“Sometimes, Pete, when you talk about your data-driven government, I think of Robert McNamara.”
I just smiled, not sure exactly where to take the conversation from here. Coming from John, or really anyone who looked back on the Vietnam War with anguish, being likened to LBJ’s Secretary of Defense was not exactly a compliment. By all accounts, McNamara had been a brilliant individual, a genius even, his rimless glasses and sharp gaze embodying modern technocracy at its finest. But the outcome of the war—and David Halberstam’s book The Best and the Brightest—made the sum total of his brilliance seem dark and ironic, as he and the other geniuses of the national security establishment led our nation into quagmire and defeat.
I could also see where the comparison was going. Before serving in public office, McNamara had been the CEO of Ford Motor Company, and the use of data and metrics on his watch escalated almost to a kind of fetish. After the Vietnam War collapsed into chaos, historians and journalists inquired into how the most brilliant minds of their generation could have led the country into such a lethal blunder, and the image emerged of McNamara as a data-obsessed manager who missed the forest for the trees. “Statistics and force ratios came pouring out of him like a great uncapped faucet,” Halberstam wrote. Yet, for all the statistical brilliance of McNamara and the rest of President Johnson’s inner circle, all of them were tragically late to the obvious fact that the war was a losing one, keeping America entangled there at a cost of thousands more American lives.
This must have been on Voorde’s mind when he had turned the subject of our conversation to the road surface app that he had heard about. My administration was in the process of creating the first objective asset map of the city, cataloging the quantity and quality of streets, fire hydrants, signs, and anything else in a public right-of-way. This work even included an app, which could be run on an iPhone mounted on the dashboard of a supervisor’s vehicle, to scan the conditions of the road and report cracks, potholes, and other deterioration.
Thinking back to his youth on the street department, John was skeptical. “You have this technology to tell you which streets need repair,” he said. “But if your foreman’s any good, he ought to already know that off the top of his head!”
The technology had other capabilities, and I’m glad we use it—but, admittedly, the councilman had a point. One of the reasons we have qualified, experienced individuals in organizations is to use their intuition and expertise to solve problems. If the foreman of a street crew knows every crack on Lincoln Way West and every pothole on North Shore Drive like the back of his hand, why do I need to spend money on an app to tell us where the problems are?
For all the power that data analysis represents—and I’ve worked to build a reputation for running one of the country’s most data-oriented city administrations—it also has its limitations, and the potential for mischief. You might spend lots of time and resources gathering data that will never be used, or accumulate data that winds up telling you things you already know.
SO HOW DOES A TECH-ORIENTED MAYOR make sure that the data is serving the administration, rather than becoming an end in itself? Put another way, how does a government official interested in data come to be viewed more like Goldsmith or O’Malley, and less like McNamara? Over time, I’ve learned a number of rules that have helped us to make sure the use of data makes sense, and does good.
First, know the difference between reporting an issue and resolving it. In some cases, the two go so closely together that you can lose track of the distinction. For example, when we installed ShotSpotter technology using microphones to acoustically pinpoint gunshots, we were enhancing our ability to deal with gun violence. An officer could be immediately dispatched to the scene of a shooting, be it an outdoor fight or a domestic violence case, whether someone called it in or not. And this, in turn, would help in the long run to deter gun violence. But in other cases, knowing more doesn’t help. At a tech conference, I once saw a pitch from a start-up that would automatically detect patterns of opioid use by scanning for trace amounts in sewage. The technology is brilliant, and may do a great deal of good in some places. But in South Bend, our problem wasn’t knowing how much opioid use was prevalent in this neighborhood compared to that one; it was a lack of mental health and addiction resources to deal with the issue wherever we found it. Financing a project to tell us more about the problem could even come at the expense of treatment options, which are grossly underfunded in our county and state health systems. In cases where we have ample means to fix a problem, then we need only to find it. The rest of the time, reporting an issue is necessary, but not sufficient, for resolving it.
A second rule we learned quickly was to recognize that responsiveness and efficiency are not the same thing; in fact, they can sometimes pull against each other. Consider the example of snowplowing. The most responsive thing to do would be to ensure that anytime someone called about an impassable street, a plow crew was immediately dispatched to take care of that block. It would be an attractive thing to be able to do (think of the political credit)—but it’s also clearly not the most efficient; far better to use a zone system, covering the city as quickly as possible, starting with main roads and then moving to residential streets, with added input from a parametric model that takes temperature and precipitation rates into account. Any other approach would take longer, and ultimately mean less quality of service and/or more cost. Local officials often feel pressure to deal with a squeaky wheel right away, when stepping back and considering a big-picture solution would serve people better.