Unlocking the Power of Construction Data Analytics: Insights for Better Decisions

In the construction industry today, data is being collected constantly — on phones, on computers and tablets, by cameras — and even on some of the devices used to perform construction work. If a general contractor (GC) isn’t monitoring and leveraging that information by using data analytics, they’re missing out on information — and may be making decisions that are less informed than they should be.

Data analytics can be defined as the process of examining raw data to uncover patterns, draw conclusions and identify actionable insights. Analytics provide real-time information and feedback on how well construction companies are performing across various projects, tasks and processes. 

Though some elements of data analytics can be complex, there are also simple and relatively straightforward ways a GC can implement this process into their business — and make sure their choices are backed by reliable, live data. From basic compliance measures to descriptive statistics that provide a clear picture of what is happening, data gives organizations insight into what’s working and where to focus their attention for improvement on projects. 

This article explores the efficacy of data analytics in the construction industry, including practical steps for implementing a system of data analysis, challenges with acquiring data and best practices for fostering a data-driven culture on any construction team. 

Key Types of Data Dashboards for Construction

Data analytics can essentially perform two vital tasks — describing the current state of the work and predicting future issues, costs or other factors. 

The following depicts a few key ways these two types of analyses can make a GC’s job easier. These methods often form the basis for further analysis, involving looking over data that’s already been collected and stored to identify trends and patterns. 

Descriptive Analytics

Descriptive analytics involves programs that describe the current state of work. Descriptive statistics can provide a clear, straightforward overview of current activities in real time, making it easier to identify whether progress deadlines are being met.

One easy way to start implementing descriptive analytics is with basic compliance measures. A company can establish and track key performance indicators (KPIs) that reflect adherence to safety protocol and regulatory standards. These basic compliance metrics provide a straightforward and data-driven overview of current operations, making it easier for project leadership to identify any areas that may need attention.

Predictive Analytics

Predictive analytics is a more advanced and sophisticated form of data analysis, and is a helpful tool for GCs and project leadership  since these programs can anticipate future issues and costs using statistical modeling. This can be useful in project planning, resource allocation and risk management. 

There are many varieties of predictive analytics techniques, from regression analysis to clustering.  In general, these tools and models use historical data to forecast future outcomes. For example, using factor analysis and clustering, companies can group together similar materials and use these clusters to anticipate costs for future projects that use the same materials—using all of this information to generate a conceptual bid estimate.

Practical Implementation of Data Analysis

One of the most vital parts of analyzing data is making sure the data is useful to begin with. Construction companies have to make sure they’re collecting high-quality, high-integrity data.

The principle of "garbage in, garbage out" applies here—if data collected is poor, imprecise or inaccurate, any insights derived from it will be just as flawed. By implementing standardized data collection practices, companies can make sure that the information they analyze is both reliable and actionable.

Equally important is the training and adoption of these standardized practices. Team members must be educated on good data habits and trained to accurately review visualizations and regularly look at relevant data.

Challenges of Data Analytics

Getting Company Buy-in

The main challenge to data analytics is getting buy-in across a construction company. Some team members, especially those used to more traditional methods of assessing progress and cost, may be resistant to change. 

To overcome this, demonstrate how data analysis can provide tangible improvements little by little. Starting with tools that are proven to increase efficiency or cut costs can help illustrate the value of these programs and encourage wider adoption.

Navigating Costs

Another significant challenge is the cost associated with implementing data analytics. From purchasing software and tools to hiring skilled personnel, expenses can add up quickly. Advanced analytics platforms, data storage solutions and visualization tools like Power BI, Tableau or Looker often come with substantial licensing and subscription fees. 

Additionally, hiring or training staff to manage and analyze data — such as data analysts, engineers and scientists — may also be necessary to budget for in order to successfully develop a data program. 

Companies can mitigate these costs by starting with the basics. Implementing fundamental data collection and analysis practices can provide immediate benefits and lay the groundwork for more advanced analytics in the future. This incremental approach allows companies to build their data capabilities over time without incurring prohibitive costs upfront.

Best Practices for Implementing Data Analytics

Data analytics has the potential to transform construction operations, driving informed decision-making and improved project outcomes. To fully realize these benefits, it's important to implement data analytics thoughtfully and strategically.

Build a data-driven culture.

The evolution of technology presents new opportunities to enhance efficiency. Finding success in data analytics relies on the solid foundation of a data-driven culture and a willingness to change old methods from every member of the crew. 

Company leadership can start by emphasizing the importance of good data habits. Regularly reviewing visualizations and reports generated by data analytics tools encourages team members to understand their value and how to use and read them correctly. Before analytics can be most useful, an environment must be fostered where data is valued and utilized effectively. 

Adopt tools incrementally.

It’s perfectly OK — and in some cases, best — to go slow with data analytics tools. While there are dozens of programs and tools that hold the capacity to forecast costs, automate schedules and predict obstacles, adopting tools incrementally can make sure that involvement with data analytics grows at the same rate as buy-in and comfort level with increasing technology. 

It's often effective to start with one team at a time, allowing for focused training and smoother adoption. Prioritizing which data sets to start with can also provide quick wins and demonstrate the value of analytics early on. 

Work with a Data Process Analyst.

There are specialists who have spent their careers helping companies develop their data processes, and hiring a data process analyst can be a game-changer in gaining buy-in across a company, as well as training team members to successfully collect good data. 

A data process analyst can guide teams through the initial setup of data collection and analysis practices, helping them understand how these tools work and how they can help them at their jobs. They can also provide ongoing support and training. 

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The Future of Data Analytics in Construction

As the industry evolves, there’s likely to be a significant shift from merely descriptive analytics to more advanced predictive and prescriptive analytics — tools and processes that can forecast future obstacles and suggest actionable and proactive steps to handle them.

The field of construction is on the cusp of a data-driven transformation, where Artificial intelligence (AI) and Machine Learning (ML) are set to play pivotal roles. These technologies can analyze vast amounts of data far more quickly than human capabilities. Machine learning algorithms can enhance predictive analytics by continuously learning from new data and improving their accuracy over time. The application of AI and ML in construction could range from optimizing project schedules and resource allocation to predicting equipment failures and improving safety measures.

But in the current state of the industry, most companies don’t have data that’s mature enough to make these tools effective. Many are still in the early stages of their data analytics adoption journeys and should focus for now on improving data quality, standardizing their data formats and training team members to understand and use data tools.

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