Smart cities, smart grids, smart mobility—the word “smart” implies that algorithms and artificial intelligence will optimize the delivery of services so that they become more efficient. One goal is to produce an elusive win-win of environmental and economic benefit. Smart devices learn patterns of behaviour and adjust service delivery accordingly. If your smart car knows that you regularly drive to and from work every day in the morning and the evening, once you plug in your vehicle, it may wait to fully top up the charge until late at night at times when there is less load on the electrical grid. Multiply these decisions by a million and more, and it can circumvent the need to build extra electricity-generating capacity.
These much-touted environmental benefits, however, require smart systems to be able to identify how you interact with your car and all of the other smart devices that you use. The more data that a system can use to learn behaviour, the more efficient those systems can become.
Here, the promise of open data to provide big datasets excites, especially for policymakers that are concerned about the tendencies of Big Tech companies looking to amass and monopolise data for their own smart services. Leaving data open means that more organisations and individuals are open to innovate and create these environmental benefits. Open data produces more potential win-wins. It provides more efficient services for citizens and also increases the chance for more companies—particularly SMEs that may find it difficult to gather a dataset of sufficient size—to improve their competitiveness.
But opening data that can identify individuals to provide that bespoke service creates a potential privacy challenge. When a company collects closed data, they can ensure that consumers understand the purpose for which their data is being used. Companies can seek consent for that data collection and protect people’s personal information. There are still data privacy challenges—partially addressed by the General Data Protection Regulations—but these challenges multiply once data sets are opened for experimentation.
Anonymizing, pseudonymizing, and aggregation are all ways in which open data can be cleaned of personally identifying features, but the more identifiable the individual in the data, the greater the potential to deliver smart services that provide an environmental benefit. There is no “one size fits all” solution, and various data spaces will need to weigh the costs and benefits of how to deal with data privacy without curtailing the potential benefits.
About the author
David Regeczi is a managing consultant for the Digital Economy at Capgemini Invent with 13+ years’ experience in economic development for high-tech industries in both developed and developing economies, focused on innovation and competitiveness in various subsectors of ICT. His focus is on helping policymakers better understand how the digital economy continues to transform public policy, focused on competition, fair relationships, and the economics of data.