The term “Smart City” was popularized through a 2011 IBM marketing campaign as the company was making a bid for government infrastructure dollars. In part due to its origin, the smart city has remained an abstract concept — referring to everything from self-driving cars, to automated streetlamps, to the Internet of Things. While the vagueness of “smart” is part of the appeal, there is no one technology that makes a city smart. The utility implied by the word “smart” comes from connected resources and the ability to understand and leverage data. The truly smart, albeit, less sexy, goal that cities should be focused on is becoming data-driven.

A data-driven city has the ability to adapt its services and operations based on analytics. Due to a wide array of reasons, most city governments are not optimizing currently available information. However, there are clear steps that cities can take to ensure their policies and impacts are data-driven, starting with the creation of Offices of Data Innovation (ODIs). An ODI will help fellow departments understand, leverage, and connect their data to better achieve their missions. This core investment in data governance will allow cities to address the principal barriers to data-driven action, namely: poor data accessibility across departments, the high price tag of outsourcing technical expertise, and the lack of contextual knowledge present during conventional data projects.

The creation of ODIs would alleviate the widespread issue of data silos within city governments. Many of these silos are perpetuated by a lack of desire to share information with other departments and a possessiveness over data brought about by existing norms. This cultural aversion to data is explored in A New City O/S:  Information is often viewed as a gotcha game, or to identify problems and... that, in turn, makes it harder to convince all the players to use information to support positive steps like making programs work better, because many government insiders look on information with profound suspicion.[1] 

The creation of an ODI is a critical step in facilitating a cultural shift away from this reluctance to information sharing. The next step towards a data-driven city is sharing data in a consistent and scalable way. New York City’s Mayor's Office of Data Analytics (MODA) is an example of an existing ODI with an efficient hub system. It “does not store any data itself but provides a view directly to agency data systems, allowing users to search for and share data with each other through a user interface.”[2] This allows for automatic sharing instead of requiring agencies to actively send data into a central location, reducing the agency-level effort. It is easy to see the benefits of having seamless access to data from different agencies – from increased emergency reaction speeds, to the distribution of one city service based on gaps in another. This agility and insight into cross-agency performance is foundational for any city striving to improve its services.

This new ODI and data-sharing strategy needs to be overseen by people who understand data from both a technical and social lens. The more pronounced obstacle to data usage is the absence of technical and operational data expertise within city governments, and the subsequent need to outsource advanced data analytics projects to private companies. An internal ODI would bring this expertise in-house, removing the expense of hiring private companies and making data-driven projects more financially feasible. All too often, the high cost of technical projects pits their potential long-term benefits against current budgetary constraints. The ODI would serve as a shared technical resource across agencies that can be utilized without allocating a sizable portion of the individual agency’s budget to a proposed analysis project. Situating the ODI as an independent agency serving all departments would also insulate it from political pressures and vulnerability.
 
The less clear-cut challenge with civic data use is how to appropriately contextualize quantitative information within a city. One critique of “smart” city discourse is that it represents “technocratic fiction: one where data and software seem to suffice and where, as a consequence, knowledge, interpretation and specific thematic expertise appear as superfluous.”[3] There should not be a tradeoff between data expertise and thematic expertise. An effective ODI can address these issues by elevating relevant agency knowledge and hiring for the elusive role of someone who understands the inherent bias baked into any data source. An example of a potential project team structure is outlined in Table 1.

In addition to incorporating subject area experts into data-driven projects, cities need to develop training programs designed to increase the digital literacy of all employees. This is a necessary step in meaningfully empowering subject matter experts to leverage their data in the most effective way possible. Widespread training will not only allow agency employees to better understand and use their data, but also equip them to question why and how their data came to be and how it can be thought of critically as the agencies progress. The most successful data-driven cities will be those that disseminate information in conjunction with the ability to analyze it.

This brings us to the final, but perhaps most critical piece of the data-driven puzzle: funding. There is a reason why New York City is one of the only U.S. cities with a formalized data office. The funding and political will needed to create a separate agency for the purpose of optimizing public data was generated through an executive order by a tech-driven mayor and his wealthy tax base.[4] The political and financial capital necessary for the creation of this kind of agency are, understandably, the biggest barriers to ODIs outside of the New York context.

This kind of barrier was apparent in Mayor Lori Lightfoot’s decision to merge Chicago’s Department of Innovation and Technology with Fleet and Facility Management in late 2019.[5] The same team is now managing properties and city vehicles, as well as physical IT infrastructure. It effectively removes the innovation mandate of the department by narrowing its role. This merging decision saved $1 million in the context of an $838 million budget shortfall.[6] In the long term, it will hinder Chicago’s ability to optimize the delivery of services to residents and use its budget more efficiently.

Most cities, large and small, encounter this kind of financial constraint coupled with the competition between long-term and short-term goals. It is a difficult decision to prioritize an investment in data analytics, with the promise of funds to be saved in the future over other more pressing needs within the city, such as direct poverty reduction measures and food security improvements.

That said, the need to effectively understand and leverage data is here to stay. Cities need to ensure they have the governance structures in place to not only wrangle available information but to leverage it in equitable and effective ways. The establishment of ODIs will begin to reframe data governance as the infrastructure investment it truly is, allowing cities to constructively engage with the new digital landscape.