Mobapi is a technological solution that implements the entire value chain of data, from its collection to its visualization. Its application areas are numerous. Open Data is an integral part of it, which is why Mobapi is interested in supporting public organizations in the process.

The challenges of Open Data

The digitization of public data has redefined relationships between public and private actors, as well as citizens. It facilitates access to public data and its re-use.

These issues have been taken into account by European and French legislatures, who have made rulings on the subject. In France, it is the communities that are invited to lead the process and involve their municipalities.

Although open data is a technical challenge, it is also, above all, a societal challenge.

1/ Put data back at the heart of public action

Opening data will require preliminary re-appropriation work. It is therefore important for a community to make data and its use a strategic issue that lets them:

  • Guide public policy decisions
  • Optimize public spending (by sharing tools and mastering data)
  • Facilitate the economic development of its territory
  • Inform and involve citizens in the public policy of their territory

An organization’s data is often fragmented between its various services, and divided into heavy business software. Their opening requires the implementation and synchronization of actions both technical and organizational.

Read: Practical guide for opening public data by Fing

2/ Prepare for the opening of data

Since public data is subject to specific legislation, it is important to anticipate the legal aspects governing their availability and use.

Read: Laws regulating public data

It is also necessary to take a step back from the data managed by the organization by mapping its information system. The major limitations to opening data, such as redundancy or poor interoperability, can then be identified.

In addition to better understanding the opening, the organization will be able to urbanize its data and implement a common data management system for all its services.

With a team experienced in IS data management, Mobapi is an expert in helping organizations urbanize their data in order to open them.

3/ Prepare the data for dissemination

The mapping of the IS data must be supplemented by an evaluation of the use of the data itself, internally as well as externally. We will then be able to reflect on their dissemination by defining in particular an opening policy that makes explicit:

  • The storage mode for open data
  • The dataset formats
  • APIs to access them

This opening policy will also be reflected by the license of use chosen for the data’s availability. Two licenses form a consensus for Open Data projects, each with their own political and economic messages:

How to implement an Open Data project?

1/ Form a knowledgeable team

To succeed, the political impact of an Open Data project should not be overlooked. It is a question of educating the public actors on the opening of the data by involving them in the project from the beginning. It will be important to form a steering committee composed of elected representatives. Keeping them informed about the progress of the project will help them better align the project with their political lines.

The project team should also bring together all the community’s services around data culture: DSIs, lawyers, researchers, business services, communication services, field agents…

The formation of an ethics committee is also important: its role is to provide a legal and/or citizen-centered vision on the dissemination of data described as “sensitive.”

2/ Show agility

Involving many actors, an Open Data project can seem complex and time-consuming to implement. It is therefore important to adopt an “agile” method of management to make its progress and interest visible to all.

We prefer the implementation of practices favoring collaborative work and collective intelligence around open data. For example, we can choose a project-specific mode of governance and free tools to facilitate the involvement of services in the process.

Read: The 10 commandments of Open Data experimentation

Agile management must be accompanied by a pragmatic approach to the actions to be taken. It will be de rigueur for the pedagogy to explain the stakes and opportunities of Open Data to the actors. In this way, we will be able to optimize the culture of openness by working with services in the interest of opening data. The focus will be on easy-to-open data, and open communication on the project’s progress.

Read: Proposed priority data base for the NOTRe law

3/ Use the ecosystem

It may be wise to get closer to external partners that are working to support Open Data initiatives. We can cite institutional players such as Etalab (government initiative) and associations such as Open Data France, FING, Libertic and the GFII.

Read: Report on local authorities’ support mechanisms for opening data

Another aim will be to facilitate meetings between producers and consumers of data via events (hackathons, contests…) or the implementation of an infolab

4/ Measure the impact of the opening

Finally, it is important to plan the project’s evaluation, whose relevance depends on the methods used. Many of them are known and documented. Their integration provides an overview of an Open Data project’s impact by measuring its “performance”:

  • The degree of data openness (via the MELODA tool or Tim Berners Lee’s rating scale)
  • Reuse of data (by gathering information)
  • API uses (by measuring the number of calls)
  • Feedback from social networks (resulting from Open Data-driven communications)

Surveys are also a means of evaluating the project’s less visible aspects. We can reference, for example, the first Open Data study conducted by the Loire Atlantique in 2014 through surveys and workshops. This study shows the importance of raising internal team awareness upstream of the Open Data project launch so everyone understands its ins and outs.

Examples of successful Open Data projects

More than 20 years after the first definition of Open Data principles, there are plenty of proejct examples. In France, the first initiatives came into being in 2010, so it is possible to assess their results and impacts with considerable hindsight.

Collaborative production

This concept consists of bringing together public actors, private actors and citizens to produce publicly available data. We can give the example of OpenStreetMap, which has joined the Montpellier metropolis and the municipality of Plouarzel, among others, to map public spaces through the direct participation of residents.


“Crowd repair” involves calling on everyone to correct erroneous public data. A 2011 project was led by Nantes Métropole in collaboration with OpenStreetMap to improve postal address data. These have been opened by the organization but not all of them have associated geographic coordinates. A similar example from 2012 is the integration of metro station data in Paris

Open innovation

This refers to the innovation process based on sharing and collaboration. It is now promoted and used to reinvent interactions between public organizations and their constituents.

For example: the public Reactor program carried out by the 27th Region, the Fab City project, and the 62 open innovations published by the city of Brest. 

Further reading:

The information presented in this article was largely derived from the book “Open Data. Opening, exploitation, evaluation of public data” by Vincent Kober, the “regional editions.”