What’s driving the need to be especially mindful of data intelligence right now? Workforce disruptions abound, from the work from home (WFH) phenomenon, dislocations due to robotics and artificial intelligence, and even some movement to redefine the relationship between workers and company interests (see for example a recent Deloitte paper, From survive to thrive: The future of work in a post-pandemic world, 2021). Workforce and industry disruptions are also felt in the real estate market, which is further complicated by climate change impacts, the sometimes severe measures to counter housing unaffordability, shifts in consumer behavior (some made more profound by Covid-19) that affect both retail and distribution space, along with general growth and increasing scarcity of land in some markets. Corporate investors are also becoming increasingly sensitive to accounting for risk associated with exposure to climate change and other economic disruptions, such as those associated with technological innovation (for example autonomous vehicles), labor demand and locational shifts, etc.
What objectives can timely data accomplish? WFH worker relocation patterns can be tracked. Areas of vulnerability to industry changes driven by various factors can be identified and gauged in terms of economic, demographic, and physical hazard and related conditions. This kind of information can all be used to show regional shifts in relative competitive advantages/disadvantages based on the distribution of impacts.
Most of the examples cited above have spatial as well as numerical components, so data mapping is a critical component in compiling as well as illustrating the data.
What use will data clients make of this information? Entities will strategize to take advantage of opportunities and to avoid or mitigate shocks to their systems, through investment practices, goals and targeting, and other policies. As has always been the case, good information helps officials fulfill their managerial obligations.
Information is never neutral, and the data elements mentioned as examples above can be very sensitive and must be packaged as such, including, for example, having data providers carefully distinguish between facts, projections, speculations, and opinions. Because the trendlines of some of these data elements are not yet historically established (let alone projected with confidence), some degree of uncertainty in working with them is inevitable. Nevertheless, the cost of being forewarned is most likely to be minuscule compared to the alternative.
