In this special guest feature, Richard Harpham, Vice President of Slate Technologies, outlines the digital “dark data” construction and real estate companies need to know about and how AI can help reveal them. Slate is an AI platform that maximizes efficiency and costs for the construction industry. Prior to Slate, Richard led the software commercialization efforts for construction startup Katerra, which was a technology-driven off-site construction company.

There is a quixotic issue at play in the construction industry, namely that while techniques, tools and machines have changed, how the industry runs has remained largely unchanged for over 100 years. This has had an effect on the industry’s bottom line and  according to McKinsey, “globally, construction sector labor-productivity growth averaged 1 percent a year over the past two decades, compared with 2.8 percent for the total world economy and 3.6 percent for manufacturing.”  This is because the way managers handle the day-to-day issues that require problem solving remain unchanged – namely using written notes, Excel spreadsheets, and gut instincts for real-time decision making.

“Keeping things old school” might be the most comfortable way to do business, but it leads to a lot of waste and costly errors, which then leads to slow industry growth. Thankfully, the answer is hiding in plain sight in the digitally created dark data across almost every function in the construction industry

So, what is Dark Data? Gartner defines dark data as the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes.  It’s estimated that companies only use or tap into about 1% of their dark data that they have and store.

In construction, the problem with dark data is that the industry doesn’t   access it and doesn’t know what to look for when they do. According to a white paper from FMI, 95% of all data captured in construction and engineering industry goes unused.

So, what dark data should construction site managers and executives be looking for?  Typical examples of this dark data include:

  • Common Data Points: This includes emails, spreadsheets, PowerPoint and Word files, text on drawings, voice-messages on phones, and minutes from meetings.

There are, however, most specific examples of construction dark data including:

  • Critical lead-time information: This is data collected based on things experienced in a similar past project.  Some leading firms experience exactly the same down-time issues caused by not ordering certain materials early enough to arrive when expensive labor is ready to install them, such as cross-laminated-timber or glulam, custom mill-work, interior lighting, custom doors, or windows, etc.
  • Lessons Learned’ reports: This is a typical and simple example found in almost every construction company, generally created at the end of multi-month or years-long projects. They are prepared and diligently stored, but never cross referenced, or presented to decision makers at the right moment during future projects.
  • Site Notes: Previously stored notes in daily logs. These usually include a treasure trove of information, such as past risk avoidance tactics/results, installation success/failures, and learned best practices for tackling specific weather/delivery/lifting/storage situations.
  • Health and safety reports: This is data that captured injury incidents at identifiable stages/conditions/tasks that will probably reoccur at future site projects.
  • Traffic & Weather Reports: This is data about seasonal traffic and/or weather situations that were recorded by the project/site manager that could predict similar challenges in the future.
  • Subcontractor data: Installation notes from subcontractors that worked on a site that are discovered and recorded can help design more efficient means to execute a set of tasks on future projects.
  • Punch list and snagging reports: A standard “to-do list” in construction, this information can be analyzed for patterns that suggest situational likelihood of similar quality issues occurring in the future.

In hindsight, this dark data is not so dark after all, as it is mostly based on information being recorded daily by numerous members of any given contractor or sub-contractor team. By knowing this dark data is mostly accessible, hiding in the servers/clouds of construction companies, it’s now time for technology providers to step up,  access and analyze it. The good news is that the industry is catching up to a new wave of AI-based tools being used to harness this data and apply it to the decision-making process.  

Source: Inside Big Data