Data-Driven Design and ConstructionAECbytes Book Review (March 24, 2016)
As computing becomes more and more pervasive in society and we increasingly rely on our computers, tablets, and smartphones to live—not just professionally, but also in our personal lives—there is little doubt that we are also seeing a corresponding increase in the amount of data that is being generated. Commonly referred to as “big data,” it is like an expanding mushroom cloud that, if not properly dealt with, can engulf us to the point where we are not able to function. Fortunately, technologies are also being developed to “deal” with this data, to parse it and make it more meaningful and manageable. It is hardly surprising that database companies like Oracle and SAP are among the most successful technology companies in the world.
In the AEC industry, we haven’t reached the point yet where we are deluged with data, but with the increased adoption of BIM and the recognition that it is the “I” or the information in BIM that is paramount, we will eventually get to the point where we have a surfeit of data but don’t know how to use it properly. However, if we are pro-active and learn how to harness this data now to design and construct more effectively, we can not only prevent the data problem from getting out of hand in the future, but can also actually use it to improve the efficiency as well as the quality of our work. More effective use of data is the basic premise of the book Data-Driven Design and Construction, authored by Randy Deutsch and published by Wiley last October. Let’s explore the book to see what light it can shed on how data can be used more effectively for building design and construction.
About the Book
The author of Data-Driven Design and Construction, Randy Deutsch, has impressive credentials, both in academia and in the industry. He is currently an Associate Professor in the School of Architecture at the University of Illinois at Urbana-Champaign and is also associated with the Harvard Graduate School of Design where he leads an Executive Education program. This is paired with practical industry experience working as an architect for several years on complex building projects. He is a strong advocate of the use of BIM and other computational tools in AEC practice, which he has written about in several articles and also at length in an earlier book on BIM and integrated design, also published by Wiley. In this book, he draws upon his work in academia and the industry to ponder on the question of data, why it is important, and how we can best use it to improve design and construction in the AEC industry.
While Data-Driven Design and Construction is not really intended to be a “how-to” book, it does attempt to present some practical tips for the effective use of data, as evidenced by the subtitle of the book which is “25 Strategies for Capturing, Analyzing and Applying Building Data.” It is also liberally strewn with lengthy interviews of industry executives from leading AEC firms such as SOM, HOK, HDR, Thornton Tomasetti, and others, some of which make a compelling read in and of themselves. The Foreword of this book is from another well-known personality in the AEC industry—James Timberlake of KieranTimberlake Associates, co-author of the seminal book “Refabricating Architecture.” All in all, Data-Driven Design and Construction can be considered a heavyweight book, in the line of similar AEC technology books published by Wiley starting with The BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors (the first edition of which was reviewed in AECbytes in 2008), BIM for Facility Managers (reviewed in the Q3 2014 issue of AECbytes Magazine), and more recently, Building Information Modeling: BIM in Current and Future Practice (reviewed in AECbytes last year).
The substantial content of Data-Driven Design and Construction—including the 25 strategies and extensive case study interviews—is divided into three parts: the first looks at exactly what data is in the context of building industry and why it is important; the second part explores where the data can be found and how it is being used; and finally, the third part looks at the applications for data use throughout the project lifecycle, particularly in construction, facilities management, and operation.
With regard to what data is, while the book does not weigh in conclusively on whether “data” is different from “information” and even if there is really such a thing as “big data,” it does emphasize that in the context of buildings, data comprises all that can be known about the proposed design: the properties of each of its components, both geometric and non-geometric, and any relevant information about the building type, the location, the weather, local codes, and so on, that can impact the design. While 2D documents can also be considered as data, the increasing use of BIM and its ability to capture the properties of a design in depth has given a jumpstart to our thinking of building as data. As to why data is important, the argument for that is presented more unequivocally—it can make design more informed, hopefully improving its quality, and allow designers to better validate and justify their decisions. Clients can be presented with hard facts as evidence which can make proposals a lot more compelling.
While BIM eventually becomes the source for capturing the data of the single building it represents, what is needed for a more informed design is access to all the relevant data that can impact the design of the building—before it comes into existence and becomes a contributor of data. All design firms have access to public data sources maintained by the government such as GIS data, weather data, economic data, etc., as well as private data sources such their own databases, their clients’ databases, or data that is available for purchase. Data for design can also be captured from sources in the field such as sensors, scanners, and drones. For all of this collected data to actually impact the design, it needs to be analyzed, and this is typically done by firms using custom in-house tools as well as different commercially available analysis tools for aspects such as energy, daylighting, cost, structure, schedule, operational performance, and so on. All these analytics, in turn, can be directly applied to guiding the design and justifying the different decisions made about it, from basic questions such as the orientation and shape of the overall building, to more specific aspects such as the type of glazing and shading device selected for a particular room.
Once a building is designed, it yields a rich treasure trove of data that can be extracted, mined, and used downstream in construction, facilities management, and operation. The actual construction process provides its own set of data points that can be tracked to measure the construction progress against the planned schedule, as well as different aspects of the site such as logistics, inventory, and safety. Once the building is completed, the as-built data again becomes invaluable for operations and maintenance, and even though this phase is not well-served by technology yet, the data will be available once the technology catches up. In all these post-design phases of a building, data interoperability becomes critical, highlighting the importance of open standards such as IFC and COBie.
Data-Driven Design and Construction is an extensively researched book on an aspect of building design and construction that may seem dry and not particularly exciting, but which actually underlies everything we do. Not only do we need to effectively use data—and a lot of it—to design and construct a building, the building itself will also generate, after construction, a copious amount of data that can be mined to operate it more efficiently and which then itself goes into the larger pool of data impacting future designs. The book does a good job of making AEC professionals conscious of how much the aspect of data permeates their work and why it is important. By focusing upfront on data and mining useful information from it, not only can we improve the design and construction process but also the product.
At the same time, the book candidly acknowledges that this is not a new movement or trend in the industry—data has been shaping architecture and planning for generations, but we have not been conscious of it until now. The difference is that sophisticated database technologies are now available to store, manage, parse, and search data, making it possible to quickly get responses to any design-related query we might have. No longer is building data just going into a black hole—it can be used to not only make better decisions but also show exactly why we made them. This is a big change for AEC professionals in their thinking and mindset, perhaps even more than BIM since it is more abstract than focused on building geometry.
On the flip side, while no one can argue with the fact that a more informed decision is always better, I did find that Data-Driven Design and Construction carried a somewhatexaggerated notion of the importance of data, arguing that data-driven design and construction could save the AEC industry from languishing, make firms more competitive, give architects a renewed sense of purpose, and rebuild the industry’s credibility in the eyes of building owners. The ability to mine and apply data, however, is just one additional tool in our arsenal—a powerful one no doubt—and we need a lot more of them, not to mention continued human ingenuity and creative thinking, to improve the quality of our buildings and how we go about building and designing them.
Also, while Data-Driven Design and Construction includeda lot of lengthy interviewswith practitioners and researchers who were using data in cutting-edge ways, I was disappointed to not find any case studies of actual projects designed using this approach, demonstrating exactly how data was effectively used and how it had made a difference. How can you tell data-driven design and construction from non data-driven design and construction? Is there a noticeable difference? It would have been useful to get some more insight on this. Also, while there was an acknowledgement that the AEC industry has a lot to learn from other industries on how to use data, there wasn’t any further discussion of this. Again, it would have been informative to know how other industries are using data and the difference it has made to them.
Some other aspects of the book that could have been improved include making it more action-oriented with specific takeaways for practitioners who would like to make their work more data-driven. While the book does compile 25 strategies as it indicated in its subtitle, I did not find them to be very instructional or insightful. Also, I found an over-reliance on the ideas and opinions of some of the interviewed people in the book—they kept getting quoted numerous times throughout the text. Often, this also led to a one-sided perspective—for example, the energy analysis tool vendor, Sefaira (recently acquired by Trimble), was extensively quoted and illustrated, but no other software vendor was represented. It would have been interesting to also know their take on data as it relates to their software and how their users were taking advantage of it.
While the term data-driven is not specific to AEC (see https://en.wikipedia.org/wiki/Data-driven), the main message of Data-Driven Design and Construction to AEC professionals—to be aware of how much of their work uses and creates data, and to learn to use it effectively to improve how they work as well as the quality of the end product—is one they need to hear. The book is packed with information about data and extensive interviews with industry movers and shakers who are using it in cutting edge ways, yet it is a relatively easy—albeit lengthy—read. Those looking for a “cheat sheet” on how to use data in AEC will not find it here; rather, it is ended for those who would like to have a more comprehensive understanding of data as it applies to our industry.
As to the question of how we can avoid the debilitating “analysis paralysis” of data overload that I referred to in the introduction, it does seem like, fortunately, we are not there yet and can cross that bridge when we get to it.
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