There’s no running from Big Data—Real Estate leaders need to get a handle on what types of data are most valuable and how they can be used.
How’s this for some almost incomprehensible numbers: It’s been reported that 90% of the data in the world has been generated in just the last two years. We are currently creating 2.5 quintillion bytes of data daily—and that rate is accelerating. (1 quintillion has 12 zeros, in case you were wondering.)
Big Data has infiltrated every industry and every organizational group. Big Data in real estate is no exception. It’s reaching our personal lives in ways many of us don’t even realize yet. And it’s not groundbreaking to predict that this trend will only continue.
Because of the rate at which Big Data is growing and the incredible number of ways it can (and will) be used, it’s important to take a step back and seek a greater understanding of what types of data are being collected, how it’s gathered and analyzed, and how it can best be used by organizations. All those quintillions of data points mean nothing without the ability to capture, review, and analyze them effectively and, ultimately, use them to make informed business decisions.
The Limits of Big Data for Real Estate
For the past several years, Big Data has been touted as the solution to every business problem. If it’s not solving your problems yet, it will soon—or so we’ve been told. And so, many corporate real estate leaders have focused primarily on capturing and gathering as much data as possible, through IoT sensors, building management systems, and employee management systems.
Now, however, companies have huge amounts of data without the means of effectively analyzing it. Raw data, without analysis and context, isn't useful when making business decisions. Companies may try to build an in-house team to get a handle on the data coming in, but without training in how to work with Big Data, most of what those teams do amounts to guesswork and science experiments. This is why working with a vendor or platform that specializes in data collection and analysis, like Serraview, is so crucial.
Part of the problem companies face is that we are still figuring out which data is the most valuable for corporate real estate. We know that looking at desk assignments and space usage in real-time can provide actionable insights, but when we started to combine that data with, say, information on air quality from building systems, we found patterns we didn’t expect. In other cases, we found solid evidence to back what we’ve suspected for years—that air quality can impact the health, happiness, and productivity of employees. With this knowledge, you can work with your building maintenance team to optimize your building’s environment.
Know What You’re Looking For
With Big Data, it’s really easy to get caught up in the latest shiny object. A new sensor or beacon comes out that measures X and you become obsessed with measuring X without first questioning why you want to measure it or what insights it will provide. When it comes to Big Data in real estate, you need to stay focused on the questions you’re asking—otherwise you end up wasting time and money. After all, if you’re making an apple pie, you’d be wasting your time trying to harvest all the raspberries you can find.
Some questions you may consider include:
- How do people in my organization work together or collaborate?
- How are they using the space?
- What does a day in the life look like for different people or teams in my organization?
- What tools are they using and how?
- How is technological collaboration happening?
Staying clear and focused on these questions will help you seek out the data that will be the most valuable and provide the best insights.
Using Big Data Strategically
The strength of a tool like Serraview is to take all that data you’re collecting—from sensors and beacons, other software platforms, and systems—and make it useful and actionable. You still need to ask the right questions and think critically about the analysis Serraview provides, but it makes the process more efficient.