5 Attributes of High Quality Construction Data
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.
In the construction industry, we’re no longer dealing with the issue of not having access to data, but instead with a lack of information to drive decision-making. What’s the difference? While there’s no shortage of data, many firms are still learning to use that data in an insightful way. The benefits of mastering this skill are plenty; they include having a stronger competitive advantage and greater project outcomes.
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 gathered 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 your current work and future jobs. Its predictive nature can create systemic inaccuracies down the line.
Why Quality Data Matters
All construction data is not created equal, and time spent gathering poor data is time lost. According to a new report from Autodesk and FMI, Harnessing the Data Advantage in Construction project data has grown exponentially — doubling in the last three years. Yet not all of that project data is created equally. Roughly half of the survey’s respondents shared that “bad” project data (e.g., inaccurate, incomplete, or inconsistent data) contributed to a poor outcome for one in three project decisions. What’s more, bad project data is costly. Avoiding rework triggered by bad project data could save the global construction industry over $88 billion annually.
How do you tell the difference between good and bad project data? Let’s take 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.
How to Spot Bad Construction Data: Siloed, Unreliable, Inaccessible
Bad project data doesn’t come down to a single reason. Research indicates that data management solutions and the challenges the industry faces vary and are unique to each organization’s way of working. In Harnessing the Data Advantage in Construction, the most common contributors to bad project data included:
Inaccurate/Incorrect data* (24%) Missing data* (24%) Wrong data* (21%)
*Please check out the report for definitions
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 by providing 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 something against.
As industry data expert Jit Kee Chin shared, 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.”
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.
Organizations committed to quality data typically share these three most common efforts or investments made to ensure decision-makers have access to actionable, high-quality data:
Regularly reviewing data at set intervals for quality purposes (40%) Having established data reporting and monitoring practices, both at the time of collection and use (38%) Structuring data in a common data environment (38%)
Just like clean job sites are integral to successful project outcomes,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 fine-tune 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
When working to improve your construction data, ask yourself, “Can your team see the data in real time? Can they access it across devices like mobile, and from remote locations?” These are two of the most common issues facing construction professionals when it comes to the transparency and accessibility of data.
Quality information should be accessible and transparent to reflect what is currently happening. Even something as seemingly innocuous as a one-day lag in accuracy can lead to immense setbacks for a project. Survey respondents from the report Harnessing the Data Advantage in Construction, shared that having access to data was essential to accurate project decision-making. When asked what the greatest risk was to project decision-making, 43% said “time constraint/urgency of decision”.
The quality of project data needs to improve if project leaders are to make critical decisions in the field quickly and autonomously. Accessibility is also important for 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.
Let’s say you have consistent, clean data that can be accessed universally across team members’ devices and locations. Oh, and that data transparently provides up-to-date information about a project. What more could you want?
Usability. Usability is a major factor in whether this data can actually be put to work to solve real problems you might face on the job. Good data can be used to inform work decisions as well as to solve both present and future issues that may arise on the job.
To help support your project staff over data management and analysis, make sure you have a formal data strategy in place. This framework will help to alleviate burdens on busy supervisory staff, and improve data consistency moving forward. Formal data strategies combined with data-rich environments could reduce the number of delayed or poor decisions, saving the industry $50 billion annually.
Furthermore, adopting solutions with advanced analytics and machine learning can provide insights for both today and the future that can 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 than ever are digitized, 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.
Download the Data Strategy checklist
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 a formal data strategy can make a huge difference when it comes to promoting a good project outcome, a happy team, and an efficient work process. What’s more, putting quality data standards in place through advanced analytics and other innovative construction technologies can set you up for success not just now but also in the future.
Start building managing your data more profitably with the 4-step process revealed in our latest report, Harnessing the Data Advantage in Construction, made in partnership with FMI. Download the data strategy checklist here.
The post 5 Attributes of High Quality Construction Data appeared first on Digital Builder.