One of the smart city’s most alluring features is its promise of innovation: It uses cutting-edge technology to transform municipal operations. Like efficiency, innovation possesses the nebulous appeal of being both neutral and optimal, which is difficult to oppose. After all, who would want her city to stagnate rather than innovate?

There is little doubt that cities can benefit from new ideas, policies, practices, and tools that make data easier to interpret and access. Where smart-city proponents such as Alphabet’s Sidewalk Labs go astray, however, is when they equate innovation with technology.

The perspective is deeply misguided. Technological innovation in cities is primarily a matter not of adopting new technology but of deploying technology in conjunction with nontechnical change and expertise. This requires data scientists to reach out beyond the realm of databases and analytics to access as much contextual knowledge as possible.

“You come in with your fancy machine learning algorithm in your pocket,” says Amen Ra Mashariki, former chief analytics officer at the Mayor’s Office of Data Analytics in New York City, “but what’s always going to be your ace in the hole is the knowledge of the people who actually do the real work.” Breaking down departmental silos, creating new practices to manage data repositories, and training staff in new skills—not finding the optimal machine learning algorithm—is the real pressing task at hand in cities such as New York, Seattle, and Boston.

Seattle, where the Human Services Department (HSD) improved local homeless services by restructuring contracts with local service providers and aligning other agencies behind a common purpose, illustrates the benefits of recognizing that innovation can mean more than just “use new technology.” Municipal governments operate within a remarkably complex structure—their powers and capabilities are limited, and they must engage with numerous other institutions. Yet no smart-city technologies are designed with any such structure in mind; a focus solely on technology would have left HSD powerless to improve homeless services.

Technology also cannot provide answers—or even questions—on its own: Cities must first determine what to prioritize (a clearly political task) and then deploy data and algorithms to assess and improve their performance. Wired famously promised that “the data deluge makes the scientific method obsolete” and represents “the end of theory,” but in today’s age of seemingly endless data, theory matters more than ever. In the past, when they collected minimal data and had little capacity for analytics, cities had few choices about how to utilize data. Now, however, cities collect extensive data and can deploy cutting-edge analytics to make sense of it all. The magnitude of possible analyses and applications facing them is overwhelming. Without a thorough grounding in urban policy and program evaluation, cities will be bogged down by asking the wrong questions and chasing the wrong answers.

“The key barrier to data science is good questions,” observes Joy Bonaguro, former chief data officer of the City of San Francisco. Improving operations with data often hinges not on developing a fancy algorithm but on thoughtfully implementing an algorithm to serve the precise needs of municipal staff. Bonaguro therefore seeks far more than technical expertise when building her team. “When we hire data scientists,” she explains, “I really want someone who does not want to just be a machine learning jockey. We need someone who is comfortable and happy to use a range of techniques. A lot of our problems aren’t machine learning problems.”

Because gaps and disparities in data are common in city governments, research is vital to successfully implement data science in municipal settings, says Chicago’s former chief data officer Tom Schenk. “It’s super easy to miss what departments don’t think is important because it’s very banal to their process, but is key for our statistical modeling. The stats aren’t hard for us. We spend most of our time talking to the client, trying to understand everything so we can apply statistics.”

The Mayor’s Office of New Urban Mechanics (MONUM) in Boston is developing ways to bridge the disconnect between the expectations generated by technology and the day-to-day realities of municipal governance. “For many years now, we’ve been talking about the need to become data-driven, and that is clearly one important direction that we need to explore further,” says MONUM cofounder and cochair Nigel Jacob. “But there’s a step beyond that. We need to make the transition to being science-driven in how we think about the policies that we’re deploying and the way that we’re developing strategic visions. It’s not enough to be data mining to look for patterns—we need to understand root causes of issues and develop policies to address these issues.”

In April 2018, MONUM released a “Civic Research Agenda” comprising 254 questions, the answers to which will inform the city’s efforts to improve life for all Bostonians. These questions range from big (“How can we gain a holistic understanding of the kind of future people want for Boston?”) to small (“What can be done to lower the cost of construction?”), from technological (“How does technology play a role in perpetuating or addressing long-standing inequities across our city?”) to non-technological (“What is at the root of community opposition to new housing?”).

All of this is necessary, says Kim Lucas, MONUM’s civic research director, to ensure that municipal projects are based on evidence and demonstrated civic needs. She relies on research to “find the right tool to answer the right questions. If you’re not asking the right question in the first place, how do you know that technology is the right approach? It may or may not be.”

This is the core message of my forthcoming book, The Smart Enough City: Cities are not technology problems, and technology cannot solve many of today’s most pressing urban challenges. Cities don’t need fancy new technology—they need us to ask the right questions, understand the issues that residents face, and think creatively about how to address those problems. Sometimes technology can aid these efforts, but technology cannot provide solutions on its own.

It’s always difficult to resist the allure of tech goggles—the perspective that every ailment of urban life is a technology problem that only technology can solve. But if we fail in this task we will end up building cities that are superficially smart but under the surface are rife with injustice and inequity. A look at what actually enables and sustains cities reveals that the most important innovations occur not in the cloud above but on the ground under our feet.

This edited text was excerpted from The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future, published by the MIT Press.

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