The foundation for smart city success Seven layers of data governance and management

1 | The foundation for smart city success: Seven layers of data governance and management

PwC | Smart Cities

Smart data governance is key ■ Imagine this: Sensors monitor vehicles

and pedestrians as they travel through your downtown, along with weather and road conditions. Artificial intelligence adjusts streetlights, traffic lights, congestion fees, public transportation, parking fees, notifications of parking space availability, and push advertising to maximize city revenue, profits for participating businesses, and the speed and safety of vehicle and pedestrian traffic.

■ Imagine this too, even more fantastic: Public opinion overwhelmingly supports this system and the businesses that participate in it, because individuals are confident that their data is private and that they are getting value for it.

$737

$2,094

$1,721

$1,428

$1,194$1,007

$858

$2,577

16%

22%

21%

20%

19%

18%

23%

Source: "Smart Cities Market Analysis & Segment Forecasts to 2025", Grand View Research, 2018 empowered by www.emis.com

2022202120202019 2018 2024 20252023

The global smart city market is booming: how well can you manage the data on which smart cities will be built?(US$ billions)

Revenue Growth rate

Smart cities are the future, but how smart a city becomes, how fast, and at what ratio of costs to benefits—and which companies will win shares of this booming market (see Figure 1)—depend on the answer to one question:

How well can you, a private or public sector entity, govern and manage the data on which smart cities will be built?

If a city government or a smart infrastructure provider fails to govern and manage smart city data intelligently and responsibly, they will not just leave significant value on the table as data-driven business models go to other cities and companies. They may also provoke a public and regulatory backlash that can stop a project dead in its tracks.

2 | Smart Cities—Adept data governance and management is critical to fully realizing the potential2 | The foundation for smart city success: Seven layers of data governance and management

Start with strategySmart city data governance and management must enable cities and businesses to turn data into benefits (both financial and quality of life), while ensuring public buy-in. Achieving those twin goals—benefits and buy-in—requires setting a smart data strategy at the start.

A review of global best practices, as well as PwC analysis, indicates that the most successful strategies for both public and private entities are built on seven layers of data governance and management:

1. categories

2. consent

3. collection

4. anonymization

5. storage

6. access

7. monetization

Together, these seven layers create a remarkable foundation: secure, actionable data, ready for business and cities to use for new business models and services, with residents fully supportive.

With that foundation, companies can help cities become fully realized smart cities:1 ones that offer not just point services or a few interconnected smart services, but a digital ecosystem, built on the city’s own digital platform, in which new products, services, and business models are rapidly innovated and deployed.

The result can mean new profits for businesses, new revenue streams for government, a more fertile environment for innovation, and better services for residents and local businesses, all in support of sustainable growth.

India advances on smart city data strategy

In India, the federal government’s Ministry of Housing and Urban Affairs has developed its DataSmart Cities² policy framework—with the assistance of city officials, academics, and private-sector leaders—to help enable smart city data governance that can solve complex urban challenges.

The Ministry has already developed a framework to assess cities’ data readiness on five pillars: policy, people, process, technology and outcomes. This framework focuses on designing City Data Policy across 100 Smart Cities to foster collaboration between government and private entities on best practices for data standardization, data categorization, data privacy and security, data flows and approval, and data archiving and retention.

The Ministry has also launched an open data platform,³ to serve as a single source of truth for open datasets from multiple cities and government agencies. The platform already contains more than 3,400 datasets and over 240 APIs. Every participating city must appoint a City Data Officer to facilitate data sharing and exchange through the platform and to help their cities derive value from the data ecosystem at the city, state and national level. The goal is to unlock the potential of open data, open innovation and co-creation. To date, all 100 Smart Cities have already been onboarded on DataSmart Cities initiative.

3 | The foundation for smart city success: Seven layers of data governance and management

Data governance and management is so critical because of what we might call the golden rule of smart city economics: the more quality data you can integrate, the more value you can create. Such value is not just monetary, but it also shows up in a city’s attractiveness to business, its government’s productivity, its societal inclusiveness, its environmental sustainability, and its livability.

Today, most smart city projects are point solutions: isolated initiatives for services such as LED street lights, pothole reporting, parking, digital bill pay for civic services, or waste and water management. These initiatives are valuable, but when a city or service provider can integrate initiatives, with ever more granular data from ever more sources, the value grows exponentially.

When, for example, cities bring together data from streetlights, parking, and vehicles, then integrated control systems to manage traffic—and slash congestion—become feasible. Or with data integrated from smartphones, smart transit cards, and facial recognition-enabled payment mechanisms, governments can roll out city-wide payment systems.

Increasingly, even point solutions need top-notch data governance and management, as they ingest and generate ever greater amounts of sensitive data. Transit access, library checkouts, and general city security cameras may all soon have facial recognition capabilities, for example.

The golden rule of smart city economics

Smart cities mean sustainable growth.

3 | The foundation for smart city success: Seven layers of data governance and management

4 | Smart Cities—Adept data governance and management is critical to fully realizing the potential4 | The foundation for smart city success: Seven layers of data governance and management

Toronto: Principles and frameworks for smart city data governance

In response to the obstacles (see “Hitting Resistance” above) that one of Toronto’s smart city initiatives has encountered, the city and its public and private partners have established guiding principles and frameworks that should both support this project and other future smart city initiatives.

The principles include continuous engagement with both public sector stakeholders (including elected officials) and the public, while putting privacy issues (and privacy regulations) front and center. Frameworks include a Digital Strategy Advisory Board made up of technology CEOs, professors of information and privacy law, and subject matter experts to study who should control and access data. A Smart Cities Panel is engaging citizens in a city-wide community consultation process. Multiple stakeholder groups are also collaborating on a series of concrete data collection, data governance, data storage, and IP monetization plans.

Hitting resistanceAs forward-looking cities are rolling out smart initiatives, some projects are hitting resistance—due to a failure to effectively plan for data governance and management challenges.

Beyond the US cities banning4 facial recognition technology and the sale5 of location data over privacy concerns, and the pushback in India6 and Africa7 over facial recognition technology, consider a controversy in Toronto. There, a company has an innovative plan8 to turn part of the city waterfront into a future-ready neighborhood, loaded with smart city tech: a smart power grid with thermal energy; smart

containers and underground tunnels for freight; dynamic streets; high tech water and waste management; and more—all working together.

However, while this digital-age plan clearly has its proponents, it has also been met with public opposition surrounding citizen data collection and data security. Top-notch data governance and management is not just a good idea for smart city projects. It may be the critical difference between an initiative that wins stakeholder support, and one that becomes mired in delays and controversy.

5 | The foundation for smart city success: Seven layers of data governance and management

Based on our work with smart cities all over the world, with companies leading in GDPR and CCPA compliance, and with private and public entities deploying cutting-edge cybersecurity and privacy, we have identified a seven layer model through which smart cities—and the companies that help build and operate them—can achieve the data governance and management that they need.

The model begins with preparing for the data categories of the future. It then creates tools and processes for informed consent, for secure and efficient collection, anonymization, and storage, and for secure and tiered access. It finally offers a platform for rapid, innovative monetization.

Build it: the seven layers of data governance and management

1. Data categories

4. Anonymization

3. Collection

2. Consent

5. Storage

7. Monetization

6. Access

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6 | The foundation for smart city success: Seven layers of data governance and management

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.

Dubai, for example, has created a system that categorizes data based on its likely benefits and its readiness for open publication or sharing on the city’s data platform. Copenhagen’s City Data Exchange pilot (see page 11) categorized data based on when it was collected (real-time or historical; frequency of updates); where it was collected (public space or private building; street location, postal code, and which 100 X 100 meter square on the map); and how it was delivered (raw data, excel files, APIs, GIS maps, or dashboards.)

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.

Barcelona, for example, is piloting a dashboard that enables city residents to set permission for who has access to their data, for how long, and for what purpose. This dashboard encourages citizens to share their data by using it to support better neighborhood services, as well as greater participation in city budgeting and other political processes. It also offers residents a visualization tool that blends resident-sourced data with data from other sources, such as administrative open data.

1 Data

categories

2 Consent

7 | The foundation for smart city success: Seven layers of data governance and management

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.

A good example of such a system is in Cape Town, which has deployed a pilot to make better use of fixed asset data that the city collects. The project traces asset data back to source systems, assesses factors that impact data quality, cleanses the data, and applies algorithms to support better investment and maintenance decisions.

3 Collection

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.

Anonymization by design is a necessity, because layering it on later can be so challenging. One study9 showed, for example, that assessing supposedly de-identified mobile data at any given four points in space and time is enough to uniquely identify 95% of individuals.

4 Anonymization

8 | The foundation for smart city success: Seven layers of data governance and management

Smart city stakeholders must also prepare for data governance in the age of artificial intelligence (AI), whose ability to find meaningful patterns—and identify personally sensitive information—in huge amounts of apparently unrelated data raises new privacy challenges.

To ensure that data management is avoiding these privacy pitfalls, many entities are including a third-party audit of their anonymization tools and procedures as a standard best practice.

Public and private smart city stakeholders alike need cost-effective, scalable and safe data storage. Whether or not they depend on cloud-based 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.

5 Storage

8 | The foundation for smart city success: Seven layers of data governance and management

9 | Smart Cities—Adept data governance and management is critical to fully realizing the potential9 | The foundation for smart city success: Seven layers of data governance and management

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.

Dubai has deployed an open platform for public data, Dubai Pulse, to support its broader Dubai Smart City initiative. Dubai Pulse offers private and public sector entities centralized data access which supports Platform as a Service (PaaS), Data as a Service (DaaS), and Infrastructure as a Service (IaaS) offerings. It, therefore, serves as a digital innovation platform for entrepreneurs, the broader private sector, and government agencies, as part of the government’s ambition of making Dubai “the smartest and happiest city on earth.” The system includes datasets on business performance, employment, the economy, financial markets, the environment, healthcare, housing, transport and traffic, utilities, land, and water.

6 Access

Hong Kong opens up access to government data

The government of Hong Kong, as part of its effort to encourage smart city initiatives, has established a new open-data policy and a Public Sector Information10 (PSI) Portal. Government agencies must now release their data for free public use on the PSI Portal, unless critical considerations (such as privacy) forbid it. Through annual open data plans, the government will tell the public which datasets will be released on the PSI Portal in the next three years and which have already been published.

To enhance this data’s usability, the government is standardizing formats; labeling datasets; making it free to use, redistribute and modify the data; creating public APIs for direct, automatic access; establishing feedback mechanisms; and continuously uploading and updating data.

10 | The foundation for smart city success: Seven layers of data governance and management

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7 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.

One emerging model is for city governments (or their contractors) to sell sensor-sourced traffic information, combined with hyper-localized environmental data (such as how foggy or icy a specific road is), to a logistics provider. This provider then offers commercial, real-time route planning to its customers.

Yet creating a market for municipal data is just one type of monetization. Cities can also make taxpayers’ money go further by using data for better spending decisions, as Copenhagen’s City Data Exchange does (see page 11). Monetization can also foster a virtuous cycle of ever-greater participation and monetization, through micropayments that incentivize data sharing and provide value to the sources of that data.

Whichever form monetization takes, it will win the greatest buy-in and be most sustainable if it maintains a citizen-centric lens: always taking into account the value to residents, whether through greater livability, more effective use of their tax payments, or direct monetary returns.

10 | The foundation for smart city success: Seven layers of data governance and management

11 | The foundation for smart city success: Seven layers of data governance and management

Copenhagen: Collecting data with an eye to monetization

Copenhagen’s City Data Exchange (CDE) pilot deployed data collection that integrated preparations for the later layers of smart city data management—including monetization.

To better understand how pedestrians and vehicles move through the city, the city collected multiple categories of data from multiple sources, including cell phones, wireless connections, camera images, traffic sensors, visual surveys, and public transport ticket purchases. To encourage public support, the initiative only accepted data that had been fully anonymized by the data supplier, and it imposed strict and well-publicized limits on how this data will be used.

The initiative then provided tools to visualize the data in ways that support use cases for residents, businesses, and the city government: personalized marketing, better located and planned stores, customized product and service offerings, reduced traffic congestion, more efficient use of public spaces, improved air quality, and a better tourist experience.

With learnings from this now-ended pilot, Copenhagen has joined with 19 other cities in Denmark to create a Regional Data Hub,11 which is fostering smart city initiatives intended to drive sustainable urban growth and help achieve the UN Sustainable Development Goals.

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11 | The foundation for smart city success: Seven layers of data governance and management

12 | The foundation for smart city success: Seven layers of data governance and management

Accelerate constructionThe seven layers of data governance and management are the foundation for successful smart city initiatives. Public and private stakeholders should move quickly to construct them.

Cities must transform roles and mandates to achieve the dual imperatives of turning data into benefits and winning public buy-in. One successful model—which Toronto recently adopted after studying global best practices—is to assign the primary responsibility for protecting citizen rights to a trusted, non-partisan, senior city official. In the case of Toronto, this official is the City Clerk, already responsible for administering city elections and for making information available to the public while protecting privacy. Under Toronto’s

Four key stakeholders required to establish effective data governance and management

CitizensAcademics and external data

governance experts

Industry advisory board

Civic authorities

Effective data governance

and management

model, as its Chief Technology Officer (CTO) looks at how best to turn data into benefits, he or she must work closely with the City Clerk on the city’s data governance strategy to ensure that the public’s privacy and other rights are protected—and that the public knows it.

Success also will require engagement with local or federal privacy commissioners, since coordination among the different levels of government is often a top smart city challenge. Equally critical is rigorous public consultation with residents (through digital and physical open houses and town hall meetings), with smart infrastructure providers (through an industry advisory board), and with external data governance and privacy experts.

13 | The foundation for smart city success: Seven layers of data governance and management

Commercial smart city providers, for their part, should build the seven-layer model into their product and service portfolio—embedding, for example, cutting-edge encryption and anonymization techniques and multi-tier access controls. Private-sector companies will need their chief information and technology officers to work closely with the business, bringing together value creators and value protectors to jointly create a trusted data strategy. They will also need to bring in representatives from supply chains as well as privacy and security experts, to measure and mitigate third-party data risk.

To meet the demands of smart city data governance and management, many public and private entities will have to rebuild their technology architecture. Narrow-band wireless networks, for example, often do not offer sufficient bandwidth, at an acceptable cost, to support the massive data gathering, distribution, and encryption that smart city data management demands.

Above all, public and private entities must design a flexible, scalable data governance and management model—one that will serve smart city needs not just now, but also far into the future.

Win the futureCities should move quickly. How smart they become, and how fast they become it, may determine whether or not they are winners in the global economy of the very near future.

Will a city offer smart infrastructure that generates revenue to support top-notch services, all with low taxes and a light environmental footprint? If yes, then it will likely attract growing and innovative businesses, along with top talent. That will set the city on a path for sustainable growth, aligned with the UN Sustainable Development Goals—and the businesses that seize a share of that growth today will keep riding that growth tomorrow.

But if flawed data governance and management cripples smart city initiatives, cities can face declining services and a falling revenue base, while residents and businesses alike flee for cities with a better quality of life and a greater ease of doing business. Companies whose smart infrastructure and service offerings contain weak data governance can face lawsuits and controversies over their current projects, and increasingly be out of the running for future ones.

Smart cities offer a bright future for residents, businesses, and government leaders alike. To play a leading role in that future, city governments and commercial smart city providers should work quickly now to build its foundation: trusted data governance and management, based on seven layers of best practices.

Smart cities are powered by public and private stakeholders working together.

14 | The foundation for smart city success: Seven layers of data governance and management

¹ PwC, “Creating the Smart Cities of the Future,” November 2018.² Ministry of Housing and Urban Affairs, Government of India, “DataSmart Cities”.³ Ministry of Housing and Urban Affairs, Government of India, “Open Data Platform:

India Smart Cities”.4 Blake Montgomery, “Facial Recognition Bans: Coming Soon to a City Near You,”

The Daily Beast, July 31, 2019. 5 Shannon Liao, “New York City might ban wireless companies from selling your

location data”, CNN Business, July 24, 2019.6 Rina Chandran, “Facial recognition push at India airports raises privacy

concerns,” Reuters, July 25, 2019.7 “China’s AI Package For Africa Includes Mass Surveillance Technology,” Mind

Matters News, May 31, 2019.8 “Toronto Tomorrow: A new approach for inclusive growth,” Sidewalk Toronto.9 Yves-Alexandre de Montjoye et al., “Unique in the Crowd: The privacy bounds of

human mobility,” Nature, Scientific Reports volume 3, article number: 1376 (2013).10 OGCIO, HKSAR Government, “Over 650 datasets to be released in first year of

government open data plans,” Jan 3, 2019. 11 Den Regionale Data Hub, https://denregionaledatahub.dk/en/, 2019.

Endnotes

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© 2019 PricewaterhouseCoopers LLP, a Delaware limited liability partnership. All rights reserved. This content is for general information purposes only and should not be used as a substitute for consultation with professional advisors. 647639-2020 VC

Greg ChiassonPrincipal, Capital Projects & InfrastructureTechnology, Media and Telecommunications LeaderChicago, ILUnited States+1 (847) 343-7076greg.chiasson@pwc.com

Michelle HollandDirector, Consulting & DealsToronto, ONCanada+1 (647) 299-1801holland.michelle@pwc.com

NSN MurtyPartner & Leader, Smart Cities Gurgaon, HaryanaIndia+91 (124) 626 6718nsn.murty@pwc.com

Hazem GalalPartner, Cities & Local Government Global LeaderDubaiUnited Arab Emirates+971 (4) 3043393hazem.galal@pwc.com

Joseph CarrozziPartner, Cities LeaderSydney, New South WalesAustralia+61 (0) 411 853 100joseph.carrozzi@pwc.com

Albert WongPartner, Public Sector ConsultingHong KongChina+(852) 2289-1807albert.wong@hk.pwc.com

Borries Hauke-Thiemian Partner, Government & Public Sector Hamburg Germany +(49) 175-951 5621 borries.hauke-thiemian@pwc.com

Abhishek DubeyAssociate Director, Government and Public SectorGurgaon, HaryanaIndia+91 (124) 626 6718d.abhishek@in.pwc.com

Jon WilliamsPartner, African Cities & Urbanisation LeaderCape Town, Western CapeSouth Africa+(27) 71 382 1073 jon.x.williams@pwc.com

Yassine DiniaSenior Manager, Government & Public Sector ConsultingDubaiUnited Arab Emirates +971 (0) 4 304 3221yassine.dinia@pwc.com

Gary SharkeyGlobal Sustainability and Smart CitiesLondon, EnglandUnited Kingdom+44 (0) 20 7213 4658gary.sharkey@pwc.com

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