Can you briefly remind us what open data is all about? What are we talking about?
Open data means making available data produced by public entities. This includes information on companies, land registry data, seismic risks, climate data and much more. Two very concrete examples are the train timetables published by the SNCF and the financial data on companies held by the registries of commercial courts. This information used to be difficult to access, but European directives and France's 2017 Axel Lemaire law have changed the game by making it freely available with a single reuse license. Another example, with court decisions that will also soon be available in open data, which incidentally raises many questions such as anonymization and data reuse to protect privacy.
By making data easily accessible, the aim is to foster innovation, or more simply to improve solutions and services in various sectors of activity.
This being said, open data, which offers greater transparency for many types of data, is confronted in its reuse with the need to protect personal data. While it opens up new perspectives, it also requires extreme vigilance with regard to conditions of use and distribution.
With the advent of open data, some people thought they were seeing the end of the paid-for B2B information business model, in favor of freemium information sites. Yet information companies are still around. How have they managed to survive?
The role of Ellisphere and the other players in the corporate information market is to help economic players make decisions quickly thanks to reliable information. To do this, we work with economic, financial, extra-financial and legal data to build solutions tailored to the needs of risk management, compliance and marketing professionals.
In addition to the paid sources we use, open data has made some data available free of charge, but often of lesser quality. To maintain the quality and reliability of the information we distribute, we have to check and cross-reference the data collected from several sources, which means an additional workload for our teams. However, this work remains essential to guarantee high value-added services, and to maintain quality BtoB data on the information market.
Open data is just another source of information...
Indeed, open data is a valuable source among others. At Ellisphere, we cross-reference this data with our proprietary repositories to ensure consistency. For example, in France, 60% of companies choose not to make their accounts public, which limits access to this information for the general public. However, companies like ours have access to this confidential data, without being able to disseminate it as is, enabling us to gain a more complete and accurate picture of the economic and financial health of the various players.
In addition to these consistency checks, we enrich open data with our own sources to create relevant information solutions. These control and enrichment processes are crucial to ensuring that our customers' decisions are based on reliable information.
Even more so than for open data, the advent of Artificial Intelligence is seen as the beginning of the end for many professions. What is your analysis of the arrival of AI?
Artificial intelligence represents a revolution in the way we process and analyze data, as well as an opportunity to build new solutions and services.
However, for AI to be effective, it must be powered by high-quality data. We need to ensure that the data used by our algorithms is accurate and complete. AI applied to raw, unprocessed data will not produce reliable results. That's why we focus on the quality and integrity of the data we collect and analyze.
Once again, data quality is at the heart of everything we do.
At Ellisphere, new technologies have always played a central role in the company's development, and in the deployment of major, highly innovative projects. Can you give us some concrete examples of how new technologies have enabled us to build new products, services or processes?
New technologies, and AI in particular, have transformed the way we work and the services we offer. For example, we use AI to detect document fraud or inconsistencies in the information provided by companies. A case in point is the detection of false legal representatives or administrative documents, which is crucial in preventing certain types of fraud.
We also use AI to analyze the economic health of companies. Algorithms can detect weak signals indicating possible default or, on the contrary, positive signals about financial strength. This enables our customers to make more informed decisions and better manage risks, particularly in credit management.
New technologies mean new professions, new work organizations... How can we maintain the necessary skills in our teams, but also put in place new business expertise that will contribute to the development, particularly of a company like Ellisphere?
Employee training is a key element. At Ellisphere, we have set up training programs for our data managers so that they can use new tools and techniques to collect, control and enrich information. This training changes their work habits, but is essential to maintaining the quality and reliability of our data repositories.
Once the information has been checked, it is analyzed by artificial intelligence algorithms to detect the probability of default, fraud or growth. This approach enables us to remain at the forefront of innovation and offer our customers ever more effective solutions.
In short, open data and AI represent major opportunities for Ellisphere. Combined with solid human expertise, they enable us to innovate, develop new products, and offer our customers reliable and relevant solutions for ever more engaging decision-making.
But using open data and AI without control is absolutely inconceivable for us. And as our Anglo-Saxon partners abruptly but evocatively put it, "bullshit in, bullshit out". The answer lies in combining new technologies with solid human expertise.
