data analytics construction

Data analytics in design and construction: from confusion to clarity and the data-driven future

If one word could summarize the nature of capital project delivery right now, it would be more. More complexity, more speed, more unpredictability. More pressure. Above all: more data analytics.

Data helps virtual design and construction (VDC) teams predict project risks and navigate change, which is especially vital in today’s fluctuating construction environment. As the volume of data generated on each project grows, it plays an increasingly important role in determining which endeavors succeed and which ones cost too much, take too long, and fail to result in an optimized facility.

The problem is that data, by itself, is inert. It’s an email from an equipment supplier, or a design tweak in Revit, or a progress update from the field staff. Before these disconnected inputs can drive coordinated action, that huge volume of data must be standardized, consolidated and analyzed.

This is the point in the article where you might expect a celebration of artificial intelligence and machine learning. Too often, though, these technologies—and the overall push toward facility digitalization—generate a lot of excitement while other variables that impact the future of design and construction analytics are overlooked, particularly the human factor. How members of a project team think about data, and how their thinking impacts the quality and availability of that data, can change the outcomes of data analytics at least as much as technologies do.

To get more from project data, get your team on board

We all know the old wisdom, “Garbage in, garbage out.” Even the smartest technologies cannot compensate for poor data quality at the source—or rather, the sources, plural, because quality issues very often start with too many data silos and not enough harmony between distributed workflows. Consider how many data sources are active across a typical project’s design and construction lifecycle:

BIM-Data
  • BIM data: This is the data that the architecture, design, and construction teams generate as they co-author the building design inside a shared digital environment.
Design-Data
  • Design data: This data complements and extends the BIM data, and could include decisions, changes, drawings, issue trackers, contracts, budgets, project timelines, and more.
Construction-Document
  • Construction documentation: Issue trackers, RFIs, submittals—outside of the building model, there’s a huge volume of data involved in moving a project forward.
Project-Mgmt-Data
  • Project management data: This includes scheduling documents, construction sequencing plans, jobsite updates, and much more.
Financial-Data
  • Financial data: The project’s target value and all of the details that contribute to its financial status are vital sources of data, particularly as design teams seek to resolve issues at the concept stage without driving cost overruns in downstream construction.

These data sources are like the components of an electric vehicle’s engine. Each one is vital, but until they’re assembled and functioning together in the right way, the current won’t flow and the wheels won’t turn. To get where you need to go, especially at the high speeds required in today’s competitive world, you don’t just need the right components—you need to use those components in the right way.

These data sources are like the components of an electric vehicle’s engine. Each one is vital, but until they’re assembled and functioning together in the right way, the current won’t flow and the wheels won’t turn.

In terms of data, that means integrating all of those various inputs into a single source of truth—in other words, building a central engine to drive your project forward. Getting there requires two important shifts, one in terms of the team’s mindset and the other in terms of the tools and practices in place to put that mindset into action.

Data-Driven Mindset vs Data-Driven Tools

A data-driven mindset

Good data practices start with team buy-in. That includes all members of the project team, not just those with the word “data” in their job title. It’s a small shift in attitude that can generate big wins, because when teams move from thinking of data management as someone else’s job and embrace it as a shared responsibility, they begin to seek out the good practices that are necessary for data-driven project delivery. They look for opportunities to link workflows between remote teams, move locally stored information into common databases, and catch bad data before it impacts progress.

It’s important to create conditions in which this mindset can thrive. This works best when you build an integrated delivery team from the start of a project, including trade partners who aren’t typically involved so far upstream. This unique approach has a practical advantage; it puts the skills and experience of a multifunctional team to work throughout a project, not just at key moments. But it also gives project leaders the opportunity to ensure that every team member is on the same page in terms of delivery approach and shared values, including values related to the integrity of data analytics.

It’s a good idea to codify these values in a project charter which, like the sheet music that guides an orchestra, gives all team members a common framework from which to proceed. Establishing these values and priorities so early in the project is a lot easier than trying to correct poor practices downstream, when the damage may already be done.

When teams move from thinking of data management as someone else’s job and embrace it as a shared responsibility, they begin to seek out the good practices that are necessary to data-driven project delivery.

Data-driven tools and practices

Of course, a willingness to share the responsibility of good data management is only half of the winning formula. For data-driven project teams to succeed, they need standardized practices for inputting, tagging, and tracking all of that data, and they need to support those practices with centralized, cloud-enabled data platforms.

Autodesk BIM 360 is a popular example of the technologies emerging today to meet that need. It integrates information from remote sources into a common data environment, including BIM data, RFIs, regulatory submittals, and other construction documentation, giving teams access to a single source of truth in real time. Through that common data environment, teams can build multidimensional visualizations that combine diverse data inputs to help drive faster decision-making—and they can make those decisions with confidence, knowing that their view of the project is accurate, up-to-date, and complete.

Most project teams understand the rewards of standardized data collection practices and common data environments, but not everyone is ready to get on board. Some potential partners haven’t invested in the right technologies, or they haven’t developed the “digital literacy” necessary to use those technologies to their best advantage. This will change as the risks of poor data management become impossible to downplay, but until then, project owners and team leaders should qualify potential partners according to their ability and willingness to adopt the industry’s best data management tools and practices.

Where will design and construction data analytics go next?

The benefits that await project teams with a strong data management strategy can sound both alluring and vague to those wondering what it looks like to “accelerate project outcomes” or “streamline decision making.” How, exactly, will a data-driven mindset and standardized practices extend the usefulness of BIM data, or enable deeper construction data analytics, or help project leaders coordinate operations between distributed teams?

To answer questions like these, consider three examples of how design and construction data practices are already shaping on-the-ground project delivery approaches (and imagine what these practices mean for the future):

1. Generative design

It’s not uncommon for project owners to require changes late in the design phase, when time is limited and pressure is growing. This might happen if, for example, there’s been a slight change in how a manufacturer makes, stores, or checks the quality of their products at any point in their process, which could set in motion a ripple effect impacting several rooms or spaces in the facility. Design changes may also happen as a result of fluctuating markets; a spike in demand, for example, could prompt an owner to hire more operators, necessitating additional change rooms in the facility layout. The architectural team might spend days or weeks drawing and evaluating possible solutions before arriving at the best way forward.

Generative Design

Using today’s predictive design technologies, teams don’t need to spend so much time finding the right solution. What if a processor, fed by historic project data and powered by machine learning, could rapidly auto-generate hundreds of viable layout options in response to a change? This demonstrates the virtuous cycle of good data management on a macro scale: it benefits not just the immediate project, but all future projects. A historic database of high-quality data means that project teams can ask questions like, “How did we solve this in the past?” and “What can we learn from that facility to help us optimize this one?”, and they can get answers almost instantly.

In our example, that means knowing exactly where to place those additional change rooms in order to maintain traffic flow and support efficient shift changes in the new facility. It means teams gain the ability to assess more design options, more quickly, with more data analytics available to identify the best solution.

2. Construction sequencing

When unpredictable issues interrupt a project’s momentum, delivery teams can find themselves scrambling to keep their projects on track. These issues could come from any direction: a design clash, a safety incident, a shortage of skilled workers, or a bottlenecked supply chain, to name just a few examples. Even teams that spend considerable time planning for potential roadblocks can find themselves impacted by unexpected events.

Construction Sequencing

Using BIM-based 4D sequencing, today’s teams can end the guesswork and prevent sequencing issues before they happen. Forward-thinking project teams are now leveraging 4D sequencing capabilities to accurately predict issues and keep projects on track, even under rapidly changing circumstances. That means using the right data to analyze the probability of specific issues and identify potential bottlenecks long before they become a risk.

3. Project turnover

In the past, design and construction teams worked on one side of a data divide, and the teams responsible for operating and maintaining a new facility worked on the other.

To bridge that chasm, teams have traditionally relied on the Construction-Operations Building Information Exchange (COBie), a standardized data structure that provides a pathway for knowledge transfer between construction and operations. But COBie is an imperfect tool; it requires delivery teams to “flatten” all the rich, multidimensional data that they’ve spent months or years collecting in their building information model, resulting in a two-dimensional database with limited usefulness for operations and maintenance teams. Turning that database into a meaningful data analytics resource can take a lot of time and money; many project owners elect to skip that step altogether.

Project Turnover

Today’s digital twin technologies are revolutionizing how construction and facility management teams exchange data. This is one of the most exciting innovations in project data and its applications. By capturing real-time data through sensors, scanners, AI technologies, and other inputs, advanced digital twin technology extends the functionality of the BIM model and gives project teams a clone of the real-world facility. That allows teams to share data across that delivery/operations chasm in 3D, giving the facility management team a whole new (and accurate) perspective on the space and equipment they’re responsible for.

If a three-dimensional data handover is already possible, imagine four, five, or six dimensions overlapping to present facility management teams with extremely detailed and immersive models. This is where advanced digital twins are changing the way facilities operate; project owners, under pressure to get the most from their capital investment, increasingly expect a fully digital, predictive facility from day one of operations. To meet that need, today’s project teams are turning to emerging technologies that leverage digital environments to drive better project outcomes, such as the Internet of Things (IoT), reality capture, augmented reality, and virtual reality. These technologies eliminate a lot of manual data work and contribute to a much more robust lifecycle management program right from the first day of operation, without a lengthy handover process.

Embrace the future of data analytics

More data doesn’t automatically mean more advantages for project teams. But it does mean that teams willing to shift their way of thinking have the potential to avoid the roadblocks built into traditional project delivery methodologies. By embracing the best, most innovative data practices and technologies, today’s integrated project delivery teams can get the insights they need exactly when they need them, which not only drives faster, more successful project outcomes during design and construction—it also means that project owners get smarter and more streamlined facilities that will maintain their value long into the future.

To find out how your next capital project can benefit from a data-driven project team, reach out to us.

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