Informing Better Construction and Maintenance Programs

This blog post is an excerpt from GovLoop’s recent guide, “Putting Data Analytics at the Forefront of Your Agency.”

When you think about the role of analytics in government, you probably think about numbers flying through cyberspace to inform unseen or intangible decisions about operations. But in addition to influencing programmatic decisions, data analytics can actually have a dramatic impact on the way physical infrastructure is constructed and managed.

In a recent interview with GovLoop, Lisa Cooley, Director of Federal Solutions, and Timothy Duggan, Director of Cost Analytics, at Gordian explained how analytics can be used to inform and improve construction and maintenance programs in government. Gordian provides decisive RSMeans data and innovative technology to help facility and infrastructure managers use analytics for better decisions and outcomes.

The role of analytics in the future of government construction and maintenance cannot be overstated. Currently, federal facilities and infrastructure managers are confronted with significant challenges. In addition to resource constraints like the hiring freeze and shrinking budgets, they deal with long budgeting cycles in a volatile construction cost market.

“Managers are tasked with predicting construction costs many years out in a very unpredictable market. Projects end up requiring a lot of re-scoping effort to make sure they’re keeping to the budget and the program, even when costs change,” explained Cooley.

Construction market changes in government are swift, even as projects take years to complete. “Mega-projects,” like the Base Realignment and Closure (BRAC) initiative or the proposed new border wall, require so many resources to complete that the local market’s cost and demand balance is quickly and dramatically shifted. At the same time, federal contracting regulations are often disrupted and transformed by new administrations.

To confront these challenges and volatility, managers have to develop a baseline from which to assess changes and inform decisions. As Duggan explained, “history allows us to analyze the cause and effect of shifting markets, to help predict results in this new market. It’s just the size of these different elements and the scope of work that are being expanded or diminished. But all of these changes actually have happened in the past in some shape or form.”

By understanding how market shifts have impacted construction and maintenance project costs in the past, project managers can better manage future market shifts without disruption to their programs.

Federal agencies are sitting on a treasure trove of historic data that they collect under mandate. Managers should be leveraging that data to understand how much past projects cost in labor and time to construct, as well as maintain. Then, they can better understand cost drivers including supply costs, federal regulations, labor demands, agency budget and the condition of existing real property assets.

However, the reliability and quality of data held by disparate agencies and systems is sometimes uncertain. Moreover, agencies commonly lack the tools and external data assets to analyze that data and generate insights. As a result, managers often need to draw in external data sets, as well as consultative services from experts who understand that data.

“Gordian’s value proposition is to normalize and validate historic facilities and project information, and then intersect it with historic cost databases and new advanced analytics based on economic predictive inputs. This results in statistical analysis insights into prices paid and price drivers,” said Duggan.

At the Department of Energy (DOE), managers have already made significant strides towards better asset maintenance and construction with predictive tools to budget sustainment costs. The agency used Gordian’s RSMeans labor, material and equipment costs, as well as preventative and repair maintenance projections, to accurately calculate the replacement value and sustainment costs for their facilities.

That data informed a 10-year site plan that complies with OMB requirements. DOE can also export that data to its internal Facilities Information Management System where it is used to consistently update building models and cost forecasts.

However, many agencies will need more robust tools to support this type of in-depth analysis. Duggan and Cooley emphasized seeking commercial solutions that have the computing power and machine learning capabilities to consolidate and analyze these complex data sets for relevant trends.

“The right analytical tools can help federal managers do more with less,” Cooley said. Those tools, combined with historical and normalized data, are what set facilities and construction managers up to succeed in a volatile market. Ultimately, it will be robust data analytics that ensures that government infrastructure continues to be built and maintained in an effective manner.