The Transformational Power of Data
- Mar 19, 2020
Construction project performance has, until recently, relied heavily on the human element. Exceptional team leadership and personal judgment often make all the difference between projects that are on time, on budget and safe—and those that fall short.
But in a challenging and competitive environment, commercial property owners, investors and developers can’t afford a portfolio of hits and misses.
Now predictive data analytics can back up gut instinct with trusted insights, injecting more certainty into the planning and construction process. The results are quite powerful. One top commercial contractor developed advanced analytics that identified up to $40 million in near-term margin improvement and up to $500 million in overall project value opportunities, as well as a model capable of predicting safety incidents three days in advance with 89 percent accuracy.
Given the value data can provide, commercial property executives and investors clearly benefit from understanding how much the projects in their portfolios are leveraging advanced data analytics, and whether their own organizations could quickly implement a more developed program.
Identifying industry innovators
The real estate industry is in various stages of data analytics sophistication, with some sub-sectors displaying more advanced applications than others. In engineering and construction in particular, only about half of the executives who participated in KPMG’s 2019 Global Construction Survey said they apply data analytics to all of their projects, and less than 15 percent described their capabilities as advanced.
Indeed, capabilities differ greatly from one construction firm to the next. Most said they expect to be data-driven within the next five years. But the top 20 percent of firms identified through the survey as the most innovative in the sector have already invested in and introduced a number of data analytics and related technologies.
Where firms are along this journey toward more advanced data analytics is becoming an increasingly important factor when commercial real estate executives select project partners.
When partnering with a contractor or engineering firm, it may be difficult to separate those presenting flashy technology from firms with proven technology and analytics capabilities. Key differentiators include the ability to produce almost real-time accurate and updated forecasts on productivity trends, or the ability to develop new or revised cost projections based on changing project conditions.
LAUNCHING AN ADVANCED ANALYTICS PROGRAM
While implementing an advanced data analytics program can seem daunting, in fact the process to begin takes three basic steps.
The first step is to understand the data available and where it resides. Most firms are swimming in valuable information, from project field labor and safety reports, to contractor bids and schedules. But often the data is in spreadsheets and scattered across the organization. Data needs to be organized and secured, such as in a cloud-based environment, in order to develop, test and deploy analytics models.
Second, companies need to determine if they already have the expertise to explore a more sophisticated program, and then decide if they want to invest in internal resources, seek help from the outside, or some combination.
Finally, it’s important to have a road map that defines quick wins, medium-term enhancements and long-term goals for transformational upgrades to data analytics capabilities. Keys to success include gaining buy-in for the program from key stakeholders, using targeted pilots to gain momentum, and operationalizing successes at scale across the organization.
Ultimately, strong data and analytic capabilities not only can improve a firm’s business performance, but improve its ability to compete overall.
Clay Gilge leads KPMG’s Major Projects Advisory practice in the U.S. where he works with companies to apply innovative methods and tools, including advanced data analytics, to their engineering and construction projects.