Mission Control: The Roadmap to Long-Term, Data-Driven Public Infrastructure

Mission Control

The Roadmap to Long-Term, Data-Driven Public Infrastructure

Benjamin Schmidt

copyright © 2022 benjamin schmidt

All rights reserved.

mission control

The Roadmap to Long-Term, Data-Driven Public Infrastructure

isbn

978-1-5445-3070-3 Hardcover

isbn

978-1-5445-3069-7 Paperback

isbn

978-1-5445-3068-0 Ebook

For my family, who have supported me at every stage.

Contents

Introduction

Part I. failure to launch

1. The Limitations of Data

2. Key Mistakes in Mission

3. Focus or Fail

Part II. The Mission Control Rule Book

4. Setting Course

5. Putting Milestones in Place

6. Creating Metrics

Part III. Data-Driven Decisions

7. The M³ Roadmap to Data-Driven Public Infrastructure

8. Data-Driven Decision-making

9. The True Promise of the M³ Roadmap

Conclusion

Acknowledgments

About the Author

Introduction

America does not make bad infrastructure. We simply fail to update it properly.

Despite our reputation for inept infrastructure development, America actually ranks thirteenth in the world for the quality of our roads, waterworks, electricity, and bridges. Perhaps not as high as we might like to be but certainly not as disastrous as we might assume, we have a great base to work from.

Of course we do. We’re great builders in this country. Our problem is, we fail to consistently upgrade the base quality of what we’ve already built.

And this comes from a breakdown in our process.

We split the responsibility for our infrastructure across vast organizations. Highways are often managed by the state, while interstates are managed by states and sometimes funded by the federal government. All other roads are managed at a local level. Most water systems are local to a single or a few cooperating communities. Utilities, by their very nature, are independent organizations. And construction and engineering firms encompass hundreds or even thousands of workers and consultants and to support the vast array of work needed to build and maintain our infrastructure.

It’s very easy for all these massive parts to become gridlocked, even when everyone is on the same page. After all, one of the rare points of consensus across American politics and society—including between Democrats and Republicans, urban and rural citizens, local and federal government, and every utility and road construction company in the land—is that we should update our infrastructure. Thirteenth isn’t good enough, particularly when there is lead in the water and bridges collapsing in the most prosperous country on Earth. In fact, almost everyone agrees on at least some of the proposals to achieve these improvements. You’ll struggle to find anyone who is against proposals for better roads, bridges, rail, water, power, and other civil infrastructure.

Yet even here, despite this broad consensus, the implementation process for infrastructure is full of the same poorly managed, misaligned, and extremely wasteful systems government is infamous for.

Nowhere is this as clear as in the allocation of federal dollars to states for interstate maintenance and management. In order to collect those dollars, states are required to collect massive amounts of data on the quality of every mile of their road network. They drive around in expensive vans equipped with LIDAR technology (basically, radar but with light) that measures the distance and depth of cracks running along the roads.

This is valuable data and well worth collecting. It could be used to update our road infrastructure across the country. The problem is that it never reaches most of the levels of government that could most use it. The data gets reported back to the federal government, who use it in their long-term planning, but it is inaccessible to everyone else—from state to local governments and down to the companies that actually do the maintenance work—until it’s largely out of date.

Why?

It takes months and millions of dollars to collect, compile, and share this data with the federal government. By the time the data is ready to be used, months have passed. By that time, multiple work crews have already performed maintenance, vehicles and weather have further damaged and degraded the structures, and entirely new traffic patterns may have emerged.

In a pattern we will see over and over again in this book, data that should allow for planning, execution, and upgrades instead sits on a hard drive as a massive pile of largely unanalyzed information, begging to be used for more insights but never delivering.

In a state like Pennsylvania, this whole process takes nine months and millions of dollars. Then, when it comes time to make choices about constructing new roads or making repairs on existing roads, the state has to spend additional resources to send someone back out in a truck to check the roads all over again. Because of the cost, Pennsylvania can’t afford to risk one of their few multimillion-dollar sensor vans. So instead, the data that is actually used to make infrastructure decisions at a local level is of much lower quality.

It’s less objective, less precise, and less comprehensive. And at the end of the day, that’s how we determine where to spend infrastructure dollars on our roads.

These frustrating limitations aren’t due to any technological hurdles. The technology exists right now to do this better and more affordably. And the issue is not something fundamental in our policies. Again, almost everyone is behind collecting big data sets to help set massive federal budgets and ensure we are astutely managing the public purse.

So where is this system going wrong? And how do we fix it?

A New Language for Benchmarks

Everyone in government complains about budgets, and I agree, budgets are the enemy—but not for the reason you think so.

Far too often, the only motivator in government is the budget. So long as a department stays within budget, their operation is counted a success. This eliminates any room for ambitious planning or risk taking. There’s no way to measure such efforts, which means success goes unrewarded and failure—going over budget—is heavily punished.

If we want to transform our infrastructure, then, we need to develop a new language that gets us outside pure financial thinking and moves us into a realm in which we can measure progress through concrete benchmarks.

This is where a better use of data really comes in.

In the past, when managing civil infrastructure, local governments used to be forced into using subjective, infrequent, and incomplete data when making decisions. The best hopes for better outcomes were all outside of the administrator’s control: the election of a particularly skilled mayor, the influx of new business and new citizens, or an economic boom that would see a major boost in tax revenue.

As we’ll see throughout this book, far too many government offices still rely on this method.

However, it doesn’t have to be this way. Thanks to advances in innovative new technologies, we can collect and organize data and make better data-driven decisions today, all without overwhelming a local government budget.

To change this dynamic, we must look squarely at how our current data frameworks misuse the technology we have. For too long, data—and the technology that gathers it—has been seen as a luxury, something to attend to when there was room in the budget. We have to change that thinking today.

Instead, we should aim for continuous streams of data that allow us to benchmark how effectively we are advancing toward our goals. Only then can we exit the myopic focus on finance and start aiming for an elite, well-maintained, and ever-evolving level of infrastructure.

A Roadmap to Transform Local Infrastructure

The stakes for upgrading the data behind civil infrastructure could not be higher. In most communities, the local government is never more than a single sewer line break away from bankruptcy. There are fine lines between prosperity and a spiral toward dirt roads, and for a long time, there are few ways to thicken those lines.

Better data policies can move us beyond such turmoil. The problem is that the organization to upgrade the system on a wider scale isn’t in place. Some organizations have no