Dig into the master schedule on any major construction project and you will likely find plenty of examples of big data. They might include terabytes of building-information-modeling data tied to perhaps seven different schedules, each with its own logic linked throughout the sequencing of the project.
If the logic that ties some 23,000 activities—about the average in a two-year project—is a bust, the time it takes the project manager to track down the problems manually while the schedule is moving forward can push a project seriously off its calendar. When that happens, everyone's margins are at risk with each cascading delay and threat of litigation.
That's one example of why, in today's buzzword bingo of information-technology jargon, big data has become a hot number.
In a recent report, Mark Beyer, vice president of research for Gartner Research, summed up the trend. "Big data has such a vast size that it exceeds the capacity of traditional data management technologies," he wrote. "It requires the use of new or exotic technologies simply to manage the volume alone."
And it's not all about size with big data, Beyer adds. "A complex statistical model can make a 300-gigabyte file 'seem' bigger than a 110-terabyte database even if both are running on multicore, distributed parallel processing platforms," he adds. "That's why big data has quickly emerged as a significant challenge for enterprises."
In an engineering and construction industry where the reach of 3D BIM technology grows more complex when scheduling is added to make a 4D BIM, big data arrives each day with an ever-louder thud.
Yet few firms are investing in data-taming tools. According to Gartner, firms in the construction and materials industries with annual revenue of about $250 million invest about 1.6% of that in IT. For firms with annual revenue of about $10 billion, the average is 1.1%—dead last compared to industries such as banking, health care, retail and transportation (see related story here). And that's for all IT operations. Within those percentages, firms are making even smaller investments in software tools to tame big data.
Breaking New Ground
But for every firm whose tech strategy is still to work off spreadsheets and re-enter data into different silos, other engineering and construction firms are breaking new ground. MWH Global, a "wet" infrastructure firm, is building unique apps for its clients, thanks to the firm's ability to capture knowledge from its data systems. Other firms such as Turner Construction are joining with Virginia Tech researchers to find new ways of looking at productivity data using jobsite video and images—that is, using mash-ups of structured and unstructured data. Still others are seeking better views of complex scheduling project data.
"No question, schedules are a bear to tame," says Shawn Pressley, chief information officer and senior vice president at construction-management firm Hill International.
"It's when you drill into a schedule's work breakdown structure or enterprise breakdown structure—that's where problems can get nasty," Pressley adds. When project managers start to associate costs to a three-day schedule of a crane, the five-day schedule of its crew, the seven-day schedule of the materials and the cost of the crew's labor—then factoring that into the schedule—the sheer size of the data can get out of control.
A prime example is the delivery of steel to a site, Pressley notes. "The schedule says when the truck carrying the steel needs to be there and [a beam] needs to be bolted to column 8B, for example," he says. "You can overcomplicate a schedule so much that logic ties don't work. That's when you get logic [busts] that say faucets on the 23rd floor will delay the concrete on the fifth."
Faulty logic woven throughout a firm's files is a common problem among the multinational crews that make up today's massive project schedules.
"We had a guy on a project in the Middle East send me a schedule to analyze. It was showing a turnaround time of three hours for what guys were normally doing in three weeks," he adds.
It was examples such as this that sent Hill in search of a new breed of business intelligence and analysis tools. The company found a solution with Acumen, an Austin, Texas, startup whose services include risk analysis of project schedules as well as benchmarking tools to measure schedule quality against industry data.