For the 4th Season of arcCA DIGEST, dedicated to Data, we asked experts in this realm to identify emerging issues, problems, opportunities, circumstances, etc., to which they believe architects should be alert. These are their replies.
In 2018 and 2019, Autodesk sponsored a series of six workshops around the globe to explore the relationship between emergent digital collaboration technologies and the challenges of improving the delivery of the built environment. Phil Bernstein, FAIA, who was formerly a vice president at Autodesk and is now a consulting fellow there, as well as Associate Dean of the Yale School of Architecture, participated in these workshops and subsequently penned a series of articles for ARCHITECT magazine, summarizing the observations and insights of this collective effort, and outlining the most important ideas that arose consistently across the six sessions. The following remarks are condensed from these articles.
To engender team, firm, project, and industry success, information systems that support project delivery must overcome not only technical but also procedural and cultural roadblocks. Technically, an obvious characteristic of information and data exchange within the AECO enterprise is transactional incompatibility. Projects are delivered with a heady mixture of various tools, standards, data types, and formats, none of which are vaguely compatible.
This disaggregation mirrors the structure and protocols of the industry itself. Even if technology were seamless, industry processes for transparent, trustable interaction are far from the norm. Delivery models are based on mutual suspicion and inequitable distribution of risk, as well as lowest first-cost, commoditized compensation structures. Rewards for doing well are limited, while punishments for failure are manifold.
Perhaps most challenging are the underlying cultural inhibitors to trustable data. Industry professionals are trained in silos, rarely interacting with other disciplines until their first project, and prioritize different objectives for that work. Rather than combining their insight to mutual benefit and reward, the players bludgeon each other to protect design, schedule, and budget interests. Clients either don’t understand the attendant risk, or they use disproportionate bargaining power to shed that risk onto their project teams. Trust is almost never part of the equation.
Yet information is a vector for trust. An industry aligned around building effectively—and being rewarded well for doing so—would necessarily work from refactored, digitally integrated, transparent project delivery models based on a new reliance on information. Data environments that can engender that trust are accessible, secure, and, most importantly, transparent.
Nick Carter is founder and CEO of IngeniousIO, developer of a cloud-based platform for integrating project management and business management for the AECO industry.
From an AI perspective, the most interesting thing that’s changing in the AECO world is the shift from passing PDFs back and forth to applying and sharing contextualized data. This shift changes the way buildings are designed and projects are executed; it will also change how architectural practices are structured. What connects the two are changes in resource utilization. Successful businesses adapt: when one service is reduced by improved technical capabilities, they figure out ways to capture other revenues from other areas of expertise. If the external data (design and construction documents, construction administration, etc.) and the internal data (fees, staffing, etc.) are integrated, you can use AI to analyze the impact of technological changes on your core business.
Myoung Kang is a Bay Area designer, maker, technologist and entrepreneur. Prior to receiving her M. Arch. from California College of the Arts and working as a designer at an award-winning architectural firm in San Francisco, she worked extensively as a software engineer on large enterprise and government projects focusing on data warehousing applications, methodology, and processes.
If architecture doesn’t start using data to inform design practice, it’s passing up not only an important opportunity to create more efficient processes and buildings but also opportunities to provide clients additional services post-building construction. Data is powerful, and BIM tools are essentially databases containing large data sets that can be extracted to provide multitudes of operational and maintenance capabilities. Emerging technology such as augmented reality and all of the infrastructural tech such as SLAM, gaming engines, and AI, to name a few, are laying down the foundation for applications in the AEC industry, which is ripe for disruption.
Andrea Love, AIA, LEED Fellow, is a principal and director of building science at Payette, winner of the 2019 AIA Firm Award.
We live in a world where data is becoming our currency. Architectural design has started to embrace data to inform design outcomes, from programmatic data to building performance and everything in between. We can design with the data to create buildings whose architectural beauty and expression are derived from their performance. And, when expression is so integrally connected to performance, it can better withstand the onslaught of “value engineering.” For example, for the Northeastern University Interdisciplinary Science and Engineering Complex, we worked parametrically with programmatic and energy performance data to develop the building’s dynamic form, optimizing shading depth and spacing to decrease peak solar radiation while minimizing glare and maintaining comfortable daylight. When the sunshades were targeted to be cut by a third in value engineering, the team was able to run the proposed alternative and demonstrate its impact on comfort and energy use. The analysis showed that a reduction in sunshades would increase the peak cooling loads and the size of cooling equipment, leading to higher, not lower cost. We were able to use the data not only to optimize the design, but also to protect the design intent.
Zigmund (Zig) Rubel, FAIA, is the President and CEO of A Design+Consulting, a firm specializing in data-driven processes to plan, design, and construct healing, learning and discovering buildings. The firm provides design services, operational consulting and technology strategy definition for its clients.
Look at trends. The focus of my practice is healthcare, which is constantly changing, as are many sectors. If you can follow where data is leading you, then you’re able to future-ready your designs. As Wayne Gretzky said, “Skate to where the puck is going, not where it has been.”
Discern the signal from the noise. Not all data is equal. Distinguish the significant indicators from supporting or irrelevant indicators. Ensure that all the project stakeholders are in agreement on the weighting of the data, as this weighting should help you choose what your solution is.
No decision is final until all decisions are made. With data, you have the ability to explore a large set of questions. It is important that all of the important questions are answered before you decide which path the team should go down. Similar to LEAN and the Last Responsible Moment, a data-centric approach supports more analysis to ensure the outcome meets the desired requirements.
Kyle Steinfeld is an Associate Professor of Architecture at UC Berkeley. In his academic and scholarly work, he seeks to illuminate the dynamic relationship between the creative practice of design and computational design methods, thereby enabling a more inventive, informed, responsive, and responsible practice of architecture.
Architects have long valued tools not only for their ability to support design documentation and collaboration, but also for their creative capacities: how technology can help get novel design ideas out of the mind of an author and out into the world. Historically, our expectations of digital tools have been no exception. From the outset, computational design tools were intended not only as effective instruments for documenting our ideas and communicating them with others, but also as full-fledged creative assistants and “partners” in design. While, throughout its sixty year history, computational design has largely failed to live up to this promise, recent developments suggest that this might be changing.
Looking ahead to emerging trends in the relationship of data and design, we find the peril we rightfully hear so much about, but also opportunity. From my position, I am Interested to see if data-driven techniques such as machine learning can not only assist with documentation and communication tasks, but also fulfill a central promise of computing in design: to augment the creative process of architects.
