The latest report card from the American Society of Civil Engineers gives infrastructure in the United States a grade of D+. They estimate an additional $200 billion is needed per year, on top of existing public spending, to achieve and maintain a grade of B. This begs the question: if infrastructure money is so tight, where do we put our money first to reduce the risk of failing infrastructure? Or, to state it another way, what is more important for a community or region to repair or upgrade: a public hospital or a highway bridge?
This sort of question comes up often in public debates and elections. It’s a hard question to answer because you are choosing between two critical infrastructures.
There are always plenty of opinions. But opinions are just that, opinions. If you’re lucky, there might be in-depth research studies supporting those opinions. Comparing transportation studies and public health studies is like comparing apples and tractors. Many public sector studies are huge tomes written by experts and rarely digestible by the average voter. Studies can also be hard to discover and access. Because they take a long time to research and compile, public sector studies may not be timely.
Leading electricity, natural gas and water utilities use advanced analytics to assess their infrastructure and assign standardized risk scores across different asset classes to arrive at a prioritized list of projects to address. Knowing which risk is more acute and/or represents higher consequences switches infrastructure debates, in a utility board room or city council chambers, from opinion-driven to data-driven.
Risk is defined as the probability of failure and the severity of consequences if failure occurs. For infrastructure, failure can be defined as something other than complete collapse. Infrastructure is intended to serve the public, so failure can be defined in terms of service.
For instance, failure for both a hospital and a bridge might be the inability to serve at least 75 percent of the normal volume of patients or vehicles in any 30-day period. That might the level at which the respective medicine and transportation systems for the surrounding region can’t absorb the traffic being redirected from the failed asset.
The failure of public infrastructure carries with it multiple consequences in terms of emergency response, public health and safety, economic impact, taxation, policy and more. These consequences can be measured or estimated, weighted, scored and added to the analytics process.
By tying dollar amounts to public projects that also carry a defined amount of risk reduction, politicians, administrators, community advocates and voters gain insight into the trade-offs inherent in any community budgeting decision. Advocates for less spending would know the level of risk that they are accepting; advocates for less risk would know the spending their position requires.
This approach is common in the utility sector. Regulated utilities must make a case for the rates that they charge and the profits that they can earn. Utilities that use advanced analytics to show regulators the relationship between budget requests, spending plans and risk reduction have an easier time arguing, and winning, their cases.
Utilities have multiple systems across their service territories for measuring and gathering data on the state of their electricity, gas or water delivery infrastructure. Other public infrastructure, like hospitals and bridges, don’t necessarily enjoy these same capabilities today. By 2020, however, Gartner predicts that the Internet of Things will contain 30 billion devices. It is likely that at least some of those devices will gather and report useful data on the state of public infrastructure.
Analytics will not eliminate public debates and elections, nor should it. Using analytics could get everyone in the community on the same page about the status of current infrastructure, the potential need for investments, and the impact those investments should have. Public infrastructure has a direct impact on our quality of life. It deserves the same level of analytics being implemented in other sectors of the economy.
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