The construction industry is awash in data, which until recently was seen as little more than a project byproduct to be filed away once the job is over. In the old days, past project data might be dug up and referenced again at the outset of a future similar project. However, the older the information is, the more likely it is to be outdated and less useful to project planners.
Static project data does virtually nothing to make planners’ jobs any easier. A spreadsheet of project materials costs from 20 years ago found in the back of a file cabinet is unlikely to offer much insight in 2020. Along with the development of artificial intelligence, the vast processing power of computers has been applied to enable digging into that data. It’s been nothing short of a goldmine for C&E companies, who are waking up to the power of unlocking its predictive capabilities.
Data Analytics’ Important Role in Preconstruction Planning
The success or failure of a construction project starts with planning. Every aspect must be accounted for, from designing the structure itself, to hiring subcontractors, to arranging the delivery of materials. And all that is done before the first nail is even hammered. That’s a lot of balls to keep in the air, and if the orbit of any one of them is thrown out of whack, it can jeopardize everything else.
Data analytics has a role to play in easing some of that burden. It can also help firms avoid the headaches and wasted time that accompanies disputes resulting from going over budget and falling behind schedule. Globally, the average large construction project typically takes 20 percent longer to complete and runs a whopping 80 percent over budget.
Predictive analytics is already all around us in our everyday life.
All too often planners find themselves needing to reinvent the wheel at the start of each new project. It doesn’t have to be this way, though. Machine learning tools have the ability to look at historical project data, pick out patterns, and offer guidance based on that context while factoring in current, real-time market trends. This can include everything from materials price spikes observable during certain months of the year or common aspects of similar projects that have gone over budget. The tools can be also used for flagging potential risks based on what has previously gone wrong.
Predictive analytics is already all around us in our everyday life. When Google or Amazon’s algorithm thinks it knows what you’re searching for it will offer up suggestions you can click to save time. That same principle is being applied with data-driven planning tools. As a project begins to take shape during preconstruction planning, predictive analytics software will scan its data stores to offer suggestions based on what it thinks will lead to the best outcome. These suggestions can be accepted or rejected by the human planner, and the system will self-improve as it learns from its mistakes. Procore Analytics is a market-leading analytics tool companies are using to turn data into actionable insights. It not only generates custom reports, but it allows you to choose how the data is presented.
Analytics Considers Every Angle
Budgeting is another major factor project planners must consider. While their expertise may get them to within striking distance of the final numbers, the number of projects winding up over budget suggests it’s not exactly a perfect method.
The need for a combination of current and historical data stems in part from how radically the industry has changed since the Great Recession.
Predictive analytics gives planners the ability to produce data-driven budgets that consider critical but easy to miss details, such as regional labor cost trends. According to Construction Specifier, predictive systems have been used to more accurately predict the cost of a construction project as far out as three years before breaking ground.
The need for a combination of current and historical data stems in part from how radically the industry has changed since the Great Recession. Construction’s labor force declined significantly and has never fully recovered. The resulting diminished labor pool has made it much more challenging for builders to craft accurate budgets based on historical data alone.
Predictive analytics gives firms opportunities to save time and money at just about every project phase, but it’s in the earliest stages where its value really shines through. While human ingenuity will likely never be fully replicable by artificial intelligence, today’s tools for extracting valuable insights from historical data and current market realities are helping companies budget better, allocate resources more accurately, and sidestep pitfalls that weren’t seen on previous projects.