Discover Data Analytics in Construction Claim Management

Introduction

Claims management can be complex and challenging in the construction industry, often involving multiple stakeholders, extensive documentation, and intricate contractual obligations. Data analytics offers a powerful tool to navigate this complexity, improve decision-making, and enhance the chances of successfully resolving claims.

The Role of Data Analytics in Construction Claims

Data analytics systematically analyzes large datasets to uncover patterns, trends, and insights. In construction claim management, data analytics doesn’t refer to a specific software or tool but to a mindset that will use multiple software to develop a system and product that can ease the search and analysis of data in complex and large construction projects.

It can be particularly beneficial in the following areas:

Improving Claims Analysis

By analyzing past claims data, construction and claim managers can identify common causes of claims, such as delays or cost overruns, and develop strategies to mitigate them in future projects. For instance, a study might reveal that most delays are caused by late material deliveries, prompting a focus on improving supply chain management.

Enhancing Documentation and Evidence Gathering

Data analytics tools can assist in organizing and analyzing project documentation, making it easier to substantiate claims. For example, CLM ” contract lifecycle management ” software can ease data records and deliverables notifications.

Predictive Analytics

Predictive models can forecast potential claims based on project-identified risks, allowing for proactive measures in claim management. For instance, predictive analytics might indicate a high probability of project delays or cost overruns due to design or supply chain.

Implementing Data Analytics and using tools

1. Data Collection: Gather project data, including schedules, budgets, communication logs, and contractual documents. Multiple tools and software can be used in this process, such as P6, hardollars, Aconex, Autocade, Revit, Risk Manager….etc.
2. Data Processing: Organize and clean the data for analysis. This process needs to be done regularly using the same software or collecting the information and data in more accessible software like Microsft Excel.
3. Analysis: Use statistical tools to analyze the data. This step is the primary step. it encompasses using a tool that can gather information from different platforms, either automatically or manually, and then analyze and present them in a simple way that aids decision-making. A common tool is Power BI
4. Actionable Insights: Apply findings to current and future claim management strategies.

Examples

• Delay Analysis: An analysis of a major construction project’s schedule could reveal patterns in delays, attributing them to specific subcontractors or supply chain issues, thereby informing future subcontractor selection or supply chain management strategies.
• Cost Overrun Prediction: Data analytics could be used to analyze historical project data, identifying factors that frequently lead to cost overruns. This information helps in better budgeting and resource allocation in future projects.

Conclusion

By effectively implementing data analytics tools, construction professionals can better navigate the complexities of claim management, leading to more successful outcomes and more efficient project delivery.

References :

PMI – Improve claim management