How to Create Your Construction Data Strategy [Checklist]
In the construction industry, the abundance of data can be exciting or overwhelming depending on who you talk to. In a new construction report from Autodesk and FMI, Harnessing the Data Advantage in Construction, research shows that “bad” construction data is commonly associated with poor outcomes in project decisions. So, how much data do you really need?
The critical factor with data in construction is less tied to quantity and is more about quality. It’s whether or not it’s actually useful. And even then, if your data isn’t good, it doesn’t help you make real world decisions, especially at the field supervision or project manager levels. In spite of the industry’s growing reliance on construction technology, only 55% of report respondents stated that their organization has implemented a formal data strategy for their project data.
Further, our research also shows poor quality construction data, may have impacted over $1.8 trillion in global construction costs in 2020 alone. You can see the detailed cost breakdown in the complete report here
One of the best steps you can take to dramatically improve your effectiveness in managing projects is to create a formal data strategy. Aside from the benefits of strong data management, one of the most important takeaways to come out of this report is the actionable, 4-step process to implementing a data strategy. We share the high-level steps below, but encourage you to download the complete checklist for deeper insight.
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Quick Look at Benefits of a Data Strategy
To support data capture, management and analysis in your organization, start with a formal data strategy. This strategy will help eliminate burdens on the team tasked with managing data and increase consistency and accuracy.
In our report, Harnessing the Data Advantage in Construction, respondents who had a formal data strategy reported a much greater percent of usable project data. Other benefits included less rework, fewer missed schedules, and fewer project overruns.
The proof is in the numbers when it comes to formal data strategies. If you’re wondering what bad data means for you, consider that a contractor with $1 billion in annual revenue could have upwards of $165 Million in cost impacts from bad data, including $7.1 Million in avoidable rework. It’s clear that a better strategy for managing data can bring significant positive impacts to your organization.
Why Do So Many Firms Lack a Data Strategy?
With savings this significant, you might be wondering why more organizations haven’t created formal data strategies. There are several common obstacles that can deter companies of all sizes from implementing a strategy.
Among the respondents in Harnessing the Data Advantage in Construction, the top reasons for not creating a formal data strategy included not knowing how to start, the potential costs, and what resources will be required.
Lack of formal data training is another reason why so many firms feel hesitant to create a formal data strategy. Only 38% of respondents in our survey were provided with formal data management or data analysis training. Yet in contrast, 50% of respondents provide their staff with formal safety training. To increase investments in formal training tied to data, we’ll first have to change the perception of its importance.
The research uncovered that training is found at higher levels in organizations that have formal data management strategies (48%) and those that always or often incorporate data into decision-making (47%). Compare that to organizations without a formal data strategy, where training only occurs 27% of the time.
4 Steps to Building a Successful Data Strategy
It’s clear that getting started is often the hardest part of creating a data strategy. The volume of project data can feel overwhelming and add to feelings of stress. This four-step process will make implementing a data strategy easier and attainable for your organization. If you’d like deeper insight on the following steps, we encourage you to download the checklist.
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Step 1: Select a single point of focus
Where can your organization most benefit from data-driven insights? Look for the areas of your organization that can generate the most value. Doing so can help you achieve buy-in and ROI faster. Your immediate goal should be to prove that a data process improvement has clear value.
In our research with industry leaders, many began with quality assurance or safety. While this may not apply to all, this is an example of two areas that often have data readily available. Having this single point of focus will help your team prioritize and accelerate the impact of the solution.
Fred Meeske, Vice President at Rosendin explains, “For teams starting to build data standards and processes, it is important to keep two things in mind: first, build an interdisciplinary team that focuses on easily achievable goals. This will enable you to learn while still providing immediate value. More intricate questions with more effort and time requirements can have a bigger impact, but when starting, nothing beats the immediate impact. Once momentum is built, and the questions are well defined, the team can start tackling harder questions.”
Picking a focal point requires you to consider some important questions. What do you want to do better? What would look different if you were able to leverage your project data and generate insights? Specific questions like this, and more, will help you articulate your organization’s aspirations with data.
Step 2: Get employee buy-in to reduce hesitancy
You need buy-in from your project team and their involvement in the decision-making process. This will help to increase their engagement while minimizing resistance to change.
McKinstry, a specialty contractor in Seattle, Washington, leans on an internal Product Management Organization (PdMO) to achieve employee buy-in. The PMO team is in charge of implementing cutting-edge solutions for clients throughout the construction process. They connect the common needs of each line of business to the overall digital transformation process.
This internal department works on the success of these data standardization efforts. They follow best practices such as the diagnose before you prescribe approach, with a thorough discovery process to identify pain points, root causes, and impacts of not having data standards. “By qualifying and quantifying this pain in terms of business value, we can articulate and prioritize the need to standardize our data among other important process and technology improvements across our enterprise,” shares Dace Campbell, Director of Product Management in Construction at McKinstry.
Buy-in from all potential users is a requirement because the success or failure of a plan relies on the adoption by and execution of the frontline staff. Gathering feedback from end-users is one of three important components that organizations say help them achieve buy-in for their data strategies.
Step 3: Standardize your data capture across all projects
In our survey, we learned that over a third of respondents describe their organizations’ data as inaccurate, incomplete, and inconsistent. Yet only 36% had started a process for identifying bad data and repairing it. In fact, several industry leaders shared that it took up to two years to completely “clean” their data.
How do you prevent tedious and resource-heavy issues like this one? First, you need to standardize how data is captured. As you implement this process, examine how the data will be leveraged so it can be converted into insights.
Dr. Jad Chalhoub, Technology Solutions Implementation Lead at Rosendin describes the challenges of collecting standardized data as “figuring out what we want to use the data for, and subsequently what data needs to be collected and to what accuracy. Different types of applications require different tolerances and collection methods, so understanding the use case is extremely important.”
There are ways to increase the likelihood of company-wide adoption of data standards and data strategies. A few of those things fall under a simple commitment to resource allocation like money, people and tools.
Step 4: Keep project data in a common environment
We can’t understate how important a common data environment (CDE) is to your construction data strategy. In Harnessing the Data Advantage in Construction, leaders stressed the value of a connected construction environment, or complementary technology solutions with robust integration capabilities to facilitate the flow of project data. Bespoke customizations to the data environment hinder the access of future industry-wide analytics.
A single point of access to data improves the ease of reporting and gathering advanced data analytics. Dr. Chalhoub describes standardization as a tool; standardization techniques can be made to all collected data. However, that does not mean the data will be comparable. “There’s always another layer you can standardize, and it’s important to know when to stop and how to use the other tools in your toolbox,” he adds.
There are a few best practices on data quality that’ll give you a head start on your strategy. Arguably the most important one is ensuring that the data collected are accurate and in a standard format.
Take your first step towards a formal data strategy
Having a data management strategy can help improve efficiencies via better, and faster, data-backed decisions. A formal data strategy can also support efforts to improve decision-making, avoid rework, and prevent lost profits. Planning to use data effectively by committing to a formal data strategy is the way to not only remain competitive, but give yourself an edge.
Ready to start building a data strategy? Download our four-step checklist to get started today.
DOWNLOAD THE CHECKLIST
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