The Future of Analytics in Design-Build

One of the most difficult aspects of design-build, as well as that of architects and remodelers, is the preconstruction and project planning phase. Gone are the days of designing and building based purely on a blueprint, engineering skill and vendor relationships. Data and analytics are now deeply entrenched into the planning and execution phases of any build. And even though the use of analytics has only emerged over the last 10 years, the ability to leverage this data to forecast or predict jobs and outcomes based on a number of factors is quickly evolving as the Next Big Thing for data use.

How Predictive Data Impacts Design-Build

Design-build professionals now rely on yesterday’s data in order to forecast outcomes for future projects. Data such as current market conditions, historical job outcomes and even economic criteria are all being leveraged to create more efficiency in schedules and lower the overall cost for projects.

Predictive data leverages science and analytical trends to create algorithms and formulas that combine economic insights along with data mining trends to arrive at a forecasted output that is scrutinized for more accurate project planning.

How Data Forecasting Keeps Budgets in Line

The ability to arrive at better forecasted outcomes helps create more accurate budgeting. Even though design-build professionals relied on pre-existing data, supply prices and other market factors, approximately 98% of construction projects still went over budget. Combine this inability to accurately forecast projects along with the unpredictable market and supply-side prices and the recipe for budgeting disaster is illuminated and very real. Rising labor costs create a pile-on effect that further prevents the right level of profitability for organizations.

Predictive modeling allows design-build organizations to create future business insights with a significant degree of accuracy. With the help of sophisticated data analytics tools and modeling, these firms can now use past and current project utilization data to reliably forecast budget trends milliseconds, days or years into the future.

Predictive modeling and analytics tools are expected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21% through 2022, according to Zion Market Research.

Using Historical Patterns to Forecast Future Decisions

Predictive modeling can be extremely beneficial to helping design-build firms, as well as architects, specifiers and remodelers leverage past and present data when making critical business decisions. Tech savvy firms today are utilizing this information to do everything from identify inventory and purchase history patterns, increase efficiencies with vendor partners and review past jobs in order to improve the customer experience with future builds and projects.

The problem with only using current data is that there are too many outside factors and variables that prevent it from maintaining its accuracy throughout the duration of the job. In some cases, projects are flush with cost overages by the time ground is broken on a new build. Many of these unknown outside factors include fluctuating material and labor costs, vendor relationships and agreements that shift, customer change orders, as well as economic factors such as trade/tariff regulations, interest rates and other policy mandates that could affect the bottom line.

Approximately half of the Design/Build organizations polled in a recent survey1 found that companies believe they can be anywhere from 25% - 50% more efficient and accurate in their decision-making.

Why Predictive Data Increases Decision Accuracy

There are a few reasons today’s predictive modeling data is more accurate than what organizations had access to in years past. Primarily, predictive modeling is based on actual, empirical data and macroeconomic insights from historical outputs and present-day models. It is far more elaborate than the forecasts based on theory that were used in legacy business operations.

Empirical data is based on “evidence” derived from previous cost data and other criteria that have proven themselves in actual real-world scenarios. These data outputs are then formulated into precision-based models and scenarios that offer visibility into accurate forecasting techniques organizations and data scientists today use to arrive at certain economic conclusions in their decision making.

Increased Insight Creates a Competitive Advantage

From these conclusions, design-build organizations can concoct better timelines and budget estimates with tentacles that span out into labor pools, inventory and supply chains, vendor partner relationships and customer order estimating. In fact, customer data is an increasing source of insight that is being utilized for predictive modeling. Fifty% of companies polled1 said identifying and improving more personalized customer traits and characteristics is the leading motivator behind the use of predictive modeling for their organizations.

In terms of where design-build firms are curating data1 for their predictive modeling, 60% say they’re leveraging customer data from web and phone interactions, and another 56% say they’re utilizing social media inputs as well.

Adoption and internal buy-in of predictive modeling technologies will be instrumental for the expansion of data analytics. With a clear path to see how predictive modeling can positively impact the bottom line, 37% of companies1 say the CEO and COO are typically the primary drivers for adoption.

This top-down approach will enable more firms to incorporate and grow their predictive modeling initiatives for a greater competitive advantage.