How the Construction Industry is Using Big Data

Illustration of computer and icons
DrAfter123 // Getty Images

In the construction industry, as in other sectors, big data refers to the huge quantities of information that have been stored in the past and that continue to be acquired today. Big data can come from people, computers, machines, sensors, and any other data-generating device or agent.

That, naturally enough, is what makes it big. Construction and building big data already exists in all the plans and records of anything that was ever built. It is also constantly increasing with additional input from sources as diverse as on-site workers, cranes, earth movers, material supply chains, and even buildings themselves.

The Value of Data

Traditional information systems are good at recording basic information about project schedules, CAD designs, costs, invoices, and employee details. However, they are limited in their ability to work with unstructured data like free text, printed information or analog sensor readings. Often, they can only handle orderly digital rows and columns of numbers.

The idea of harnessing big data is to gain more insights and make better decisions in construction management by not only accessing significantly more data but by properly analyzing it to draw practical building project conclusions. In fact, big data, like truckloads of bricks or bags of cement, isn’t useful on its own. It’s what you do with it using big data analytics programs that count.

Business with Big Data

To see how big data is already being used by the construction industry, consider the design-build-operate lifecycle that increasingly defines construction projects today.

  • Design: Big data, including building design and modeling itself, environmental data, stakeholder input, and social media discussions, can be used to determine not only what to build, but also where to build it. Brown University in Rhode Island, US, used big data analysis to decide where to build its new engineering facility for optimal student and university benefit. Historical big data can be analyzed to pick out patterns and probabilities of construction risks to steer new projects towards success and away from pitfalls.
  • Build: Big data from weather, traffic, and community and business activity can be analyzed to determine optimal phasing of construction activities. Sensor input from machines used on sites to show active and idle time can be processed to draw conclusions about the best mix of buying and leasing such equipment, and how to use fuel most efficiently to lower costs and ecological impact. Geolocation of equipment also allows logistics to be improved, spare parts to be made available when needed, and downtime to be avoided.
  • Operate: Big data from sensors built into buildings, bridges and any other construction makes it possible to monitor each one at many levels of performance. Energy conservation in malls, office blocks and other buildings can be tracked to ensure it conforms to design goals. Traffic stress information and levels of flexing in bridges can be recorded to detect any out of bounds events. This data can also be fed back into building information modeling (BIM) systems to schedule maintenance activities as required.

Industry Preferences for Information and Insights

As data gets bigger and bigger, the need to boil it down to the actionable essentials gets bigger too. A survey of construction companies by software vendor Sage in 2014 found that:

  • 57% want consistent, up-to-date financial and project information.
  • 48% want to be warned when specific situations occur.
  • 41% want forecasting, allowing them to better prepare for best and worst-case building events.
  • 14% want online analytics to see for instance precisely which factors are affecting profitability and by how much.

Big data analytics can enable or offer opportunities to improve each of these aspects. The variety of inputs in big data allows better levels of certainty about status reports and forecasts. The analytics can provide more helpful indications of risk levels before a threshold is exceeded and an alert generated. They also offer insights that traditional systems simply cannot.