Use of Real-Time Data Analytics in the Construction

The integration of Data Analytics is reshaping the world of the construction industry by improving project management, efficiency, and decision-making across the construction lifecycle. Data analytics in construction can assess data on performance, finances, or safety and define the areas of improvement with historical data.

According to a report by MarkWide Research, the value of global big data in the construction market is expected to reach $10.8 billion by 2025.

The construction industry is among the primary data-intensive markets and there is an increasing need for better construction analytics and big data management. By leveraging big data, construction firms can significantly improve their project management and optimize costs ultimately leading to wider adoption of this technology.

Research by KPMG reported that more than 45% of construction companies are in the process of adopting digital analytics tools and around 68% have already adopted or plan to use advanced analytics systems to capitalize on the growing construction market.

Here is an in-depth look at the growing role of data analytics in construction, highlighting strategies through real-time insights from construction projects across the globe.

Why use data analytics in construction?

• Better Decision Making: By leveraging data analytics, stakeholders get critical insights into project performance to make quicker decisions resulting in effective resource allocation and risk management.
  
• Increase Productivity: Construction teams can identify bottlenecks and inefficiencies, streamline processes, and maximize productivity by scrutinizing data on project timelines, resource utilization, and workflow patterns.
  
• Effective Cost Management: Data analytics identifies cost-saving opportunities by discovering waste, optimizing resource allocation, and improving budget forecasting accuracy, reducing project expenses.
  
• Risk Mitigation and Quality Assurance: By analyzing data, Construction teams maintain quality standards and identify potential risks like safety hazards or delays allowing for proactive risk management.
  
• Optimized Resource Allocation: Construction companies can minimize idle time, optimize project schedules, and allocate resources effectively by evaluating data on material usage, equipment efficiency, and labor productivity.
  
• Predictive maintenance: Data analytics facilitates the prediction of equipment failures and maintenance needs through IoT sensors and monitoring systems, enabling proactive maintenance and preventing costly downtimes.
  
• Increased Collaboration: By leveraging data analytics, project stakeholders experience enhanced communication and collaboration. There will be a centralized platform for sharing and analyzing project data for better coordination and alignment of project goals.
  
• Improved Client Satisfaction: Data analytics will monitor project progress, manage timelines, maintain quality standards, and deliver projects on time, within the budget and desired quality thereby enhancing client satisfaction.

Real-World Examples of Using Data Analytics in Construction Projects

1. Predictive Analytics for Project Planning: In the Crossrail project, predictive analytics were deployed to identify and mitigate risks. By examining historical data and market trends, the team anticipated delays related to weather, material supply, and labor availability enabling more effective scheduling and resource allocation thereby keeping the project on track.

2. Real-Time Monitoring with IoT Devices: Integration of IoT devices on construction sites offers real-time data to improve project management. In the HS2 high-speed rail project, IoT sensors monitored equipment usage, tracked materials, and ensured the safety of workers. Real-time data empowered project managers to make swift decisions thereby reducing downtime and increasing the site efficiency.

3. Data Analytics for Enhanced Safety: Data analytics significantly elevate the safety standards in construction. In the construction of The Shard project, data from wearable devices monitored the health and fatigue of workers. This analysis revealed patterns that could lead to accidents, allowing for timely interventions and more effective safety protocols.

4. Optimizing Supply Chain Management: The Thames Tideway Tunnel project leveraged analytics to optimize its supply chain by evaluating supplier data, transportation routes, and inventory levels. This approach allowed for accurate forecasting of material needs and minimized delays due to supply shortages, bringing better supplier coordination and timely material delivery.

5. Improving Quality Control: Data analytics drives the maintenance of high-quality standards in construction projects. The Battersea Power Station redevelopment project used data analytics to track the quality of materials and workmanship. By analyzing the inspection and test data, the team identified defects and implemented corrective measures to take swift actions and minimize rework and costs.

6. Financial Management and Cost Control: Proper financial management is crucial for staying within the budget in construction projects.  The Crossrail project leveraged data analytics to monitor expenses and manage budgets. Integrating financial data with project schedules and progress reports assisted the team in detecting cost overruns and taking real-time corrective actions, maintaining financial stability, and avoiding budget crises.

7. Collaboration with Data-sharing platforms: For large construction projects to be successful, robust collaboration amongst stakeholders is highly critical. The construction industry exemplified by the HS2 project, employs Common Data Environments to drive seamless data sharing. These centralized platforms enhance communication, reduce errors, and improve decision-making processes to work towards shared objectives.

8. Real-time scenario with data analytics: In the Crossrail project, by leveraging Building Information Modeling and other analytics tools, the project team integrated data from diverse sources. This analysis enabled the visualization of potential issues, optimization of workflows, and resource management. The insights were pivotal in navigating the complexities of the project by demonstrating the transformative impact of data analytics in the construction sector.

Improve Construction Data Analytics with Linarc

As the construction industry undergoes digital transformation, more projects are embracing integrating data analytics in their business to drive safety, quality, and efficiency. Linarc represents this revolution by enabling the construction industry to predict outcomes, mitigate risks, and operate intelligently.

Linarc empowers the construction teams with a suite of cloud-powered features for real-time inventory and usage tracking, preventing shortages and overstocking, and with real-time project status updates, managers can monitor progress, identify delays, and make data-driven decisions.

Contact us at https://linarc.com/requestdemo/ to learn more about our construction and big data solutions using some of the most innovative technologies.