Their Construction

Why Subcontractors Need to Own Their Construction Data

As new construction projects get underway, a routine practice is for general contractors to ask their subs to use software such as Procore or PlanGrid. The GC’s goal is to ramp up efficiency by making sure all parties stay on the same page with billing, RFIs, submittals, logs, photos, and various key performance indicators (KPIs).

After the job is finished, the GC can then analyze that historical data to do things better, faster, and cheaper down the line. Subs, though, usually have no choice but to say au revoir to the data relating to their portion of the project.

As a result, subcontractors too often miss out on the full benefits of project management and construction analytics software. Busier subs may be so focused on the present project—making sure things run smoothly right now—that “leaving the data to the GC” even feels like a relief.

COMO (Costs of Missing Out)

But for subcontractors, there is a problem with this status quo—namely, the costs of missing out on data analytics are increasing all the time.

Today’s construction-management researchers are hard at work on new approaches to analysis that stand to provide powerful competitive advantages to subs. The potential benefits here are eminently practical. They include things like reducing or eliminating the need to file liens against GCs due to payment delays (a relationship-ruiner if ever there was one).

Manideep Tummalapudi, a Ph.D. candidate in the construction management and education program at Colorado State University, is among those researchers. You have likely read about how collecting and analyzing medical data can lead to earlier and better interventions. Tummalapudi and his CSU colleagues are working on something similar with respect to the project data collected by subs and GCs.

The idea is to put real-world construction projects’ cash-flow curves under the microscope by collecting planned-versus-actual data on schedules, estimates, and billings from a diverse array of projects, as differentiated by type, size, and other characteristics. “We want to look at these various metrics and attributes so that we can better understand the kinds of patterns that emerge on successful projects,” the researcher explained.

With enough project data in hand, Tummalapudi says, predictive analytics should allow subs and GCs to dramatically improve their day-to-day project management.

“The subcontractor could say, ‘right now at this stage of the project, our cash-flow curve is looking like X, and we know from past patterns that it is likely for this project to be delayed,’” he said. “If you learn this in month two or three of the project using data, you can do what it takes to help the GC/subcontractor avoid delays and overruns. If you don’t know it’s happening until month nine of the project, you’re apt to lose time, money, and reputation.”