aerial image of a construction engineering site

Data gathering and analysis are a key part of a construction project’s daily activity. This is how critical players in the project—stakeholders, project managers, safety officers, and field engineers—obtain fact-based insights in real time to make crucial project decisions. Error-free and accurate data management is also vital in keeping the construction project compliant with existing regulations and seamlessly carried out within the delivery cycle.

As the demand for transformative and efficient construction management solutions continues to grow, it’s clear that digitisation is vital in handling big data in construction. After all, digital construction data is easier to collect, replicate, sort, and analyse. Moreover, storing it in the cloud further simplifies planning, procurement, scheduling, progress monitoring, and other essential construction processes.

Analysing big data

The term ‘big data’ refers to the diverse sets of information that are too large to store, sort, and analyse using traditional methods. Coined in the advent and expansion of the internet, it is the by-product of digitisation. As the world becomes more connected through the improved accessibility of computers and smartphones, data production and sharing have also increased exponentially. This data is too big to measure and classify, so it is commonly referred to as ‘big data’.

Although not as massive as the daily data output of the global financial market, construction data still falls in the category of ‘big data’ for obvious reasons. For one, the sheer amount of information involved in each project’s structural analysis, cost estimate, risk management, and BIM modeling requires more robust construction analytics to expedite decision-making and task execution while preventing costly miscalculations.

Common challenges of engineering data analytics

Analysing engineering and construction data has its own share of challenges. It’s important to note that data analytics varies significantly from industry to industry, as each industry deals with unique data, its own use cases and applications, and its own data collection and management methods. For instance, unlike the retail or manufacturing industry, construction and other project-based industries often follow an irregular and distinctive pattern. This sometimes means running data on a single engineering analytics program is impossible.

Unless the required data analysis is for a progressive project that uses a single building plan, custom data analytics may be needed for each project. Or one that can be scaled up should the project manager needs to factor in more variables. Examples of these variables include:

  • inconsistencies due to mid-project changes
  • the project’s unique scale and limitations
  • the use of newly introduced materials, equipment, or methods
  • additional or replacement subcontractors
  • external forces, such as disasters or changes in transportation routes

Why data analytics is essential for engineering decision-makers

Previously a luxury not every construction company had, data analytics has become integral to every construction project. As the core of any data-driven construction and engineering management approach, data analytics provides decision-makers with insights based on facts, not flawed assumptions or cognitive biases. Here are some of the decision-making situations where data analytics is most valuable:

1. Choosing projects

As a construction company expands and adds more assets, it becomes capable of taking on large and long-term projects, often lasting several years. While this is undoubtedly a step-up that potentially promises more revenue, the associated risks also increase. Here are some risks and challenges project managers juggle when deciding to expand.

  • It’s almost impossible to define the scope of a three-, five-, or ten-year project. It can be written down on paper, but there’s no telling what might happen along the way. The chance of hitting a roadblock or adding more expenses is high.
  • The market is volatile. Prices tend to fluctuate and sometimes staggeringly. The construction company can bid at a price that might significantly plummet once the project starts.
  • With the lack of empirical data, decision-makers tend to focus on fundamental and technical market analysis, which is inherently inconsistent as it is based on general information—in other words, a gamble.

With data analytics and engineering management software, construction firms can avoid making important decisions based on guesswork or unreliable predictions. The software can be programmed to analyse all available information about each project and determine which one will yield the most profit. It can also determine potential contingencies that can increase your negotiating power.

2. Challenge estimates

Construction companies and subcontractors desire one thing—to deliver a satisfactory service without sacrificing profit. As such, subcontractors will try to make an offer that achieves just that and allow the main contractor to review it thoroughly. However, reviewing quotes or bids takes time, particularly if they involve complex unregulated accounting. And as previously explained, the longer the review takes, the higher the chance the results will no longer be up-to-date and reliable by the time they come out.

With data analytics, a construction company can compare data from multiple projects and distinguish patterns that allow them to hash out quotes better. The information and insights they provide are timeless or apply to a broader timeframe. In addition, they can accurately calculate the potential cost of services and compare it against the subcontractors’ estimates. If significant discrepancies emerge, the construction company can challenge the quote and ultimately get a better deal.

3. Monitor progress

One of the fundamental traits of a good construction project manager is responsiveness when risks are detected. Acting quickly to deal with potential onsite incidents helps prevent costly downtime and possibly extensive repair work on assets. And it’s not just the risks that a construction project manager must monitor, but the positive developments as well, as it’s an important consideration in how the project should move forward.

Analysing data collected and uploaded to your construction project management platform allows for early detection of potential hazards. It also shows where the company is losing or gaining money and what steps of the process need improvement. One of the best things about construction project management software is it creates an environment where everyone can be involved. This feature creates another layer of information and metrics—personnel’s comments and behaviours—to analyse and provide valuable insights.

A bright future for construction companies leveraging data analytics

Data analytics makes it easier for project engineers and managers to make decisions that contribute to the completion of each construction project. Risk management and asset protection are also more manageable if insights into the construction project’s success are readily available.

Construction and engineering businesses worldwide have seen a massive uptick in revenue linked to data analytics in recent years. With the trend in data-driven engineering and construction solutions looking positive, more and more companies are expected to jump on the bandwagon. Those with at least a digital system for compliance records or construction data management will find it easy to integrate data analytics into their system.

Want to find out how PlanRadar can help improve your data-driven project efficiency, gain insights and simplify your project workflows? Book a free 30-day PlanRadar trial or contact us to find out how.