Plan, Build, Operate: Concepts of Data Science for Construction
The merging of data science with ConTech is transforming how construction companies plan, manage and execute their building projects. Exciting innovations in cloud services, IoT technologies, and augmented reality are happening as established data sources like BIM, cost reports, and environmental reviews also shift. These advancements give construction industry leaders and professionals a cause for optimism.
It’s estimated that the general adoption of ConTech solutions could boost productivity by up to 40% industry-wide, while research has shown that investments in data analytics net an average of around $13 for every dollar spent.
The power pairing of construction technology with data science is poised to yield healthy ROIs along with other benefits. Here are a few potential use cases for every phase of a project:
The financial executives of a construction company can leverage their big data inventory to make better forecasts of new project costs. By looking at past labor, material, and equipment expenses along with productivity metrics and other historical data, they can get a more realistic handle on the projected costs of a new construction project from start to finish. The information empowers the company’s decision makers to write more accurate bids, and guides project managers to stay within the planned budget.
Construction managers can also mitigate future risks by learning from past mistakes. Artificial intelligence (AI) and machine learning algorithms identify patterns of cause and effect in a company’s data warehouse, which gives managers a better understanding of what actions and decisions have led to problems in the past. They can then apply this insight to new projects and avoid repeating undesirable patterns, in the future.
Site planners and project managers can use data analytics to optimize the schedules and logistics of their construction activities. Data about the weather, local traffic, and business and community events around the job site, can be aggregated and analyzed to reveal the best timing and conditions for orchestrating large supply deliveries. Drivers can then close off routes that will adversely impact the public while also reducing transportation time.
Foremen and supervisors can use data science to streamline logistics within the job site itself. Data-connected and sensor-equipped bulldozers, forklifts, and cranes, coupled with smart devices worn by workers, stream details about their geolocation coordinates and environmental conditions to a data analytics platform hosted in the cloud.
Based on these inputs, predictive analytics makes recommendations about the best placement for crew members, the best location to store building supplies, and the most efficient route for moving supplies and equipment from one area of the job site to another.
Wearable devices like data-connected hardhats, vests, and body cameras can also be leveraged to improve worker safety. When a construction company applies machine learning to big data collected from wearables, it can discover historical patterns linked to dangerous tasks and conditions. Predictive analytics can then recommend behavioral changes and preventative measures that could be taken to reduce the chances of future incidents.
The owner and facilities director can use data science to optimize the ongoing maintenance of a building and streamline operational costs. Information from IoT-enabled electrical, heating, and cooling systems is aggregated on a data platform and analyzed for performance efficiency and energy use, as well as patterns of activities that may have led to system downtime and the need for repairs.
Briq is the only data analytics platform designed exclusively for the construction industry. We help contractors and specialty contractors of all sizes make better financial predictions and decisions through data. To learn more, contact us here.