“Preparing the country for the challenges of the digital transition of tomorrow's economy” is the ambition of the law for “A Digital Republic” enacted in 2016. Its application has accelerated open data strategies in administrations. Henceforth, Anyone can access public data to understand the main characteristics of a territory: the topics covered in the deliberations, the position of an elected official on a subject related to your activity, discussions around competing projects within the CdC, etc.
This information is a priori easily accessible to companies that work with local public authorities. A priori, because the volume of administrative documents to go through is gigantic, not including articles published in the local press. An EPCI like “Basque Country Community” for example publishes several thousand pdfs every year, to which must be added the historical stock of administrative documents. And the information that will interest sales teams is sometimes found in a 5-line paragraph, in the middle of a 200-page document.
So how can you take advantage of the wealth of these documents to understand the challenges and dynamics of a territory in a minimum of time? ?
Part of the work of sales teams consists of understand the local situation, the positions taken by elected officials, the history of the development of infrastructure projects, and the budgetary situation of municipalities, in an area that can count several thousand municipalities.
This involves reading thousands of pages of administrative documents and trying to keep an eye out for any new publications. And the panorama would not be complete without articles published in the local press, which further burdens the list of necessary reading.
To put it simply: It is a task that cannot be done manually. That would be like reading the entire Harry Potter saga several times a month, for each project. This colossal but important work represents hours of work that a project developer does not have. And yet, if he does not, he will regularly miss out on key information related to his projects.
Here is an example of a document that can be retrieved as you search. The document is scanned, which makes it necessary to search it manually, line after line, for information relevant to its activity:
Development of the PLU of La Verdière
If this systematic work cannot be done by hand, there is a technology that allows large quantities of textual documents to be automatically read: the Natural Language Processing.
We use numerous applications based on this technology on a daily basis: voice assistants, connected speakers, chatbots or even translation tools are some of the practical applications of NLP. We can now add to this list the automatic reading of administrative documents and press articles In order to identify information useful to promoters of territorial projects.
Explain put this technology at the heart of Goodwill, territorial intelligence software for companies that work with local public authorities. Our Data Science teams have developed a combination of NLP algorithms in order to identify key information in the documents mentioned to facilitate prospecting work: locally influential people and organizations, themes, locations, position papers are all elements that our technology makes it possible to identify.
At the end of the day for users: make data that cannot be found by hand accessible, not only to allow them to identify new opportunities, to anticipate risks that may impact their business, but also to simply save them time. A time that they can then use to go further in the field, to meet the influencers they have identified thanks to Goodwill. Or why not, to read the Harry Potter saga!
