Data: What Is Important Now and What Should We Be Doing?
In the panel session ‘Data: What Is Important Now and What Should We Be Doing?’ representatives spoke about how they are working around challenges related to the influx of data collected by smart city technology. This review of the main themes is meant to help other cities gain an understanding of what to expect as they begin to gather and organize data from smart city projects.
Resizing and Reprioritizing
The amount of data gathered from smart city ventures will often force a reevaluation of IT expenditures. Instead of spending excessively on big enterprise solutions that do not add the premium value they once did, cities may begin to move to open source technologies that have lower costs and provide more value to city departments and constituents. Spending more money on hiring high-quality individuals who can do custom coding may also be prioritized over software spending.
Increasing Data Privacy Defenses
The collection of data opens up the possibility that individuals’ rights to privacy may be violated. For example, health-related and crime-related data, such as EMS visit data, often includes address information. In order to use the data safely, cities must find ways to defend anonymity. In this example, cities may transform addresses into latitude and longitude coordinates that are randomized a little to the left or to the right so the location data in the data set is no longer the actual address. This is one example that shows how governments must be thoughtful about individual privacy when handling and releasing city data.
In order to get the maximum return on data coming into the city, governments must also work on improving data sharing among departments. This is the proverbial ‘breaking down of silos,’ but it must be handled in a useful and systematic day. Government officials must think about how to link data, bringing in data from different systems about different topics in order to get a 360-degree picture of the problems within the city.
Partnership with Research Institutions
Cities have opportunities to form great partnerships that are based around the data they have collected. They should look to collaborate with universities by making data available so that researchers and professors can research and help solve problems in the community.
Cities are looking to solve very similar problems with technology. Given the overlap, cities should work together to define what are the standards for data, and share insights and solutions in order to create common knowledge across cities.
Cities must also think about how they will use the data in a practical sense—what sort of decisions the data can help them make and how it can be used to solve problems within the city. The question is, ‘If I could tell you tomorrow everything you need to know so you could predict certain events happening, what would you actually do differently?’ In some cases, you may find that the predictions can’t actually cause a change in response, in which case, why try to predict it at all? For example, data may help a city predict a pothole, but the city would still just have to wait for the pothole to form and then fill it. In this case, the prediction itself was invaluable because it was unable to alter the course of events or improve the city actions.
Balancing System and Individual Optimizations
When city data is used to solve problems, cities also must be mindful of whether the data is used to optimize the individual, the system, or both. For example, if automated vehicles are optimized for the individual (to provide the safest and quickest routes), then the city could experience a form of ‘automated congestion’ because solutions were optimized for individuals and never optimized for safety or energy efficiency of the system. Cities have to be careful about what they are optimizing for, and run experiments to figure out the right balance.
Monetizing the Data
While some view city data as free because it belongs to the citizens and they have already ‘paid’ for by way of taxes, cities may also want to contemplate if there are ways to monetize data. One city is exploring a way to charge for access to a cloud mirror of the open data portal, where individuals will pay because there is value added to the data in the way of performance SLAs. Another city representative suggested the option to pay for access time (for those who want the data right away or in high volume). Representatives from other cities questioned whether monetizing the data would work at all – perhaps people would be unwilling to pay for it, and even if they do, the financial and administrative costs to maintain it may mean it is more of a burden than a benefit.
While these are all important things for cities to consider when gathering, organizing and sharing city data, the panel ended with perhaps the biggest issue of all—the question of liability. The Accelerator was ironically held just after Facebook founder Mark Zuckerberg was ordered to testify to Congress about how Facebook account data was used and whether that usage was appropriate. At the Accelerator, city representatives pointed out that governments and CIOs could eventually face the same thing if the data is not handled appropriately.
With open data, you don’t know who is asking for the data and if they want to use it in nefarious ways. There is a fair amount of vulnerability as we drive toward transparency and openness with city data, and foresight is needed to avoid nightmare scenarios, such as releasing data sets that, when combined, may be used to identify individuals and violate privacy. In dealing with these issues, cities can look at what companies like Facebook are doing to reduce risk, but they also must create a new governance model that includes an emphasis on data security and integrity.