The engineering and construction industry is one of the largest sectors in the global economy. Its value is estimated at ten trillion dollars per year, or 13 percent of global GDB. By any standards, construction is big business. As the world continues to become more and more densely urban, construction is expected to be ever more important.

Yet even an industry of stone and steel is not immune to the technological demands of the new era. By the year 2020, the world will see as many as 50 billion Internet-connected devices. Each devices exudes its own “digital exhaust” – information about the user’s preferences, whereabouts, and other information. In an increasingly wired world full of smart buildings with automated systems, it is imperative for construction and engineering to use a data-centric approach in order to stay relevant.

Technology and Disruption

The construction and engineering industries are experiencing a major transition thanks to AI, machine learning, and related technological developments. More and more routine tasks which used to be performed by people are now being automated.

Engineering and construction firms are having to adapt to the new, tech-driven landscape. The most successful companies are harnessing the power of big data to move past their competition. Here’s how the construction industry can make data science work for them.

The Uses of Data

To some extent, construction companies already use data. Every successful construction and engineering firm uses data to track costs, estimate budgets, and plan out time frames for projects.

However, modern data science can take that basic level of planning and tracking to a new level.

Improving Hiring Practices

In the old days, it was up to the foreman to keep an eye on workers and track their productivity. Today, construction firms can do an objective evaluation of how productive their workforce is.

Software like Oracle Primavera, Procore, or FINAL CARD allows companies to track the performance of contractors and determine whether each contractor is performing at their optimal level. This can make a huge difference when it comes to making hiring decisions.

Improving Productivity

Data from workers’ movements can help companies analyze whether their workforce is wasting time or energy. Gathering and analyzing this data can help firms figure out how to reconfigure the worksites to allow for the best workflow and the highest productivity.

Software can also more effectively track equipment and carry out asset management. Equipment – everything from heavy machinery to small parts – is crucial in this industry. Using geolocation to track equipment, sensors to evaluate its performance, and cost analysis to decide when to buy new equipment can all make a big difference to a company’s performance and bottom line.

Transportation Routes

Data from weather reports, social media, and from past products can all be cross-referenced to map out the most effective transportation routes. This eliminates waste and cuts costs for fuel and manpower when setting up a project.

More Accurate Predictions

Data from past projects can help companies plan out their future projects. Knowing how long a project is likely to take, what the costs will be, and what the possible snags are, will help companies produce more accurate bids. This will also improve their reputation for reliability.

Construction simulation tools are very useful in this context, too, since they allow companies to predict the costs, time expenditure, and risks associated with a hypothetical future project.

Project Planning

Data can help construction companies decide where to build, what to build, and how to build it. Environmental data can help companies pick the best possible construction site. Data gathered from social media and from members of the community can help determine what the building will look like. Data from weather, traffic, and local businesses can help determine when is the best possible time to schedule construction.

Data Sources

To some extent, construction companies already are sitting on rich supplies of data. Companies with years in the business can draw on their past experience when planning out new projects.

Digitizing data means making existing information more accessible and easier to analyze. It also means making it easier to cross-reference existing data against other sources of information.

Other sources of information can include data from social media, weather reports, and traffic patterns.

The “internet of things” is also creating new, rich sources of data. As more routine tasks are automated, ever more data logs will be produced, and this will be a rich source of insight into productivity, efficiency, and risk analysis.

New data sources for the future will likely include drones, LIDAR, and information from wearables and smartphones.

Final Thoughts

Like any other industry, the construction business has certain problems to overcome. Some of the issues that tend to plague construction businesses include poor planning, bad management, faulty budgeting, cost overruns, and funding shortfalls.

Fortunately, these are all issues which can be solved with the application of data science.

Using predictive analysis to map out data from past projects onto future projects can make a huge difference when it comes to planning and budgeting.

Using data on worker movements and equipment can help improve efficiency, cut costs, and improve faulty management.

Overall, data science can help companies to perform at higher levels, make better decisions, and cut costs, all while improving productivity.   See next week’s article for additional insights into technology’s role in shoring up safety and monitoring infrastructure.