The quality of construction data matters. Here’s a look at five characteristics of good data, how to avoid some common pitfalls that lead to bad data, and how to tell the difference.
Good project outcomes stem from good decision making, and nothing affects the decisions you make on a construction job like the quality of your data. The information you gather can have an impact on everything from timelines to budgets, bid performance, and even site safety. What’s more, using bad data over the course of a project has the potential to affect not just your current work but future jobs, as its predictive nature can create systemic inaccuracies down the line.
Indeed, all construction data is not created equal, and time spent gathering poor data is time lost. According to FMI, 96% of all construction industry data captured today is not being used.
How to Spot Bad Construction Data: Siloed, Unreliable, Inaccessible
According to a report from Autodesk and FMI, “Poor project data and miscommunication on projects is responsible for 48% of all rework in construction in the U.S., meaning that it will account for a total of $31.3 billion in rework in the U.S. alone in 2018.”
While it’s not always easy to spot the difference between good and bad data, there are a number of key attributes of bad data that can help construction professionals avoid using it in the first place.
For one thing, bad data is siloed, meaning there’s a disconnect between the systems used to access the data, and the possibility that not everyone is on the same page regarding which data is most reliable and relevant to the project at hand. Unreliability is another characteristic of bad data, and professionals can spot it by ensuring that data is not outdated and doesn’t contain mistakes. Finally, poor quality data is difficult to access, making it hard to pull up relevant project information.
5 Qualities to Look for in Construction Data
Beyond identifying poor quality data, construction professionals must understand the characteristics of high quality data. Doing so not only helps to avoid wasted time but it also sets projects up for success with as many resources as possible. So what characteristics make for good construction data? Read on to learn more about the five attributes of quality construction data and a few resources to help you collect and use it.
The nature of data captured by the construction industry is often what is considered “heterogeneous” data, or data that has multiple variable types, e.g., comparing apples to oranges. This type of data is ambiguous and inconsistent in the ways it measures something and what it measures that thing against. As one industry data expert put it, construction professionals must gather “a lot of information across contracts, across text documents, across drawings and across financial information. So, the challenge in construction data is heterogeneity in terms of the data that we historically collect.”
But with the variety of data formats available in construction, how is it possible to maintain consistency? It all starts with how data is collected. Consistent data requires collecting insights in a uniform way like adopting a common data environment, which helps create a standard platform to capture data. A common data environment typically takes the form of a digital hub, where all information comes together during a building project. Any information gathered for or about a project during any part of the process should be stored in the common data environment to ensure the consistency and accuracy of all project data.
Just like clean jobsites are integral to successful project outcomes, especially in the COVID-19 era, clean data is vital to ensuring the information you’re relying on is as up-to-date and accurate as possible. In fact, data cleansing—the process of reviewing all project data and eliminating data that is not currently relevant or accurate—often leaves construction professionals with only the best quality data to work with, thus elevating the likelihood of the successful completion of a job. In contrast, data that is not clean creates increased opportunities for mistakes and rework, as well as wastes professionals’ time when they must go back and search for correct information.
The first step toward achieving clean data is to finetune your information collection and management processes. Examining vital tasks like data entry, including how and where information is entered into a common data environment, and the controls around what information is considered clean data, can go a long way to help ensure the timeliness and accuracy of the data used in a project.
3. Transparency and accessibility
Can your team see data in real time? Can they access it across devices like mobile, and from remote locations? These are just a few issues facing construction professionals when it comes to the transparency and accessibility of data. Quality information should be accessible and transparent to reflect exactly what is currently happening. Even something as seemingly innocuous as a one-day lag in accuracy can lead to immense setbacks for a project. Accessibility is also extremely important regarding distributed teams, especially those out in the field. The ability for a team to obtain quality data across devices and locations is essential to the success of a job.
To improve the transparency and accessibility of quality data on a project, industry professionals should consider adopting connected and cloud-based construction technologies that ensure project information is always up-to-date, accurate, and accessible across devices, locations, and project phases.
So you have consistent, clean data that can be accessed universally across team members’ devices and locations, and that transparently provides up-to-date information about a project—what more could you want? Usability is a major factor in whether this data that you’ve worked hard to ensure is of the highest quality can actually be put to work to solve real problems you might face on the job. Good data is information that can be used to inform work decisions for both today and tomorrow, and can solve both present and future issues that may arise over the course of a job.
Adopting solutions that include advanced analytics and machine learning can provide insights for today and the future to improve project outcomes. According to McKinsey & Company, quality data “increases in usefulness and generates a competitive advantage as it increases in analytical richness” or put another way, data that does the work of enhancing the quality of available information in the most efficient way possible—on its own. Moreover, companies that use machine learning and other advanced tools like predictive analytics and simulation modeling are best positioned to make the most effective data-driven decisions throughout the entirety of a project.
The final attribute of good construction data is connectivity—ensuring that information does not live in silos and shares a common access point among team members. Most projects involve a constant flow of information that originates from multiple stakeholders and takes a variety of formats. Back in the days of paper documentation, data connectivity was nearly impossible, and miscommunication was common. Even now, when more projects are digitized than ever, construction professionals are facing connectivity issues regarding the data they gather and use during a project.
To avoid the risk of siloed data, which can lead to communication issues, all project information systems must interoperate, with common access to critical information and documentation across the entire workstream. One way to achieve this is through integrated construction technology, which helps different data systems communicate and work together. This integrated approach to data is vital to connecting and automating workflows to improve project efficiency.
Don’t Settle for Less Than High Quality Data
High quality construction data can save time, improve teamwork, and greatly contribute to a project’s overall success. Spotting the difference between good and poor quality data, and ensuring that the information you’re using for a project is consistent, clean, transparent, accessible, usable, and connected might sound like a heavy lift. Nevertheless, adopting and deploying these data-driven construction strategies can be a huge difference-maker when it comes to a good project outcome, a happy team, and an efficient work process. What’s more, putting quality data standards in pace through advanced analytics and other innovative construction technologies can set you up for success not just now but in the future.