The words and numbers used to describe design concept, building component, and client goal form a dataset that guides decisions and improves project success.
Building on our previous discussion in “Embracing Data Democratization,” where we explored increasing access to data and cultivating data literacy, we now turn our attention to understanding the data sources we work with on a regular basis.
In today’s digital landscape, information is everywhere. When most people think of data, they likely imagine a deluge of numbers in a spreadsheet. However, data encompasses more than just numbers; qualitative insights are equally valuable. Spatial information, such as GIS data representing coordinates or polygons defining areas, is also beneficial. The true value emerges when we integrate all this data, quantifying qualitative aspects and qualifying quantitative ones. Examples include color-coded plans or maps with quantitative overlays displaying values like utilization rates, or qualitative overlays with information regarding space types. Both data types can be benchmarked to discover trends across past projects that inform future work.
With such an abundance of data, it can almost seem like an abstract concept. It’s helpful to remember data is synonymous with information. Therefore, it includes any details that describe characteristics associated with a building or its design intent, whether numbers or words.
A wall from multiple perspectives. Let’s consider a wall from multiple perspectives:
- Architect’s perspective. An architect sees a wall as a 3D element that appears as two lines in plan view or a rectangle in elevation, with a tag containing information about the wall type, indicating its composition, fire rating, and reference to construction details.
- Data perspective. From a data standpoint, that same wall has numeric values for length, height, and thickness. It has spatial coordinates for the start and end points relative to the origin and their position on the site. Boolean fields indicate the fire rating and whether it has an opening. String data specifies the stud type, and numeric data provides the stud size. Finish tags connect to the finish schedule, which holds a wealth of additional information. Metadata noting when and who edited the element is also available. By aggregating this information, we can determine the total number of walls, total linear feet, and surface area relative to material costs and construction schedule. We can also quantify the number of RFIs or the percentage of submittals received associated with that wall.
- Client or end user perspective. For the client or end user, the wall is a tangible object they interact with long after the architect’s file is closed. Their requirements inform the wall’s characteristics, such as STC (sound transmission class) ratings based on the function of the space, finishes associated with company standards, or program data that determines the wall’s length and which rooms are on either side of it.
Expanding applications. The wall example demonstrates how many different data points can be associated with a single element. Additionally, one piece of information can be analyzed from numerous perspectives based on the problems we are trying to solve. Given the breadth of data points from the wall example, consider how many other details could be associated with the following applications:
- Project requirements. Space types and sizes.
- Revit modeling. Embedded information in the model, enabling Revit to be used as a data source.
- Sustainability metrics. Reporting sustainable project outcomes and referencing resiliency data from other sources to inform projects.
- Products and materials. Databases of sustainable materials, equipment schedules, and specifications.
- Benchmarking metrics. Typical square footages, linear feet of bench in a lab, and cost per bed in student housing.
- User input. Collected via questionnaires or outreach events to understand top priorities for the users and the surrounding community.
- Economic and cultural context. Current landscape in the city and cultural references for where people gravitate toward and why.
These are just a few examples of data sources and analyses. You can quantify almost anything and glean valuable insights from it. Often, the most successful work results from reverse engineering the problem you are trying to solve, which helps identify the best data sources to use. This method maximizes the likelihood of achieving the desired outcome by pinpointing the specific data needed to guide design decisions. It is often beneficial to involve multiple parties in the data collection process, gathering data from the client, architect, contractor, and third parties to generate a comprehensive dataset.
Data integration at Hanbury. At Hanbury, this approach manifests in several ways:
- Planning. Asking the right questions and collecting applicable data from the client and users to inform decisions that better support the future goals of a campus.
- Higher education. Collecting data from current and past projects to answer client questions about the amount and types of common spaces and how they support wellness on campus.
- Science. Optimizing efficiency in lab design by using modular systems informed by fine-tuned metrics.
- Civic and community. Utilizing the client’s market analysis and considering the project ROI.
- Research. Working with academia and non-profits while collaborating with various professionals, including anthropologists, scientists, economic developers, environmental economists, social workers, and sociologists.
Designers often gather data through pre- and post-occupancy evaluations. Looking ahead, additional applications will continue to increase the integration of data in architecture, especially with the Internet of Things and digital twins. More data is becoming available via an increase in Software as a Service solutions for specific metrics within buildings, such as occupancy data. We can leverage technology to manage this increase in data at scale.
The challenge of data. While data is ubiquitous, good data can be hard to come by. Poor data quality is one of the biggest hurdles we face today and can be detrimental in a society that seeks data-driven decisions. The first step in mitigating this is being aware of the data sources being used and ensuring accurate data entry through standard practices, such as consistent Revit modeling procedures and naming conventions.
Data is a collaborative endeavor, where the words and numbers used to describe every design concept, building component, and client goal collectively form a dataset that guides decisions and ultimately improves project success.
Emily Gaines is a business analyst and strategist at Hanbury. Connect with her on LinkedIn.
