2016 Open Data Research Symposium5 October 2016, Madrid, Spain

Researching the economics of data to help governments make better choices

Open Data Institute@ODIHQ

theodi.org

Peter Wells@peterkwells

There is substantial economic evidence for open data, yet many core datasets remain unavailable.

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How do we help governments make the decision to open up this data?

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What do we already know?What do we need to know?What can we learn next?How can we use what we learn (and learn from using it)?

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Macroeconomic studies

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Microeconomic studies

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● US Landsat satellite data● Danish address data● Transport for London● ONTSI study on Spanish transport● Arup study on transport services● ODI “open data means business”● ODI/Nesta open data challenge series● &c. &c. &c.

Case studies

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• 84% of Americans with a smartphone have used open data (Pew research)

The value of open data is proven.

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Conceptual models.

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Data is infrastructure.

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https://www.flickr.com/photos/pkwflickr/6188760566/in/album-72157627764211574/

Roads help us navigate to a location.Data helps us make a decision

Core datasets are highways on our data infrastructure.

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Data infrastructure underpins transparency, accountability, public services, business innovation and civil society.

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Data infrastructure includes datasets, technology, processes and organisations.

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How does the value created by data change as we publish it at different points on the spectrum?

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0.5% GDP difference if core assets are open rather than shared.(Lateral Economics 2016)

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Who benefits?

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Most of the benefits created by open data are received by service users.(Lateral Economics 2016)

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Core datasets are often available under paid licences.

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Data publishers need to replace this funding.

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Some of the value received by service users will be received by governments as tax revenues.

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In 2014 this could have been £3bn of additional tax revenue in UK.

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What about your country?

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This creates a sustainable and viable funding model for open data.

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There are still gaps in this thinking....

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There are always gaps in thinking

@odihq @peterkwells

Private sector data. Business models. Developing countries. Time to realise value. GDP does not recognise all value of digital services. Personal data, trust and ethics. (& ?) @odihq

@peterkwells