Over the last decade, if you have heard me speak, I’ve been saying to collect all the data you can now, even if you aren’t sure what to do with it. You can’t go back in time and collect it later. Even a decade’s worth of data for any company feels overwhelming. And then, when you broaden that to the company’s lifetime or even the lifetime of their current enterprise resource planning and other data systems, we end up with mounds and mounds of data that may leave us saying, “Now what?”

The vast amount of information generated from previous projects is a goldmine of data. If properly harnessed, it can drive significant improvements in efficiency, cost savings and overall project success. However, the challenge lies in effectively managing and using this data to realize its full potential.

Historical project data offers insights to inform future projects and enhance decision-making processes. This data includes project timelines, cost estimates, material usage, labor productivity and more. By analyzing this information, we can identify patterns, benchmark performance and make data-driven decisions leading to better outcomes.

Effective data management is the foundation of any successful data-driven initiative. Before diving in deep to see what insightful nuggets lie in this goldmine, there are some best practices to ensure historical data is managed efficiently and used effectively in projects.

Data Management and Quality Assurance

I used to be a larger proponent of the single source of truth, the centralized platform or cloud-based solution for data storage and management — ensuring that all historical data is accessible from a single location, facilitating easy retrieval and analysis. While centralized platforms also provide a unified view of the project, enabling better coordination among different teams and stakeholders, they may not always be feasible, or the infrastructure may not lend itself to this structure. 

Another option is to leverage application programming interfaces and other communication tools to connect systems, providing adequate keys in the data to relate the information from one source to another. This allows the data to reside where it was created, be easily accessed and then stitched together when needed.

For intensive data work, there is often a need to pull all the data into one pool, pond, lake, etc. Standardizing data formats is crucial for ensuring compatibility and ease of analysis. Without standardization, data from different sources can be inconsistent, making it difficult to draw meaningful insights. 

This leads us to quality assurance. The accuracy and reliability of data are paramount. Poor-quality data can lead to incorrect conclusions and misguided decisions. Implementing data quality assurance measures, such as regular audits and validation checks, can help maintain the integrity of the data. 

Machine learning and artificial intelligence can significantly enhance data standardization and quality assurance by automating the processes of identifying, correcting and standardizing data discrepancies. 

ML algorithms can analyze vast datasets to detect patterns and inconsistencies, ensuring that data conforms to predefined standards. AI-powered tools can continuously monitor data quality, flag anomalies and suggest real-time corrections. 

This automation improves the accuracy and reliability of the data and reduces the manual effort required for data cleaning and validation, enabling us to maintain high-quality data efficiently and consistently.

Descriptive, Predictive and Prescriptive Analytics

Once data is collected and managed effectively, the next step is to analyze it to extract valuable insights. This is where data analytics and business intelligence tools come into play.

Descriptive analytics involves analyzing historical data to understand past performance and identify trends. For us, this can mean reviewing data on project timelines, labor hours and material costs to determine what went well and what could be improved. Descriptive analytics provides a solid foundation for making informed decisions.

Predictive analytics uses historical data to forecast future outcomes. By identifying patterns and trends in past projects, we can predict potential issues and opportunities in upcoming projects. For example, if data shows that certain types of projects consistently run over budget, we can take proactive measures to mitigate these risks in the future.

Prescriptive analytics goes further by predicting outcomes and suggesting actions to achieve desired results. This involves using advanced algorithms and ML models to recommend the best course of action. For us, prescriptive analytics can provide actionable insights on optimizing resource allocation, scheduling and procurement to enhance project efficiency and profitability.

Analyzing data from previous projects can help us develop more accurate project plans and schedules. By understanding how long specific tasks typically take and identifying potential bottlenecks, we can create realistic timelines that minimize delays and ensure timely project completion.

Labor Productivity and Material Usage

Historical data on material and labor costs can inform more accurate cost estimates and budgets for future projects. By understanding past spending patterns and identifying cost-saving opportunities, we can develop realistic and efficient budgets. These new data tools can help us close the loop from estimate to actual quicker, allowing us to be more accurate with future bids.

Data on labor productivity and material usage can help us optimize resource allocation. By identifying which resources are most effective for specific tasks, we can ensure that the right people and materials are in the right place at the right time, maximizing productivity and minimizing waste

Historical data can also enhance quality control and risk management efforts. By analyzing past project outcomes, we can identify common issues and implement measures to prevent them in future projects. This proactive approach to risk management can lead to higher-quality projects and reduced rework.

To illustrate the benefits of leveraging historical data, consider the following hypothetical scenarios:

Case Study 1: Optimizing Project Timelines

XYZ Sheet Metal analyzed data from its past 10 projects to identify common causes of delays. It discovered that delays often stemmed from late deliveries of key materials and an insufficient workforce during peak periods. 

By partnering with reliable suppliers and adjusting its labor schedules based on project phases, XYZ Sheet Metal reduced project timelines by an average of 15 percent. This not only resulted in significant cost savings but also improved client satisfaction.

Case Study 2: Improving Cost Estimates

ABC Electrical Services used historical data to refine its cost estimation process. By incorporating data on actual material and labor costs from previous projects, it developed more accurate estimates for new projects. This helped it avoid budget overruns and secure more profitable contracts. 

In one instance, accurate cost estimates allowed ABC Electrical Services to bid competitively on a major commercial project, which it won and completed within budget.

Case Study 3: Enhancing Resource Allocation

LMN Plumbing analyzed labor productivity data to identify the most effective team configurations for different types of tasks. It optimized its resource allocation by examining which teams performed best under various conditions. 

For example, it assigned its most experienced plumbers to complex installations and used less-experienced staff for routine tasks. This approach improved overall productivity by 20 percent and reduced project timelines by 10 percent, resulting in higher efficiency and profitability.

Privacy and Security

As always in these discussions, it’s essential to prioritize data privacy and security. Protecting sensitive data from breaches and cyberattacks is critical to maintaining client trust and ensuring compliance with industry regulations. Implementing robust cybersecurity measures, such as encryption, access controls and regular security audits, can help safeguard data and mitigate risks.

It is an exciting time with emerging technologies such as AI and ML poised to further transform data management in the construction industry. These technologies can enhance data analytics capabilities, improve data security and provide new opportunities for innovation and efficiency. We finally have the tools we need to mine for gold among all this data.

Effectively managing and using historical project data can provide us with valuable insights to drive better outcomes and future-proof operations. By adopting best practices for data management, leveraging advanced analytics tools and prioritizing data privacy and security, we can harness the power of data to achieve greater efficiency, cost savings and overall project success.

Travis Voss is SMACNA’s director of innovative technology and fabrication. In this role, he aids member contractors in identifying the critical technological trends within the industry and assists them in remaining at the forefront of these developments. Before joining SMACNA, Voss worked for Helm Mechanical as its leader of innovative technology. He serves his local community as a volunteer firefighter.