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Architecture, engineering, and construction (AEC) firms can put accumulated data to work by starting with a specific decision—such as where to investigate a site risk, how to improve building comfort, or how people move through a space—and then collecting only the information needed to inform it. Data can improve design, construction safety, building operations, and workplace planning; it can also support new services. The essential step is connecting analysis to an action, rather than gathering information for its own sake.
The examples below come from a Building Design + Construction feature published December 4, 2019. Its firm projects, tools, and results are historical examples, not evidence of present-day adoption or current product capabilities.
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What does AEC data include?
AEC data is information generated or retained across the design, construction, and operation of buildings and infrastructure. It is not one uniform dataset: a building model, a record of equipment performance, a project risk log, and observations of people using a room describe different things and require different ways of interpreting them.
The 2019 Building Design + Construction feature groups construction-related information into three useful categories:
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- Static data: relatively fixed facts such as building dimensions and existing conditions. These can help teams understand a site or compare design options.
- Active data: changing information, such as the performance of mechanical systems. It can help investigate energy use, comfort, equipment scheduling, or system settings.
- Human-centric data: information about where people are and how they use space. Depending on the project, this can help with occupancy analysis, workplace planning, or circulation.
The categories are a practical lens, not rigid boundaries. A building’s dimensions may be stable, while its occupancy and temperature change throughout the day. The useful question is not simply “What data do we have?” but “What decision could this information improve, and will it arrive in time to matter?”
How can data improve design and project decisions?
Project and practice information can help teams answer questions earlier and replace assumptions with evidence. But analytics is useful only when the data is relevant to a decision that a project team can still influence.
The feature described Gensler launching an Intelligent Places practice and HMC Architects developing an internal data warehouse. These are examples of firms organizing data and analytical work within their practices at the time of publication; they do not establish what those firms or others currently offer. The broader opportunity is to learn from information that already exists across projects, rather than treating each new commission as if no prior work can inform it.
A presentation proposal from Leo A Daly reportedly stated that 90% of large design-firm practice data sat on servers and was never revisited. The feature does not state the proposal’s year, and the underlying proposal was not independently reviewed in the reporting summarized here. Treat the figure as an attributed historical illustration of dormant information—not as a current industry-wide measurement.
File sharing is one potential obstacle to making project information useful. The feature cited Newforma research involving 1,154 industry respondents and described project-team file sharing as a major challenge. It does not provide the research year or original report, so the number should be understood as a figure reported in that 2019 article, not a current statistic.
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A data warehouse or shared repository can make information easier to find, but storage alone does not create insight. A firm still needs consistent definitions, a way to judge whether records are reliable, and a decision or workflow that can use the result. If project teams cannot locate, interpret, or act on information, accumulating more of it may add work without improving the design.
Can project data help identify construction risks?
Yes. The historical examples in the feature show two complementary approaches: organize safety information so teams can respond to risk, and investigate known site uncertainties before they cause problems.
Suffolk Construction’s Risk X was described as a mobile, cloud-based safety-management tool that accommodated human input and metadata. The 2019 feature reported that Suffolk reduced its recordable and long-term incident rates by 40% over a 12-month period through jobsite data use. It does not identify the calendar year of that period or establish that the result would generalize to other contractors. This is a company outcome as reported by the publication, not a controlled estimate of data’s causal effect.
The example illustrates why people’s observations can matter alongside digital records. Safety information may include what workers report, what a team observes, and relevant project context. A system that captures information but does not help the responsible people recognize a risk and respond is not enough; the intended action must be clear.
Clark Construction’s approach began by identifying major risks at the start of a project and shaping data collection around them. The feature described gathering information about underground utilities and existing conditions, including laser scans and other sources. Where available information was not reliable enough, the team sometimes pursued further investigation. This is a useful distinction: analytics cannot make uncertain source records trustworthy. The right response to a potentially consequential gap may be to verify conditions, not to build a more elaborate analysis on top of weak inputs.
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How can building data support operations?
Information from HVAC and other building systems can help teams investigate comfort, energy use, equipment schedules, and system settings. The practical aim is to diagnose a condition or improve an operational choice—not to install sensors merely because more readings are possible.
The feature described several article-era examples. Gensler was studying connected sensors for occupancy, temperature, and energy in its New York office. WSP described a multisensory system for analyzing workplace utilization. DLR Group reported using data to identify building-system scheduling and economizer issues. The source does not name specific sensor models or vendors, and these examples should not be read as claims about current systems or capabilities.
Southland Industries’ museum project was reported to include 400 sensors intended to produce data that might later support wayfinding and crowd control. The number and purpose are project details reported in 2019; the feature does not state the year of the project or confirm that the anticipated uses were implemented. This distinction matters: a sensor deployment may create the possibility of an analysis, but it does not by itself demonstrate a benefit.
For an operations question, begin with the symptom or decision. If the concern is comfort, for example, relevant information may include temperature and system behavior, but a reading becomes useful only when it can be interpreted in context and linked to a possible adjustment. If the question is equipment scheduling, the team needs data capable of revealing whether schedules or settings are contributing to the issue. The feature’s examples support these use cases, but do not provide a universal sensor specification or a one-size-fits-all workflow.
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What can people-movement and workplace data reveal?
Movement and occupancy information can help designers and building teams examine how space is used, where circulation may be constrained, and what kinds of settings people need. The methods described in the feature range from interviews and surveys to access-card records, Bluetooth beacons, simulation, and computer vision.
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ZGF Architects used a questionnaire and qualitative research across 25 outdoor spaces in six cities, interviewing 276 people, according to the feature. The firm used its findings to create metrics and inform a courtyard design; the analysis influenced the courtyard’s location and shape. The article does not state the research year. This example shows that useful design evidence need not be limited to continuous sensor feeds: structured questions and qualitative observations can also inform a spatial decision.
For workplace planning, ZGF surveyed employees about work patterns and space needs. SmithGroup used Bluetooth beacons to understand movement in an agile office and found a need for smaller conference rooms. FXCollaborative analyzed access-card information and used pedestrian simulation to consider lobby circulation. These methods answer different questions. Employee surveys can capture stated needs; movement records can help show patterns of use; simulation can explore how a design might handle circulation. None should be treated as a universal substitute for the others.
Arup’s Mass Motion program was described as allowing users to import a virtual building design, such as a Revit model, and populate it with avatars to model movement. The feature also described Arup developing a computer-vision framework to better understand movement in particular spaces. Ennead Architects used a tool developed by Buro Happold to model movement through the University of Oregon’s Phil and Penny Knight Campus, with the goal of understanding where casual interactions might occur. These are examples from the 2019 feature, not current product-performance claims.
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People-related information also raises privacy questions. In the computer-vision example, algorithms ran on a Raspberry Pi processor next to a camera, processing counts in real time without storing video. That specific design illustrates one way to limit retention of identifiable footage; it is not a comprehensive privacy or security assessment. A project should still decide what it needs to count, what information it will retain, who can access it, and how people will be informed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should an AEC firm start a data project?
Start with the decision, not the technology. The feature’s recurring advice is to define the problem before collecting data, so teams do not become overwhelmed by information or drift into analysis that cannot change an outcome.
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- Name the decision or problem. Make it specific: investigate a site condition, understand a system issue, assess circulation, or plan a workplace. “Use big data” is not an operational goal.
- Identify the action the result could trigger. Decide who would act and what choices may be open to them. If no one can change anything based on the analysis, the project may not justify new data collection.
- Choose the relevant data and method. Consider whether the question calls for existing project records, building-system readings, surveys, scans, movement information, or a combination. Match the method to the question rather than assuming sensors or a warehouse are always needed.
- Check quality and gaps before analysis. Ask where information came from, how current it is, what it leaves out, and whether important records are dependable. The Clark Construction example shows that uncertain existing-conditions information may require additional investigation.
- Plan for access, integration, and privacy. Determine how data can be shared with the people doing the work, what systems or formats must connect, and what should be retained. For people-related data, set limits on collection and retention appropriate to the purpose.
- Define how value will be assessed. Set out what change would count as useful and how the project will judge it. The feature notes that firms may struggle to demonstrate a “soft ROI”; this makes it important to decide in advance what evidence would support a claim of value.
- Review the result and refine the approach. Compare what the analysis suggested with what decision-makers could actually do. Improve the data or method if needed, but do not mistake more collection for progress.
What can go wrong when firms use AEC data?
The main risks are not merely technical. Poorly focused collection, unreliable records, late information, privacy concerns, and unclear value can all prevent analysis from improving a project.
- Collecting too much without a question: a firehose of information can overwhelm teams and encourage unproductive analysis. Keep a clear link between the information, the decision, and a possible action.
- Trusting weak source data: historic records, including underground utility information, may be unreliable. Verify consequential gaps instead of treating a dataset as authoritative simply because it is stored digitally.
- Getting the answer too late: even sound analysis has limited value if the design, construction, or operating decision is already fixed. Check early whether information can be obtained in time to affect the choice.
- Assuming prediction removes uncertainty: the feature cautions that construction is uncertain and building environments are dynamic. Predictions can help people reason about possibilities, but they should not be presented as guarantees.
- Failing to connect analysis to responsibility: a finding must reach someone able to respond. Define who reviews it, what action is available, and how the decision will be revisited.
- Claiming a return that is hard to demonstrate: some benefits are difficult to quantify. Be explicit about what is measured, what is inferred, and what remains uncertain; do not turn an association or a company-reported result into a general causal claim.
- Overlooking privacy: people-movement information can be sensitive. Consider whether aggregate counts or less identifying methods meet the need, and define retention and access before collecting data.
Can AEC data create new revenue streams?
Potentially, but the 2019 feature does not establish current market size, current program availability, or a proven commercial model. It identifies the broader possibility that data-enabled capabilities could support new services, while emphasizing practical applications inside projects and buildings.
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What is the most useful takeaway for AEC teams?
Data becomes valuable when it helps a team make a better decision early enough to act. The examples in the 2019 feature—from risk investigation and safety management to building operations, daylighting, circulation, and workplace planning—show that no single dataset or tool is the answer. Start with a concrete problem, choose information that can address it, be candid about uncertainty, and decide how the result will change the work.
Frequently Asked Questions
Does an AEC firm need a data warehouse before it can use project data?
No. The examples in the 2019 feature include surveys, site investigation, system readings, and movement analysis, not only centralized repositories. A warehouse may help organize information across projects, but the first step is to identify a decision that existing or newly collected information can improve.
Can building data prove that a design is better?
Not by itself. Data can inform a design choice, as in the reported courtyard work, but a defensible claim depends on what was measured, how reliable the evidence is, and whether the outcome follows from the analysis. Distinguish a finding that informed a decision from proof that the decision caused a particular result.
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The feature describes one article-era example that processed counts locally on a Raspberry Pi beside a camera without storing video. That may reduce retention of footage, but a project still needs to set appropriate rules for notice, access, security, and any data it does retain.
Are the AEC tools and firm examples in this article current?
They are examples reported in a December 4, 2019 trade-publication feature. They explain possible applications, but they do not verify current vendor capabilities, availability, adoption, or results.
