Did you know that the construction industry faces many challenges? One crucial challenge is that the construction process itself generates a lot of data often stored in multiple antiquated systems. It makes progress hard to track, less transparent, and more prone to failure. 

Construction analytics solutions analyze real–time data against performance thresholds–assessing trends over time and transforming information into insight, enhancing decision-making, and improving overall project delivery. Data analytics tools are designed to pull information from large data repositories and make it accessible to everyone involved in the construction process, including contractors, architects, tradesmen, and clients.

It has been reported that roughly 96% of construction and engineering data went unused in 2018. 

There are many construction companies that have used data analytics and have seen improvements in productivity, efficiency, and overall project costs.

Advantages of Data Analytics in Construction

  1. Reduces Environmental Impact: Construction and engineering industries account for 39% of process-related carbon dioxide emissions. The construction industry continues to grow so does the need for more sustainable construction practices and eco-friendly building materials. Therefore, bringing big data into construction can help solve this problem. Construction data from past projects can be integrated into BIM technology to more accurately predict the materials and energy needed for a future project. 
  2. Improves working conditions: Construction workers get more injured on their job than in any profession. The power of big data can gather health and activity information, detect safety hazards, and alert construction crews to breaches in safety protocols. This information not only protects the workers using safety technology but also prepares project managers for potential safety hazards on future projects.
  3. Increases Building Efficiency: Data analytics technology works to reduce construction time and material-related costs by presenting clear, digestible data and identifying potential structural errors before they happen. This allows project managers to make quicker, more informed decisions.