Giving Compass' Take:

• PricewaterhouseCoopers shares seven layers of data collection and management needed to ensure the successful transition to smart cities. 

• How can funders help cities to responsibly collect and manage data as they transition to smart cities? 

• Find out how smart cities can lead to sustainability

Data Catagories: The city of the future will ingest data from GPS systems, traffic sensors, mobile devices, environmental and climate monitoring, individuals’ social activity, industrial IoT sensors, vehicles, plumbing systems, waste receptacles, the electrical grid, and many more sources—forming multiple categories of data, including many that are still to be invented.

Both private and public smart city stakeholders must build data governance and management ready for all categories of data—not just the ones involved in their current projects. That requires an assessment of expected current and future categories. It also requires a scalable, flexible, and modular design that can treat these categories differently, as needed, in the six layers that follow.

Consent: Today, few, if any, smart city initiatives tell individuals what data they are collecting, who is doing the collecting, and what exactly will be done with that data. As a result, informed consent is a challenge. The solution is to design citizen-centric data governance and management that offers individuals an easy way to understand who will do what with their data, along with clear benefits to them for actively opting in to an initiative. Residents should be encouraged to renew their choice to opt in at regular intervals, with withdrawal of consent also made easy.

Collection: Private and public entities must design data governance and management not just to collect multiple categories of data, but also to standardize, encrypt, and analyze all this data, even as it arrives in different formats and through different channels. Yet many existing IoT devices lack the processing power to parse data before transmission, as well as the bandwidth to support robust encryption. Smart data strategies therefore require engagement and planning to set technology requirements that support today’s needs, as well as those that encompass emerging technologies, such as 5G, which will offer more robust capabilities.

A smart strategy for data collection must also prepare to verify data’s quality and cleanse it to make it actionable. 5G networks, for example, may have as many as one million devices per square kilometer. To correct errors in this coming tidal wave of data, and to pinpoint which data the system needs to retain, will demand a sophisticated, powerful data architecture.

Anonymization: Without the right preparation, smart cities can be a nexus of cyber risk, since they integrate data from so many different sources. City governments and the companies they work with directly control some of these sources, but many are in the hands of third parties.

Whatever its source, anonymizing data is the best way to protect it—and to reassure residents concerned about privacy. Smart city governments and businesses should therefore deploy anonymization by design: data management that anonymizes incoming information at the source, before it is stored.

Storage: Public and private smart city stakeholders alike need cost-effective, scalable and safe data storage. Whether or not they depend on cloudbased systems, they will need measures to ensure they retain and fully secure the right data (and only the right data), while eliminating the rest. Policies on data retention and elimination must meet both their own operational needs and comply with local regulations.

As smart infrastructure becomes ever more critical to urban life, cities will also need a well-defined and audited smart city business continuity plan. The plan should establish requirements for secondary storage locations, as well as resiliency and recovery programs in case of a natural disaster, equipment failure, or other outage.

Access: Today, in most cities no one participant fully owns all of the relevant data. Data management models must therefore offer public sector entities, research firms, private entities, city residents, and other key stakeholders secure access to the data they need (and only the data they need), while preserving privacy rights. Different categories of data may need different access parameters.

Best practices include open, non-proprietary application programming interfaces (APIs) and data formats; cities adding a standard “data ownership clause” to procurement contracts to ensure access rights; and modularity to enable partial and tiered access. As part of that tiered access, cities may also want to assess the risks involved with certain third parties, and how best to minimize those risks.

Monetization: AI can turn the data that smart cities gather into valuable intellectual property. To make sure that they do not miss out on these and other opportunities, governments and companies will need the above six layers in place, as well as advanced data analytics, including AI. To support innovation and agile decision-making on monetization opportunities, smart city players will also need a data monetization framework to rapidly assess use cases’ risks and value, along with structures to mitigate those risks and grow that value.