In this special feature, Richard Harpham, Vice President of Slate technologies, outlines the digital “dark data” real estate and construction companies need to be aware of and how AI can help reveal it. Slate is an AI platform that maximizes efficiency and costs for the construction industry. Prior to Slate, Richard led software commercialization efforts for construction startup Katerra, which was a technology-focused offsite construction company.
There is a chimerical problem at play in the construction industry, namely that while techniques, tools and machinery have changed, the way the industry operates has remained largely unchanged for over 100 years. This has had an effect on the industry’s bottom line and according to McKinsey, “Globally, labor productivity growth in the construction industry has averaged 1% per year over the past two decades, compared to 2.8% for the entire global economy and 3.6% for manufacturing.” Indeed, the way managers deal with day-to-day issues that require problem solving remains unchanged, namely the use of written notes, Excel spreadsheets and instincts for real-time decision making.
“Keeping things the old fashioned way” might be the most comfortable way to do business, but it leads to a lot of waste and costly mistakes, which slows the growth of the industry. Fortunately, the answer lurks in plain sight in the digitally created dark data in almost every job in the construction industry.
So what is Dark Data? Gartner defines dark data as information assets that organizations collect, process, and store in the course of regular business activities, but do not typically use them for other purposes. It is estimated that companies use or exploit only about 1% of the dark data they own and store.
In construction, the problem with obscure data is that the industry does not access it and does not know what to look for when it accesses it. According to an IMF white paper, 95% of all data captured in the construction and engineering industry goes unused.
So what arcane data should construction site managers and executives be looking for? Typical examples of such dark data include:
- Common data points: This includes emails, spreadsheets, PowerPoint and Word files, text on drawings, voicemails on phones, and meeting minutes.
There are, however, more specific examples of building dark data, including:
- Time-critical information: This is data collected based on things experienced in a similar past project. Some large companies are having the exact same downtime issues caused by not ordering certain materials early enough to arrive when expensive labor is ready to install them, such as glulam or laminate -glued, custom carpentry, interior lighting, custom doors. , or windows, etc.
- Lessons learned reports: This is a typical and simple example found in almost all construction companies, usually created at the end of projects lasting several months or years. They are diligently prepared and stored, but never crossed over or presented to decision makers at the right time during future projects.
- Site Notes: Notes previously stored in Daily Logs. These typically include a treasure trove of information, such as past hazard avoidance tactics/results, installation successes/failures, and learned best practices for dealing with specific weather/delivery/lifting/storage situations. .
- Health and Safety Reports: This is data that has captured injury incidents at identifiable stages/conditions/tasks that will likely reoccur on future site projects.
- Traffic and weather reports: This is data on seasonal traffic and/or weather patterns that has been recorded by the project/site manager and could predict similar challenges in the future.
- Contractor data: Installation notes from contractors who have worked at a site that are discovered and recorded can help design more efficient ways to perform a set of tasks on future projects.
- Checklist and Hitch Reports: A standard “to do list” in construction, this information can be analyzed for patterns that suggest a situational likelihood of similar quality issues occurring in the future.
Looking back, this grim data isn’t so grim after all, as it’s mostly based on information recorded daily by many members of a given contractor or contractor team. Knowing that this obscure data is mostly accessible, hiding in the servers/clouds of construction companies, now is the time for technology providers to scale it up, access it and analyze it. The good news is that the industry is catching up with a new wave of AI-based tools used to mine this data and apply it to decision-making.
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