Google data center big data architecture
Google/Connie Zhou Google's data center in The Dalles, Ore., sprawls along the banks of the Columbia River.

Data is one of the biggest byproducts of the 21st century. Almost everything we do produces data, from swiping credit cards to emailing, "liking" photos on Facebook, and requesting directions in Google Maps. Meanwhile, an increasing number of gadgets in the built environment, such as thermostats and refrigerators, are bolstering the Internet of Things and relaying the data that they gather.

Some organizations have attempted to quantify how much data is produced daily (see examples by Domo, IBM, and EMC Corp.), but the rate is growing so quickly that most estimates are obsolete. Suffice it to say, our production of data is exploding.

375 Pearl Street New York
Martin Dürrschnabel via Creative Commons license The tower at 375 Pearl St., New York, is now Intergate.Manhattan, a purpose-built data center campus by Sabey Data Centers.

Data has even manifested a physical presence. In New York, a new type of architecture is emerging in which large skyscrapers, such as 375 Pearl Street (commonly known as the Verizon Building), are being retrofitted into digital warehouses that accommodate computers rather than people. Similar buildings are popping up across the United States for the purpose of storing and analyzing data. These highly secured, windowless, and climate-controlled repositories are filled with our cat photos, banking transactions, and drunken text messages, not to mention all that data from the built environment.

Increasingly, the value of a business is tied to its ability to mine data. This is obvious in the tech industry, where companies like Google and Facebook have made billions from understanding the data produced by their users. But the abundance of data affects nearly every other industry as well. Even activities synonymous with intuition and dexterity, such as baseball, have been transformed by Moneyball-style data analysis.

In the field of architecture, data is having a similar impact. Not much has been written about these changes because, I suspect, architectural critics tend to frame technological developments as stylistic and philosophical transitions. From this perspective, the influence of data has failed to stand out against the far more conspicuous clichés of curvaceous facades and continental philosophies.

But when one examines what is occurring in practice, it becomes evident that data is changing architecture in the following three ways:

1. Clients are demanding data from architects
Clients are starting to ask architects to deliver more than just drawing sets. They are eyeing the data-rich BIM models that firms use to document projects as a way to supply data for downstream applications, such as facilities management.

With BIM achieving some level of maturity within the industry, there is a growing expectation that architects will produce datasets, such as the COBie (Construction-Operations Building Information Exchange) spreadsheet, as part of their regular deliverables. The COBie spreadsheet is essentially a list of building assets—such as chairs and HVAC systems—that the owner can then use to manage the facility. Next year, the U.K. government will require architects working on any publicly funded project to produce COBie spreadsheets. For architects, this means that their data needs to be as rigorous as their drawings.

Google server racks big data and architecture
Google/Connie Zhou Server racks inside Google's data center in Mayes County, Okla.

2. Clients are demanding data from buildings

Clients have also become interested in the data generated by the buildings. As previously mentioned, everything from thermostats to doors is being connected to the Internet so it can broadcast its use. At last year’s Venice Biennale, the exhibition's director Rem Koolhaas, Hon. FAIA, predicted that “every architectural element is about to associate itself with data-driven technology.”

This data enables building owners to measure and improve their facilities’ performance quantitatively. Many are already doing this—albeit in a limited sense—with their HVAC systems. But what we are seeing from innovative building owners is the use of data to conduct a holistic assessment of their performance. The Walt Disney Co., for example, combines location tracking with sales data and other user-experience metrics to optimize the performance of its parks. As more owners come to rely on building data to improve the performance of their assets, architects need to ensure that their buildings can supply this critical data.

Architects also need to recognize that clients are going to use this data to measure their own performance. Sustainable-building certification organizations, such as the U.S. Green Building Council, are already making moves to verify building performance using actual data (see LEED’s Dynamic Plaque).

Performance-based contracts, where a portion of an architect’s fee is withheld until post-occupancy data validates its prescribed design performance, are also gaining popularity. Captivating visualizations soon will not be enough to sell a project; firms will need to show that they can back up their predictions with real data.

Big data architecture Manhattan Morphocode
Morphocode Snapshot from Urban Layers, an interactive map of Midtown Manhattan's building stock color-coded by age, developed by the architecture firm Morphocode

3. Data is changing the process as much as it is changes the output

The abundance of data may give rise to data warehouses and COBie spreadsheets, but the much more profound changes for architects will be procedural. For instance, using BIM to design and document a building has required a whole new set of business processes. The building might be visually similar to what would have been designed in the past, but everything behind the scenes, from contract wording to staff training, needs to be rethought.

If architects are to harness data from the built environment, even more significant procedural changes may be coming. How will firms verify the data they produce? How will they exchange data with project partners? Legally, who will be responsible for this data? What services can be sold around this data? How can firms learn from data? Will firms need to employ a data scientist?

For the firms that are willing to tackle these questions, data will afford a profound opportunity to quantify the value they are bringing to their clients. After all, the clients are demanding it.