Advanced workplace analytics is a catch-all term to describe the process of collecting, analyzing, and integrating complex data from multiple sources. This is usually with the goal of gaining deeper insights and making better predictions and recommendations.
In the workplace, good data can help business leaders and space planners understand how employees are interacting with the space available to them. And they can use this information to create a more responsive, productive, and flexible work environment. Workflows improve and employees are happier when companies use data science and predictive modeling to better understand their needs.
Of course, this is easier said than done in a hybrid office or one with flexible seating, where employees are using the office in new—and often sporadic—ways. Companies are finding that the simple data they collected prior to the pandemic is no longer enough to support their now ever-changing needs.
We sat down with Luke Anderson, VP of Product Strategy for OfficeSpace Software, and Kathleen Williams, our Senior Product Manager, to glean insights into how their clients are using advanced workplace analytics to improve workplace efficiency and employee experience. We chat about:
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Machine learning, predictive analytics, cluster analysis, neural networks, complex event processing, process automation, text mining, deep learning Google ‘advanced analytics,’ and you’ll likely be overwhelmed with a barrage of information on statistical models, sentiment analysis, and other complex ideas
But business leaders aren’t data scientists. They don’t need to understand the ins and outs of data mining to use advanced workplace analytics to improve their workplace. What they do need to understand is how to use workplace reports and analytics to improve their workplace efficiency… Along with the questions they can answer with the right data.
The role that analytics plays in the workplace has changed meaningfully since the pandemic.
Before COVID and the pivot to hybrid work models, there were two major assumptions that people had for managing the workplace.
With these two assumptions, you end up with a pretty stable work environment where most people are in the office most of the time, with little fluctuation from day to day or from site to site. So analytic use cases used to be fairly simple. Companies just had to answer a couple of simple questions. How many people are in your office, and how many seats have been assigned?
But the pandemic totally flipped things on their head. The office is no longer the default place where you have to do your work, and employees have much more choice. They’re going into the office on a variety of different days, for a variety of different reasons.
That means the dimensions of how people are using the office have changed dramatically. And without advanced workplace analytics tools, people won’t have a good idea of how to see what’s actually happening in their workplace, which is where data analytics comes in.
Facility managers (FMs) and space planners today need to ask and answer much more complex questions. How many people have reserved seats today? How many people have reserved rooms? And how do we know whether they actually showed up or not?
Prior to the pandemic, discovering the actual ‘truth’ of real-time office use usually took a back seat to simple questions. Like how many seats you have, and whether they’re assigned.
But now that flexible working is becoming the norm, and people are coming and going into the office on their own hybrid schedules, it’s much more important to understand what’s actually happening—what the real truth is—on the ground.
We’re hearing from clients that desk booking data alone isn’t good enough to understand the office anymore. Just because someone books a desk, that doesn’t mean they actually use it. And so to make the right business decisions to run a hybrid office, we need to understand with more certainty who’s really there.
In these past few years, the types of metrics that people want access to has expanded. And data visualization needs to be a lot more flexible. The role of FMs has expanded, too, because they’re not necessarily the only ones checking the data anymore. Leadership tends to see the comings and goings of the office as the lifeblood of the company, and they want to use advanced analytics techniques to achieve their goals.
It’s no longer a case that FMs can just assign all their seats and check-in with their VPs a few times a year to make sure they’re in the right place. It’s now more of an ongoing conversation with some of the more senior folks in the organization.
There are three main buckets of questions that companies can ask and answer with the right data.
The first bucket of questions centers around people, and how to optimize the workplace for them. These are the types of questions that can improve your hybrid workplace experience, which is important for things like productivity, employee empowerment, and talent attraction and retention.
We know that our clients want to invest in the things that people actually need when they’re coming into the office. For example, if you have product engineering folks using the office, you might configure your office differently. Or have different amenities available than if it’s mostly salespeople coming in.
To get these deeper insights into how to better outfit the office, companies need to be able to use their data to answer questions like who specifically is using the office, when, and in what types of seats.
The second bucket of questions covers forecasting and strategic corporate real estate decisions. Where am I going to make investments, or where can I save money in real estate specifically?
These types of real estate analytics questions generally focus around space utilization concerns, such as cost per usage (does employee attendance justify the lease cost?), actual usage versus full potential (do you need to consolidate, sublease, or expand?), and office density (can we get more efficiency out of our space?).
These are the data points that affect your real estate portfolio and translate into dollars and cents.
And the last bucket covers operational questions. These are day-to-day questions, like making sure you have enough things like desks, parking, and food for whoever is coming in in the short term.
These core operational questions are more tactical, but they still need to be data-informed. Perhaps you’re ordering food. Order too much and there might be financial implications, but order too little, and you might have unhappy people. So companies need good data to find the right balance.
Ultimately, everyone is trying to answer the question of whether there could be a better fit between who’s coming into the workplace and the resources they have when they get there. All of these optimization questions can help companies achieve the future outcomes they’re looking for.
There will also be more stakeholders for data going forward, and they’ll all have their own questions they need answered.
And remember that getting hybrid work right is so much broader than just the real estate questions. Real estate is obviously a key component, but people need a lot more data to get full visibility into their workplace.
No—not all data is equal. Not all data will give you the same high fidelity picture of what’s really happening in the workplace anymore.
Like Luke mentioned, desk booking information alone comes with a margin of error. If people don’t show up to bookings, for example, or they don’t remember to book, desk booking might not capture that. With flexible working and a more dynamic office, the burden of proof is even higher.
So you need different data sources that can triangulate with one another to see what’s really happening in the workplace. The right data should provide visibility beyond resource booking alone, into a truer picture of employee presence in the office.
In terms of who needs advanced workplace analytics, the more dynamic your workplace is, the greater the need for analytics.
Obviously, the larger the organization is, the more value that analytics can bring, too. If you have ten people in one office, that’s not a very difficult problem to solve. But if you have 200 people at your office, or as you multiply your number of sites, your complexity grows. And it’s hard to keep a pulse on what’s actually happening across different locations and a distributed workforce without big data analytics.
The ultimate goal with workplace analytics should always be improving employee experience, making it frictionless to go into the office. Yes, understanding how employees are using the office can help with real estate and space planning decisions. But it should also help create a much better user experience.
Fostering a better and more productive workplace experience is a big part of the analytics picture. We provide advanced workplace analytics on how employees are using the office. But then our clients can take that information about employee presence and turn it into a better experience for their people.
The idea is that hopefully, with better information, it becomes win-win. The company is able to save money on the real estate they don’t need anymore, and the employee is able to get some flexibility.
At the end of the day, employees are still able to come together when the times are right for them to come together and collaborate. And they’re allowed to work remotely or autonomously when the time’s right for that. That’s much harder to do, and it takes better data and visibility to make it possible.
More than simply giving companies a competitive advantage, advanced analytics gives them a clear window into their workplace that can help future-proof the office. Employees and employers both win when the right analytics tools are in place and used properly.
With the right data management, you can make sure you have the right space for the right people—that’s key.
Based on our customer experiences, being able to ‘test and learn’ is also really important. Clients are using advanced workplace analytics to inform what’s working and what’s not working in terms of drawing people back to the office.
This goes back to how using data has changed since the pandemic. Back when there was more of a status quo in the workplace, a lot of data lived in silos. You might have had visitors in one analytics platform, room bookings in a separate dashboard, people swiping badges in another, and a whole other system for space management. It might have been nice to have these all work together, but at the end of the day, traditional business intelligence worked in the traditional office.
But with the office becoming more complex, people need more from their data. They need to be able to look across their information silos and break them down as much as possible.
My advice to companies is that you can’t rely on just one source of data. Unstructured data won’t cut it anymore. You need to bring your large datasets together to truly solve business problems.
A lot of our clients are using data to understand what seating type is most popular, so that they can cater towards those types of workspaces.
I also think data helps to give feedback around what’s working and what’s not when it comes to workplace experience.
Maybe HR is trying to make it more enticing for people to return to the office. They’re trying to figure out how to make the process easier; should they buy lunch? Pay for parking? Give out transit passes?
Data gives you the ability to simply try things and figure out what works. Hopefully in a way that is ultimately mutually beneficial for the employees and the company.
You can also think about what data the employee needs for a successful experience at the office, which is why we created the Who’s In feature.
For employees to be successful in the office, they need to know who else is going to be there and when, so they can plan to collaborate. They also need to know what spaces are available, or whether things will be really busy or really quiet if they go in.
So giving employees visibility into the workplace and how their colleagues are using it with Who’s In can also help with decision making around their own use of the office.
The future of the office is going to be a test-and-learn feedback loop. Without historical data for your new hybrid workplace, you’re going to have to rely on some guesswork in the beginning.
A lot of companies have been asking employees what they want regarding the office. But that’s a hard question for them to answer. And we know from market research surveys that people are terrible predictors of what they actually want in the future.
So as you plan for the future of work, having the right data allows you to be responsive and adapt to what actually happens. Are people actually doing what they said? Probably not in a lot of cases—but that’s ok, because you can be flexible and respond as necessary.
Of course there are other aspects of planning an office space, like good wayfinding and floor plans that help orient the people who may not have been in the workplace in a long time. But in terms of the role of advanced workplace analytics, it’s really about being responsive and adaptable to what happens after day one.
I would add that in terms of what’s successful in workplace planning, taking a ‘no policy’ approach doesn’t tend to work very well. Even if you’re not sure what the best possible strategy is going to be at the start, you still want a strategy regardless.
Based on clients I’ve spoken with, it’s much more helpful to start with policy, and then use analytics and that feedback loop to see if it’s working or not.
You can start to see operational benefits from data pretty quickly. You can start using it right away for short term optimizations, based on how people are using your workplace day-to-day. Maybe you want to shut off the air conditioning for one of your floors, or you want to try to consolidate people onto specific floors within a building. The right analytics could help with this right away.
You’re probably not going to make big real estate decisions over a short period of time. You’ll want to see your demand stabilized a little bit before making those big-ticket decisions.
The amount of time you need is really calibrated to the type of decision you’re trying to make. If you’re trying to decide if you’re closing your headquarters, you probably want a lot of historical data to be able to make a high confidence decision. Versus when you’re trying to decide if you can turn off the air conditioner for a day.
How reversible are the decisions you are being asked to make? The more impactful they are, the more you want to have both high confidence and longer views of the data.
Remember that this is a test and learn feedback loop. The reality is you might not get things right the first time. That’s why, instead of getting rid of company headquarters, you might start with subletting a few floors.
As a rule of thumb, I’d say the bigger the decision and the higher the stakes, the more you’re going to need more confidence in your data. Especially if you won’t be able to reverse your decision. You’ll need multiple data sources and more time to view the data and feel good about the trends you’re seeing.
Algorithms change, and the way we use workplace data will always be in flux. Here’s what Luke and Kathleen see coming down the pipeline.
One of our clients found that the ‘people data’ in OfficeSpace was better than their HR data when planning their return to the office. In short, OfficeSpace provided a better window into where people were after the pandemic and lockdown. This FM was able to present the data to their leadership and HR team. Then they were able to suggest areas where they might need a temporary flex space because people had moved away from the main hub.
I didn’t expect people to use our data to help identify the footprint of their hybrid workforce and see where everyone is after the pandemic, but it worked.
I’ve also seen clients use OfficeSpace floor plans to help their cleaning crews. On our platform, we have a feature where you can see little rings around the seats that people are using. It’s designed to provide more visibility into the workplace, but it can also show cleaning crews what desks have been used and therefore need to be cleaned.
That’s not a use case that we built for, but it’s a fun example of what you can achieve out of the box.
Ultimately, high fidelity usage data is becoming more and more important. As we move into 2023, I think we’re going to move away from relying on employee input so much.
Going forward, if you want to understand how employees are interacting with the workplace, you need to rely more heavily on different data sources, like badging, WiFi data, and occupancy sensors. It’s too difficult to get your employees to report information themselves with a high fidelity that you can really trust.
I also think more data will be used to help fuel smarter things in the office, to help simplify and streamline people’s lives.
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Photos: FG Trade, Anchiy, courtneyk