Shortest Way Home: One Mayor’s Challenge and a Model for America’s Future
The easy lesson to draw from this is that you must innovate to survive. But you could find a more nuanced moral of the story: that keeping up doesn’t always mean making something completely new. To survive, South Bend Watch wouldn’t have needed to start making radios or computers. They just needed to adapt a good thing they already had, and refine their business. If they, and Studebaker, and some other companies, had managed to do this, I might have grown up in a different South Bend.
Indeed, even the most orthodox economic theories showed that market failures were all but guaranteed to occur in situations, like health care and education delivery, where a seller has power over a buyer, or a buyer is seeking a service that can’t easily be assigned a dollar value, or the seller and the buyer have different levels of information about the product. The two years
LET ME ASK YOU, for a moment, to imagine a list of the most interesting subjects in the world, ranked from one to infinity. The list is different for each of us. But some topics are fairly high on the list for almost everyone: topics such as television, religion, warfare, food, sports, space travel, the presidency, and sex. Now ask yourself where, on that list, you would put the subject on which I became an expert during the winter of 2010: North American grocery pricing.
Against all my expectations, it was fascinating. I wasn’t just learning about the retail business or about computer programs—I was also learning about the nature of data. By manipulating millions of data points, I could weave stories about possible futures, and gather insights on which ideas were good or bad. I could simulate millions of shoppers going up and down the aisles of thousands of stores, and in my mind I pictured their habits shifting as a well-placed price cut subtly changed their perceptions of our client as a better place to shop.
For purpose-driven people, this is the conundrum of client-service work: to perform at your best, you must learn how to care about something because you are hired to do so. For some, this is not a problem at all. A great lawyer or consultant can identify so closely with the client, or so strongly desire to be good at the job, or be so well compensated, that her purposes and interests and those of the client become one. But for others, work can only be meaningful if its fundamental purpose is in things that would matter even if no one would pay you to care about them. No matter how much I liked my clients and my colleagues, delivering for them could not furnish that deep level of purpose that I craved.
The reason to run—the ideal reason to seek any job—was clear: the city’s needs matched what I had to offer.
By the thousandth day, our community had addressed not just a thousand but over eleven hundred homes, and was finally poised to pay more attention to preventing future abandonment than to dealing with the backlog.
In some ways, it was a classic example of data-driven management paying off. But the most important impact of the effort was unquantifiable. Hitting such an ambitious goal made it easier for residents to believe we could do very difficult things as a city, at a time when civic confidence
Inspired by the “CitiStat” model that brought modern performance management to Baltimore under Mayor Martin O’Malley and became a template for data-driven local government everywhere, SBStat is a sequence of intensive meetings where we identify issues and vet new ideas, with rigorous analysis by city staff as the basis for our conversations.
ANALYTICAL WORK SESSIONS like this meeting aren’t just the result of a mayor indulging his inner geek, though I admittedly enjoy them for this reason. More importantly, they are the backbone of our effort to make the city’s management more rigorous, efficient, and fact-driven. When I took office, it was clear that too many decisions were still made based on gut feel, rather than data—and some operations never got rigorously analyzed at all.
When the 311 center opened, a year after I took office, we gained something even more valuable than a new mechanism for customer service; for the first time, South Bend had a central, constantly updated data set on what people were calling about. Using the data, the city was able to make countless operational improvements, from cutting the time it took to get a large item picked up by our trash crews, to simplifying the way residents paid their water bills.
ARRIVING IN OFFICE, ESPECIALLY with my consulting background, I took it as a given that more data was a good thing—the more objective and analytically driven our work, the better. There was an emerging bipartisan consensus about this style of government, and I bought in. Just as Martin O’Malley had gained a reputation for excellent work modernizing Baltimore’s government with improvements on everything from overtime costs to pothole patching, Republican Mayor Steve Goldsmith of Indianapolis racked up a number of wins from increased child support collection rates to the reduction of sixty-eight thousand pieces of unnecessary paperwork per year.
“Sometimes, Pete, when you talk about your data-driven government, I think of Robert McNamara.”
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.
But after taking office, just as quickly as I learned the power of data, I also learned to be mindful of its limitations, and aware of the problems it will not solve. And I learned to maintain some level of respect for the role of intuition.
There is great power in human pattern recognition, which actually resembles big data analytics in its most important characteristic: the ability to know things without knowing exactly how we know them.
Often, discussions of performance management gloss over this crucial difference between data analysis in general, and “big data” used with artificial intelligence. Using data in general is nothing new; it is simply the application of factual knowledge to make decisions. As an approach to government, it came as naturally to Alexander of Macedonia as it did to Robert McNamara. For the purposes of using data, the only thing to change with the introduction of computers is that we can gather and apply it more quickly and precisely. “Big data” is different. It has the potential to change government, along with the rest of our society and economy, in categorically different ways than the use of data in general. Not everyone may share my definition, but to me the difference is this: Using data means gathering information, understanding it, and applying it. Using big data means analyzing information to find and apply patterns so complex that we may never grasp them.4
A person aided by data can make smarter and fairer decisions, but only a person can sense when an unexplainable factor ought to come into play—when, for lack of a better expression, “something is up.” And that, as John Voorde might remind me, has been the job of elected officials all along.
Tip O’Neill’s dictum was right: all politics is local. Especially national politics.
As the story gained increasing attention, including a feature on 60 Minutes, many responded judgmentally toward anyone, especially Helen, who could vote for Trump and then be surprised by this sort of outcome. But to do so is to assume that voting is about ideology and policy analysis, rather than identity and environment. For a hardworking and devoted woman like Helen with a small family business in a conservative Indiana community, most of the people she dealt with—neighbors, customers, and acquaintances—were people for whom voting Republican was simply a matter of course. If she was also a consumer of conservative news on television and social media, more liberal messages might never have reached her in the first place. We should not be so surprised that she was so surprised.
Looking back, I see no good reason that can be confected for why one person and not another should die at random on a routine mission. For a mind that can’t come to rest around that question, the only way out is to construct a reason going forward. You resolve to build a life that is somehow worthy of emerging on the better side of luck’s absurd equations, because you know that by definition your luck is something you don’t deserve. Nothing that had happened during the deployment would justify the pattern by which I returned safely and some of the others did not, but I had the rest of my life to try to repay whatever debt I had incurred by coming back in one piece. It all might sound superstitious, but the search for justification was an inescapable imperative for me, and another element of propulsion for my work at home. Not that it would really be possible to ever feel like I had settled this account. But it was clear that I would have to work harder than ever to make myself useful, after these reminders of the precariousness of existence not just in war zones but in general. If this loss had happened while I was still deployed, it might have propelled me to try even harder, perhaps dangerously so, to make gains for my vanishing unit. But my war was over. If I wanted somehow to earn the luck that had brought me home safe from Afghanistan, I would have to do it from home, in South Bend.
Before going overseas, I had felt comfortable being more than one person, as we all sometimes must, according to the roles we are called to play. I knew how to toggle between mayor mode, officer mode, friend mode, and so on. But something about exposure to danger impresses upon you that a life is not only fragile but single, with one beginning and one end. It heightens the desire for your life to make sense as a whole, not just from certain angles. And with this comes renewed pressure for internal contradictions to be resolved, one way or another.
It is easier to be cruel, or unfair, to people in groups and in the abstract; harder to do so toward a specific person in your midst, especially if you know them already.
sense of loss inclines us, in vulnerable moments, to view the future with an expectation of harm. But when this happens, we miss the power of a well-envisioned future to inspire us toward greatness. Here, someone will say I should be careful, as a progressive, to go around speaking of greatness. Especially in this moment, when “make great” is the mantra of a backward populist movement, the word seems associated with the worst in our politics, its champions consumed by a kind of chest-thumping that seeks to drown out any voice that would point out the prejudice and inequality we still must overcome.