Under the wrong balance of responsiveness and efficiency, data can actually make us worse at our job. This is one reason I eventually backed off from my enthusiasm for the idea of publicizing a twenty-four-hour pothole guarantee. It seemed at first like a great way to show how responsive the city was to road concerns—and doable, because in peak patching season we already get to most potholes within a day or two of them being called in. But after reviewing the concept with engineers, it became clear to me that if I instructed the staff to make sure every hole got taken care of as soon as we knew about it, I could actually reduce the efficiency of the operation. Crews on the West Side might have to drop what they were doing to go deal with a pothole on the North Side, then go chase another work order downtown, all coming to them in order of appearance. An expensive vehicle and work crew would zigzag through the city according to real-time data on which residents were first to call and complain, with little regard for whether it made more sense to have Harter Heights wait a couple days while we systematically took care of the Keller Park area for the season.
At other times, the reverse is true and responsiveness really is more important than efficiency, as in the case of graffiti. It might seem that the most efficient thing would be to treat graffiti like snow—take whatever resources we have for repainting, and have them work the city, street by street, systematically. But if a stop sign gets tagged with graffiti, leaving it there even for a couple days might motivate someone to tag something else nearby. Whether it’s a gang sign or a cartoon bunny, what shows up on Falcon Street may soon be copied on Walnut, and the longer it’s there, the more likely someone will seek to imitate or outdo it. So clearing it right away is the most important thing, and the team works to fix any reported graffiti almost immediately (except on a dedicated graffiti wall opposite the Emporium Restaurant, where artists are welcome to do whatever they like). The result is that people who might be motivated to deface public property find it’s not worth the effort, and now it is less likely to happen in the first place.
A third lesson on data and efficiency is to be honest at the beginning about whether you are willing to follow the data where it leads. When I asked Eric Horvath, our public works director, to get creative on ways to keep solid waste billing rates under control, his team came back with options from selling ad space on city trash bins to charging customers differently by how much they throw away. The most usable idea involved a technology for partly automated trash trucks that can pick up a bin with a robotic arm, eliminating the need for a human “picker” on solid waste crews. This meant a savings for the city, keeping rates lower—and, we learned, led to lower injury rates as well. But buying the technology was only worth it if we were prepared to eliminate the jobs. It wasn’t an easy thing to do, because our solid waste workers were likable and hardworking. In the end I decided to go ahead, because the city could offer the workers other jobs, provided they earned a commercial driver’s license. Half the affected workers did so, and half left city employment altogether.
But in other cases, we are not prepared to capture an efficiency when we find it. For example, we continue to operate a walk-in center for paying your water bill, even though this can be done online, over the phone, and by mail. Part of me (the consultant part of me, naturally) finds this maddeningly inefficient: Why pay for a brick-and-mortar structure, and staff, at a facility whose work can be done more quickly, efficiently, and easily by other means? But the more I looked into the issue, the clearer it became that low-income residents who did not have bank accounts relied on the facility so they could pay in cash. The long-term solution would be to help them to get banked, but the reality is that this will not happen for some of our residents. The right thing to do here, it seems, is to tolerate an inefficiency for now, even though the data tells us how it could be eliminated.
A fourth data lesson came from the ShotSpotter experience: follow the data where it leads, and recognize that it could show you the answers to questions you never even asked. When we adopted the technology, the obvious appeal was that police could be dispatched immediately to the site of a shooting, without having to rely on someone quickly calling it in to 911. We knew there would be a tactical advantage, but only slowly did we realize this would also be a powerful tool for both measuring and changing the relationship between community members and the police department.
As our police chief at the time, Ron Teachman, explained, “Law enforcement projects an air of omniscience. If residents hear a gunshot and don’t see an officer coming to the scene, they don’t think it’s because we don’t know about it. They assume we know about it, and that we’re not there because we don’t care.” With the new technology, officers appeared on the scene of shootings we simply didn’t know about before. And gains in police legitimacy could be achieved by using the community policing method of “knock and talk” in concert with the technology. When the system detected gunshots in a residential area, officers would work that block the next day, letting residents know they were concerned and leaving door hangers for those who were not there, explaining why they had visited and how to follow up.
Soon the ShotSpotter data became a measure for tracking something completely different from gunshot rates: perceived police legitimacy. Since we knew from the sensors how many gunshots were fired in the coverage area, and we also knew how many times someone in the same area called 911 for shots fired, we could now tell what proportion of the time people heard gunshots but didn’t bother letting us know. For the first time, we had an index of how many people thought it was worthwhile to call the police about gunfire near them, a hard number to help us measure something very difficult to quantify: trust in the police department.
Initially, we had assumed that a small fraction of gunshots went unreported, perhaps 20 percent. Instead, we learned that, shockingly, the reverse was the case. Now, we watch the ratio closely; I can log on to a law enforcement dashboard that will tell me, on a monthly basis, what proportion of shots are being called in. It’s only one measure, and an imperfect one, but I use it to help get a sense of how much residents think it’s worthwhile to call the police.
This leads to another concern when it comes to data-driven government, or government in general: the confusion of technical problems with moral ones. In many ways, it is psychologically easier to deal with technical problems, ones with right and wrong answers. In these cases—how to make pothole patching more efficient, or get more children tested for lead exposure—it is clear that if we find a more efficient way to proceed, by definition it should be done. But in many ways, political leadership isn’t required for these technical gains, other than to give a green light to staff who identify ways to make them. Elected officials earn our keep by settling moral questions, ones where there is no way to make someone better off without making someone else worse off.
Even the most ground-level decisions can have this character, as when we switched the trucks for trash pickup. Not only did it mean some city workers losing a job, it also meant that we had to get residents to haul their trash bins to the front curb once a week, since the newer trucks couldn’t operate well in narrow alleys. Moving the bins is a pain, and there’s no getting around this when interacting with a resident who would rather it all stayed in the alleys. We were presented with a trade-off: keep it in the alleys and let trash pickup be more expensive for everyone, or move it to the front and make it more inconvenient for some. No math could solve this problem or present an obvious right answer; we just had to make a call, and then be willing to explain it to those affected.