On the occasion of the Autumn plenaries in Vojvodina, AER members will discuss how opening public data supports the emergence of startups in regions. The panel will bring together speakers from startups, regional policymakers, experts on design and data to discuss why, how and for what regions should open their data and look at challenges and opportunities.

Open Data… why?

Everyday, public institutions collect and generate enormous quantities of data in areas as varied as unemployment, air pollution, hospital consultations, trends in construction, petrol & diesel prices, wood harvesting, production of organic milk or the evolution of criminality. This data can be re-used to create or improve services both private and public.

• Innovation: access to reusable data inspires innovative services and new business models. For the period 2016-2020, the market size for Open Data is estimated to be 325 billion EUR.
• Transparency: publishing data about processes, spendings, priorities, use of services, outputs, and making it accessible is an essential part to improve transparency and governance
• Efficiency: by using Open Data, Member States are forecasted to make 1.7 billion EUR in efficiency savings by 2020.

From pollution tracking to employment

Examples of data re-use include for instance the tracking of hourly pollution levels in sixty cities in the world by Plume Labs. This French startup uses data made public by different agencies engaged in a policy of Open Data, such as Airparif in Paris to feed its air pollution forecast app and help citizens better chose the times and places they cycle, work out or play outside with their children.

Another startup, Sun Energia in Finland uses open meteorological data for an application which calculates the costs and benefits of a potential switch to solar energy, by
assessing the solar energy production for a specific building and estimating savings. Open meteorological data is one of the inputs for this analysis.

Arbeidsmarktkansen in the Netherlands, uses public open data on employment and the economy to help organisations and individuals evaluate their actual value on the market and orient their strategies.

The stakes for regions

Opening public data means changing the way information/ data is approached. It requires quality management at the level of data collection: data need to be accurate, comparable, consistent etc (comparing apples with apples, not apples with strawberries…)

Data also needs to be usable and accessible. Publishing data which is difficult to find and to understand or difficult to link with topics which matter for citizens, is likely to result in failure. This is why, in order to harness the potential of open data, organisations need to have the end-user in mind. To define who is the end-user, what their needs could be and ensure a variety of end-users can access and use public open data, it is essential to work hand in hand with civil society, businesses, academics. While this can seem complex, this is also one of the places where the magic of open data can start operating.

Indeed by engaging with stakeholders, by asking for advice on what matters, what is useful, what helps, organisations accept to be challenged. They accept to transform in order to deliver better services. Publishing data in itself is not an objective, what matters with open data is the whole process with and for society.

This is why open data policies and strategies are essential. By developping policies, organisations aim to stimulate and guide the publication of data but also to gain advantages from its use. In the video below Anneke Zuiderwijk from the Delft University of Technology explains the different stages of an open data policy.

Panel debate at AER autumn plenaries

The high level panel debate on Open Data for Startups in Novi sad on 25 Septembre will be an opportunity to hear from renown experts what are the stakes for regions in Europe in 2018:

• what is the feedback from initiatives across Europe and beyond?
• how has open data changed the life of citizens?
• what are the barriers and enablers?
• how much does it cost?
• how to ensure the data will be used?
• how to increase data litteracy and capabilities?
• how to make data inclusive and sustainable?
• …