Big Data in Office Buildings Holds Promise Despite Privacy Worries

The Office Property and Big Data Puzzle: Putting the Pieces Together (August 2018)

Excerpts from the Executive Summary

Big data is defined as high-volume, high-variety and high-velocity information that is produced in either structured (e.g., predictable formats such as sensor data) or unstructured (e.g., pictures, text) formats. The sheer influx of big data can be overwhelming for many companies; they often choose to sit on the data they collect with no concrete plans to use it. Therefore, some firms, particularly those without the resources to sift through large amounts of data, risk missing valuable information that could improve their bottom line and position them favorably in an increasingly competitive market.

Although much of the big data now being collected by office landlords fails to trigger any privacy issues (e.g., building systems data), disclosure and permission are advised in some instances, such as cases where a landlord is monitoring tenant movements using Wi-Fi. Because of these complex issues surrounding personal data, landlords and tenants should approach data collection with a clear understanding of privacy laws and a great deal of transparency.

In regard to office properties, big data’s usefulness can be categorized into two interrelated areas: 1) how big data improves a building’s operational efficiencies; and 2) how landlords can use big data effectively to attract and retain tenants. To gain a deeper perspective on this topic, the author questioned seven office property management professionals — representing a real estate services company in Minneapolis and a development company in Dallas — to find out if and how they collect and analyze big data in their buildings. Specifically, were they using big data to improve operational efficiencies, attract tenants or both?

The author’s conversations with property managers confirm literature and media accounts of how the large amounts of digital data generated within office buildings are used: primarily for analyzing building systems and improving operational efficiencies. The conversations indicate that there is interest in using Wi-Fi, beacons and sensors for: 1) tracking where people go and gather in buildings to improve the type and location of amenities in office buildings; 2) allowing tenants to more efficiently track and manage their own energy use; and 3) providing building navigation through smartphones. However, privacy issues and data management are obstacles that have hindered widespread collection of tenant data.

Several critical takeaways presented in this report deserve the attention of the real estate industry in general and the office sector in particular:

• Big data sets are more than just big. They are dynamic and multidimensional and can be challenging to work with, but they promise to give greater insight into some of the fundamental questions of real estate more than anything has before.
• The concept of big data is not solely about the data; it is also about the tools created to deal with the data. The collection, storage, analysis and visualization all present unique challenges that require innovative and ongoing solutions.
• Small data is still important. Real estate markets are local: to make big data meaningful, sometimes the data need to be selected and sorted to such an extent that they are anything but big.
• Landlords and tenants must approach data collection with a clear understanding of privacy laws and a great deal of transparency. Personal information should not be collected or, at the very least, records should be anonymized. Data should be released only in the aggregate, if possible, and systems should be put in place to ensure the security of the data.
• Big data is spurring new technologies and disciplines that affect the real estate industry. For example, blockchain technology will have an increasingly larger role in data management and property transactions. The need for job positions such as data scientists, data stewards and data visualizers will continue to grow as companies take stock of their data sets.