Mitigate Construction Risk with Data Analytics; Solve Problems Before They Happen
Construction is a complex, fast-paced, high-stakes business. Every project faces numerous risks that can lead to cost overruns, project delays and safety hazards.
Unpredictable things happen and cause these problems. No one ever intends to have a delay or a cost overrun or an accident. However, with the advent of data analytics, we are now finding ways to avoid these challenges.
Analytics is transforming how risks are identified, assessed and mitigated. By leveraging good data and analytics, construction teams can enhance decision making, improve project outcomes and safeguard against potential pitfalls. However, you can only leverage good data if you have good data to leverage. That’s where a next-gen PMIS (project management information system) like Kahua comes into play. With a single, secure location for project information and with process controls in place to ensure the quality of data, advanced analytics tools can truly give a construction team a head start on mitigating risks.
Understanding Risks in Construction
Before delving into how data and analytics can mitigate risk, we must understand the types of risks prevalent in the construction industry. These include:
· Financial Risks: Cost overruns and budget mismanagement can severely impact profitability.
· Schedule Risks: Delays due to unforeseen events or mismanagement can lead to project timeline extensions.
· Safety Risks: Accidents and unsafe practices can endanger workers, shut down a project site and lead to lawsuits.
· Quality Risks: Poor workmanship and material quality can result in defects and rework.
· Regulatory Risks: Non-compliance with laws and regulations can lead to fines and project shutdowns.
Risk categories could also include economic, geopolitical, supply chain, ESG and climate change, cybersecurity and more, but most of our concern can be capsulized in the five highlighted above.
Data and analytics play a pivotal role in mitigating these risks by providing actionable insights derived from historical and real-time data. Here are several ways in which they can be applied:
Predictive Analytics for Risk Assessment
Predictive analytics leverages historical data to forecast potential risks and outcomes. By analyzing past project data, construction firms can identify patterns and trends that indicate high-risk scenarios. For example, predictive models can estimate the likelihood of cost overruns by examining factors such as project size, location and complexity.
A construction company can use predictive analytics to assess the risk of delays on a large infrastructure project by analyzing data from its previous projects and identifying critical risk factors. If you know where you failed previously, you can implement proactive measures and greatly reduce your risk of delays.
If you apply analytics to your safety practices, you can likewise identify high-risk scenarios and avoid them. The same capabilities would be applicable to quality and regulatory risks as well.
“Data is a tool for enhancing intuition.” -- Hilary Mason, Founder of Fast Forward Labs
Real-Time Data Monitoring
Real-time data monitoring involves the continuous collection and analysis of data from various sources, such as your PMIS, various sensors, GPS, drones, etc. This real-time data provides construction managers with up-to-date information on project progress, resource utilization and site conditions. By monitoring this data, managers can quickly identify deviations from the plan and take corrective actions.
For example, photogrammetry from drones can feed Daily Reports in the PMIS. Dailey Reports record how much material is on site. Analytics can measure material onsite vs material needed in the coming days or weeks, identifying discrepancies that can lead to work stoppages or slowdowns.
Risk Management Solutions
PMIS solutions can provide tools for assessing and managing risks effectively and collect the project data used by the tool. These tools might include features such as risk scoring or scenario analysis.
A construction owner implemented a risk management application that used data analytics to evaluate risks across its portfolio of projects. Looking at location, weather, who was the lead contractor, union vs right-to-work and the application’s insights enabled the company to prioritize high-risk projects and allocate resources accordingly. This resulted in a reduction in overall project risk and insurance costs.
“The best days in the professional life of a data scientist are the days we are able to help our clients understand what disrupts their business in ways they could not know before data informed them.” --Cosmin Ticu, Lead Data Scientist at Kahua
Enhancing Safety through Data Analytics
Safety is a paramount concern in the construction industry, and data analytics can significantly enhance safety measures. Safety systems are traditionally reactive, looking back at safety incidents and dealing with their consequences after an accident. By analyzing data from safety reports, incident logs, etc, construction firms can identify high-risk activities and implement preventive measures.
“Data science is all about asking interesting questions based on the data you have, or often the data you don’t have.” --Sarah Jarvis, Director of Applied Machine Learning and Data Science at Secondmind
One contractor analyzed data from wearable devices worn by workers to monitor their health and safety conditions. The data revealed that workers in certain areas were exposed to higher levels of dust and noise. The company took corrective actions, such as providing better protective gear and improving ventilation, leading to a dramatic reduction in health-related incidents.
Others are making better use of the tools they have always used.
Overcoming Challenges in Data Implementation
While the benefits of data and analytics in mitigating risk are clear, there are challenges to their implementation. These include data quality issues, lack of standardized data formats and resistance to change among stakeholders. To overcome these challenges, construction firms need to invest in a PMIS to govern the data and the way it is collected.
They must also provide training to their workforce and foster a culture that values data-driven decision-making. Make a plan and work your plan. Bring in a technology consultant if needed.
“Data is like garbage. You’d better know what you are going to do with it before you collect it.” --Mark Twain, Author
Conclusion
Good data and analytics are indispensable tools for mitigating risk in the construction industry. By leveraging a robust PMIS to govern your business processes, you will have the data you need to perform predictive analytics, monitor real-time data and enhance your risk assessment and mitigation strategies.
Moreover, the use of data analytics in safety management can lead to a safer working environment. As the industry continues to evolve, embracing data-driven approaches will be crucial for achieving successful project outcomes and maintaining a competitive edge.
Kahua is the leader in PMIS and the use of embedded data analytics. If you are an owner, program manager, contractor or subcontractor and are serious about improving your project outcomes, please connect with our team.
