Most people in the construction industry will say that on any given work site, there is always an amount of waste that could have been avoided. Whether it be poorly handled materials, re-work, weather damage, or working from out of date documents, there are plenty of ways that humans can make a construction site more inefficient and more costly than it needs to be.
This is not a new problem, nor is it a secret, as there have been many studies over recent decades that come to this same conclusion. Infamously, many studies seem to agree that as much as 30 percent of all construction effort can be caused by rework, which always leads to waste that simply would not be tolerated in other production industries. Industry insiders might joke that for every four buildings completed, at least one more ends up in the wastebasket. These errors are often blamed on working from the wrong information, or not being aware of information that could have avoided the issue. So the question remains, why after more than 30 years of using digital technology do projects keep experiencing rework and waste?
A recent report from Autodesk/FMI found that, “poor project data and miscommunication on projects is responsible for 52 percent of all rework in construction in the U.S.,” which ultimately leads to a whopping $31.3 billion in rework costs.
This is a lot of wasted time, effort, and money that didn't have to be spent if construction teams either knew or had access to dark data. According to a survey from Splunk, 55 percent of this kind of data is completely out of reach, as limited members of a company have access to it.
It appears that the construction industry does not have a shortage of digital data, but instead an inability to carry out even the most basic dimensional data analysis from dark data. This results in a total loss of the institutional knowledge that could be used to avoid issues and be ensure the same mistakes don't recur again.
What is Dark Data?
According to Gartner, dark data is “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.” This data is important because it helps companies see patterns and relationships that they might miss in the regular course of business, which can lead to insights that help them make better decisions that they could not make in the past.
The healthcare industry is one of several which went through a dark data revolution in the recent past using AI tools to capture, analyze, reuse, and share data to make operations more efficient. A good example of this is how Indiana University Health (IU Health) began to access dark data several years ago with the possibilities of capturing free-form notes that physicians generate during patient consultations to ultimately use them to enhance patient records.
In the construction industry it's still not commonplace to think about these AI tools, with much information sharing still unstructured. Yet, the construction industry is no more complicated or fragmented than the healthcare, finance, and legal industries that are already benefiting from these AI tools, revealing valuable situational context and insights that can dramatically improve outcomes. Early movers in the construction industry might experience huge competitive advantage, so what's really holding us back from doing the same thing?
A Matter of Trust
A relatable example that explains the dark data opportunity is something we all now trust without question – GPS-based mobile apps. For those that are old enough to remember, about 20 years ago if someone was driving to someplace new, they couldn't just pull out their phone or access a built-in dashboard computer for GPS location and traffic updates. Drivers had to have a paper map ready and listen to traffic/weather reports before they left the house or they had to listen while driving.
These were the solutions of the time and of course, they were not dynamic and could not update drivers in real-time while they were driving to their destinations. Now almost everyone uses Waze, Google Maps, or any other navigation app that they trust will get them to their destinations. These apps are full of crowdsourced information that gives drivers real-time weather, traffic updates, and speed traps – all examples of dark data that people didn't have access to decades ago.
People took an initial leap of faith the first time they opened up these apps to access this data. Just like the first-time people put their credit card numbers into an eCommerce site to buy something online, there comes a point where people took that leap and in return got access to valuable information they were previously unaware of.
In construction, this means access to the situational context that has eluded building professionals for years. Situational context that can ensure better decisions are made that reduce waste, save money, and will help projects perform with productivity levels closer to other industries. As the AI 'machines' imply opportunities in real-time for better decisions, trust in their insights will increase just like our favorite GPS app.
The good news is that the next generation of workers has much more trust in AI tools. Many current construction leaders and managers in decision making roles are aging out of the industry fast, taking their expertise and knowledge with them. The new construction workers all have smart phones and have grown up trusting the information and applications on them, but a typical firm's current digital footprint is almost impossible to navigate to find the lessons already learned.
If we can't provide tools that compare favorably to other industries, talent will not want to invest their careers into construction at all. So the incoming wave of AI based solutions are crucial to both attracting and retaining future construction professionals.
Someone Call Security
The natural retort to trusting AI tools in construction is the same for all industries – security. It is true that there have been countless cyberattacks and data breaches across consumer and corporate data tools, but it is also important to consider the risk in the data itself. When dark data is being utilized to minimize waste in construction, what is actually sensitive or confidential?
The reality is not much, as the data reflects day-to-day, real site activities: monitoring the trucks coming and going, tracking inbound concrete deliveries, monitoring heavy equipment on site and materials, labor, and installations. Unlike data breaches that reveal credit card information or social security numbers, the security stakes for leaked, real-time construction data are relatively low. Most of the risk lies in contractual liability which, ironically, could be considerably lowered financially than the gains made from having more contextual information available for the most valuable decisions.
Ultimately the construction industry is at a crossroads, one where its workforce demographics are shifting and AI technology is only becoming more pervasive in people's lives. As costs continue to rise and every decision comes with increasing risks of failure, construction decision makers need to determine what is more important – being safe and doing things the way they have been done (producing wasteful, avoidable measures) or trusting this early generation of AI tools to harvest the valuable dark data that can improve the way they do business.