovation policy.
The CNRS can identify potential for collaboration on key issues, from the water cycle to quantum science, by cross-referencing the CNRS’s mapping of scientific expertise with industrial challenges.
This foresight initiative aims to strengthen the competitiveness of its industrial partners.
Why set up a department dedicated to data analysis and foresight specifically for innovation?
Anne-Laure Thomas Derepas: The CNRS does a significant amount of work on innovation, with nearly 1300 new invention disclosures a year, 300 joint laboratories run with companies and 80 start-ups set up every year. Our presence across France also gives us a comprehensive overview of innovation deriving from basic research, particularly through the university innovation clusters (PUIs). And yet, this data has only been used to measure activity until now and knowing how many patents the CNRS filed last year is simply reporting. Identifying a particular technological field that's experiencing strong growth amongst European manufacturers, while the CNRS is scaling back its industrial protection activities in that area, is decision support. The DDAPI aims to facilitate exactly this shift in perspective. Creating a structure like this also makes sense at a time when Chief Data Officer roles are becoming widespread in both large companies and public research organisations in France and abroad.
How can analysis of data then feed into the CNRS’s innovation policy?
A.-L.T.D.: Having robust, reliable and reproducible data means we can see more clearly which direction we're heading in and then draw on this consolidated data to detect weak signals about the industrial partners we're most actively working with and the topics involved. Beyond the internal data on contracts or patents that has already been analysed, we also draw on national and international open-access sources. We try to take this further, for example by analysing our joint publications with companies or carrying out comparisons with other French and foreign organisations. This means the CNRS's Business Relations Department, the Partnership and Technology Transfer Departments (SPVs) at our Regional Offices and, more broadly, the CNRS Innovation Office can then direct their activities on a more informed basis. In particular, the SPVs manage contracts and the resulting interactions with companies on a day-to-day basis and actually produce a large proportion of the data that we then consolidate. Ultimately, our analyses are intended to enable them to plan for the future by identifying emerging or declining partnership dynamics in their regions.
Analysing innovation-related data enables you to carry out foresight analysis but why is this foresight work necessary?
A.-L.T.D.: If French companies are to remain innovative, we need to understand the technological obstacles they face and pinpoint the research laboratories that could help them to overcome these. I’ll take the water cycle as an example, as we recently carried out a study on this. The quality of the water we consume and discharge is now threatened by the presence of various pollutants like PFAS, microplastics and pharmaceutical residues at every stage of the chain – water treatment, distribution, industrial uses, wastewater collection and treatment and reuse. These pollutants can impact the entire socio-economic chain. We've identified the innovation challenges at each stage and mapped out all the public or private stakeholders in the water value chain. This type of integrated analysis is rarely carried out on this scale and helps facilitate collaboration between research laboratories and businesses, particularly SMEs and mid-market companies that don't have the resources to develop a strategic vision like this alone. Anticipating potential technological developments means we can position ourselves to address this issue of sovereignty and survival, including in France, where extreme events like droughts and floods are becoming increasingly frequent.
In a context of increased international competition, how does this foresight work help boost competitiveness?
A.-L.T.D.: The analysis of data on joint publications, patents and partnerships enables us to identify the dynamics of collaboration between the CNRS and companies in France and internationally. For example, cross-referencing bibliometric data with patent databases means we can pinpoint the areas French research is particularly active in and the industrial partners that work in the same fields. The Business Relations Department can then use this information to feed into its discussions with R&D managers at major French companies about setting up more targeted research collaboration projects. It can also work with the CNRS’s 11 representative offices abroad to support industry partners in their international expansion strategies.
Is there such a thing as ‘risk foresight’ in the field of innovation?
A.-L.T.D.: CNRS Innovation has just set up an economic intelligence unit which the DDAPI collaborates with. We can help identify high-potential technology transfer opportunities by cross-referencing data on intellectual property, partnerships and market dynamics in an approach that involves detecting weak signals to inform innovation strategy.
How are basic and applied research linked at the CNRS and how does the DDAPI act as a bridge between these two spheres?
A.-L.T.D.: On a day-to-day basis, the link between basic research and technology transfer is maintained by two mechanisms, namely the network of technology transfer engineers managed by the Business Relations Department, and our network of innovation engineers managed by CNRS Innovation. These engineers work in our laboratories or Regional Offices and possess scientific expertise as well as an understanding of the industrial sphere. The DDAPI's role is to consolidate the data that these frontline actors produce to enable reliable national-level monitoring of technology transfer work.
The CNRS is involved in 28 of the 29 France 2030 university innovation clusters. How does the DDAPI help structure this ecosystem?
A.-L.T.D.: The CNRS is the co-founder of 28 of the 29 university innovation clusters and contributes to the production of national indicators that enable funding bodies to assess these clusters. In this context, the DDAPI's role is to organise the collection of these indicators across the whole network so all stakeholders have the same level of information and the same definitions to work with. However, our role does not stop at producing figures, as we also need to make sure they are reliable. For example, one of the national indicators measures the time taken to negotiate research contracts but the very definition poses a problem because what is the actual starting point of a negotiation? The first contact with a company? When a draft contract is sent? Or the first formal meeting? No one working in the ecosystem had the same answer. At the national level, we're helping to clarify these definitions so the indicator measures what we actuallywant to measure, and then making sure the right information systems enable this to be recorded correctly. This work is not particularly visible but is nevertheless essential because a poorly defined indicator can end up leading to false conclusions being drawn.
What topics are you planning to work on in the coming months?
A.-L.T.D.: The study on the water cycle demonstrated the value of this approach but also the investment this requires. Mapping a value chain, identifying industrial stakeholders and aligning the CNRS’s expertise all represents quite a substantial amount of work and we can't do this for every subject. The challenge, then, is to develop a method for collectively identifying the areas in which this type of analysis will have the greatest impact. This involves associating the CNRS Institutes, which are familiar with scientific developments, the Business Relations Department, which is aware of industrial requirements, the Regional Offices, which are closest to the reality of local contexts and also, for example, the Climate, Biodiversity and Sustainable Societies’ programming agency, which works on themes that directly intersect with innovation and technology transfer issues.
You took up your post in March 2026. What kind of dynamic do you aim to drive within the DDAPI?
A.-L.T.D.: The fundamental change I want to lead is the shift from an approach in which data is used to measure past activity to one where it informs future decisions. For example, the DDAPI will help identify which areas we need to focus our technology transfer work on, the right partners to deepen collaborations, and where the potential for technology transfer lies that hasn't been pinpointed yet. I'd also like to integrate business development and strategic foresight more effectively. By combining our knowledge of industrial partners with an analysis of emerging themes, we can identify collaboration opportunities that the Partnership and Technology Transfer Departments and the Business Relations Department can then set up with the right industry partners.
