What if workplace data professionals were freed from time-consuming day-to-day grunt work and unleashed to be creative, take risks, and chase ideas? The impact could be transformative—uncovering insights that buck conventional wisdom, prospecting data to support bold office environment decisions, and discovering problems no one was aware of.
However, the reality of workplace teams is often far from this ideal. Data experts frequently face inefficiencies while attempting to collaborate with outside teams, high-pressure timelines, and a general misunderstanding by stakeholders of the data analytics process. These challenges can stifle their ability to deliver impactful results.
In this article, we explore what helps and hurts workplace teams and provide insights on how to set them up for success in building a data-driven culture.
Leading the Conversation for Optimized Workplaces
Mapiq empowers workplace teams with the knowledge and tools necessary to navigate the complexities of today's workplace landscape. Our Workplace Leader Series is platforming dialogue to foster a community where innovation, culture, and collaboration drive success.
Mapiq’s Alexa Lightner recently spoke with Tom Keeling, a data specialist at Cordless Consultants, and Sebastien P. Moeller, a data scientist at Booking.com, who shared frontline perspectives on what it takes to master and effectively integrate data to elevate workplace experiences.
Meet Tom & Sebastien
As a data specialist with Cordless Consultants, Tom advises corporations on appropriately leveraging office environments and utilizing smart building technology to create optimized workplace experiences using data. He says enterprises, leadership, and other stakeholders are often yet to understand how data science teams can be supported in a proper capacity and generally misunderstand how data and data science apply to workplace use cases.
In his discussion with Alexa, Tom explains that he has seen many companies encounter unforeseen challenges when trying to use new technology and become more data-driven in their workplace strategy. These pitfalls are usually due to a lack of data literacy among leaders and stakeholders, misunderstandings about the analytics process, and misconceptions about deploying a data team.
Booking.com’s Workplace Data Shift
A prime example of a company currently threading the needle of workplace data integration is Booking.com, one of the world's largest travel marketplace platforms.
Sebastien is the data scientist on Booking.com’s five-member real estate workplace services team, which supports strategic objectives for 120 global locations and day-to-day workplace operations and optimization. Sebastien joined Booking.com as the corporation opened its 65,000m² international headquarters in Amsterdam. He says it was a critical time when it needed to reassess its use of its real estate assets post-pandemic and how to accommodate employee needs best.
“The Booking culture really focuses on experimentation,” Sebastien says. “However, becoming data-driven and embodying a data philosophy in our day-to-day was a challenge within the real estate workplace team.”
"Becoming data-driven and embodying a data philosophy in our day-to-day was a challenge within the real estate workplace team."
He says this adoption process involved transitioning away from the highest-paid person's opinion (HiPPO) to a data-driven approach that actively engages stakeholders and respects the data science process. This shift has enabled Sebastien to research and develop powerful insights for workplace services to help optimize their offices and empower their employees.
For example, Sebastien has conducted research on how to allocate Booking.com’s office space efficiently. This research explored the impacts of noise levels and how office layout design impacts employee focus and collaboration. Booking.com is able to deploy these insights into their office environments through Mapiq’s platform.
Outside of their prioritized and planned projects, Booking.com also hosts hackathons to explore other use cases and ways data can support employees at work, where Sebastien says specialists like himself are encouraged to pursue ideas and engage in low-pressure creative exploration. These have allowed him to experiment with data sources and involve new departments.
“In the past, we’ve tried to estimate lunch volumes to reduce waste and linking multiple data sources before moving into [the Amsterdam] campus,” he said.
Launching a Successful Workplace Data Team
Is launching or expanding workplace data science a possibility for your organization?
Tom works with organizations day-to-day that are looking to begin engaging in data-driven decision-making, are already doing so in a limited capacity, or have integrated data and are exploring ways to build its impact.
Investment at any Scale is Always Positive
No matter where an enterprise or business is in its data journey, Tom says any investment into data is a positive move and a strong indication that leadership is in the right headspace.
“If it’s early in your journey, you can start making decisions and reacting to live and historical data in an informed manner,” he says. “If you’ve got a workplace data team, you’ve already hit a certain bar about thinking about data in ways many people don’t … you’re already doing well. You’d be very surprised at the amount of businesses that don’t look at the data that’s just falling into their lap."
"You’d be very surprised at the amount of businesses that don’t look at the data that’s just falling into their lap."
A common tendency Tom sees organizations needing help with is the tendency to approach data science as a function to solve imminent issues rather than a culture that needs to be grown and nurtured.
“We see people rushing because, more often than not, data-driven decision-making becomes an option at the last possible moment,” he says. “It’s a sort of band-aid that’s easy to put over poor business practices or poorly performing offices. They want to put data points into all of their tools to start reaping the rewards right away. That’s just not the case.”
Tom says this high-pressure approach creates a culture of fear around data and sabotages its impact, leading teams to misfire on data sources and believe they have to make considerable investments in deploying a data system. Instead, Tom believes the best outcomes materialize in low-pressure scenarios.
“Building data and verifying what your data points are bit by bit can be much more cost-effective and reliable,” Tom says. “People often don’t realize it’s an option because they’re rushing.”
“Building data and verifying what your data points are bit by bit can be much more cost-effective and reliable. People often don’t realize it’s an option because they’re rushing.”
Leverage the Resources at Your Disposal
Tom says Cordless Consultants has many clients who don’t have a data team, and their workplace stakeholders, such as facility and office management, often have little to no data literacy. In this case, he advises beginning small and leveraging tools already available. He also says teams can execute simple surveys using templates online, such as post-occupancy evaluations popularised by architects and service designers.
Tom says this is where a platform like Mapiq becomes very important, as it makes workplace data accessible and straightforward for all those involved in the decision-making process.
“They don’t have to worry about the quality or the semantics, and they can just get their actionable insights straight away,” Tom says. “[These kinds of people are] the majority of the market and the majority of the people that could benefit from the data as well.”
According to Tom, this low-stakes approach can also apply to enterprise teams. He says companies can research different workplace data strategies where they’ve seen success before making decisions.
“The benefits are obvious,” Tom says. “Then reaching out to a good data leader to build your data teams is the next step.”
"With a platform like Mapiq, workplace teams don’t have to worry about the data quality or the semantics, and they can just get their actionable insights straight away.”
Using Culture to Unlock Higher Levels of Workplace Data
So, what does it actually take to launch a workplace data team successfully and begin developing insights for office use cases? According to Tom and Sebastien, it’s all about building and maintaining a culture.
“Doing data science is much more than running a statistical model,” Sebastien says. “The reality is data quality, cleaning, maintaining and stakeholder management.
Both experts described this culture-building process as an iterative loop between collaborating closely with workplace stakeholders and delivering quality data. Healthy stakeholder relationships help support and contextualize the data science process, and delivering quality data and insights builds trust that strengthens stakeholder relationships.
Building Data Allyships
Workplace data teams require resources and stakeholder participation to execute meaningful research. Tom says this type of engagement requires establishing strong relationships and earning trust.
“You have to have the right data leadership, and you have to have the right advocates,” Tom says. “
For data teams, Toms says this means building allyship with individuals they expect to engage with the most, such as facilities and office managers, and rewarding them with useful content and actionable insights. Tom says this drives these stakeholders to buy into the data process by showing them the benefits of collaborating.
“Then you can start to spread out across the business — once the water cooler chat starts to be about the crazy dashboards or the proactive maintenance system you’ve been able to bring them on board with,” Tom says.
Sebastien says he delivers the best data work when engaged with the Booking.com workplace through mentorship and brainstorming sessions, maintaining close ties to stakeholders, such as IT and Booking.com’s People department.
“I really consider [stakeholder management] to part of the job as a data scientist,” Sebastien says. “It is extremely important to have the domain knowledge contribute to contextualizing and adjusting the outcomes to suit the needs. Data is complex, there are nuances, and you can’t only use numbers to represent the outcome, there is a lot of human in there.”
"Data is complex, there are nuances, and you can’t only use numbers to represent the outcome, there is a lot of human in there.”
When asked what aspect of data science they wish they could devote more focus to, Tom and Sebastien both said working with stakeholders would be their priority.
“Getting to know your stakeholders is probably one of the most important things and learning to be their advocate,” Tom says. “That’s what I’d be doing —workshopping, post-it notes everywhere. Smoothies and a whiteboard. Humans are the reason we do this.”
According to Sebastien, collaboration between teams with different strategic priorities is one of his biggest challenges. At Booking.com, he has forged strong relationships with human resources and IT teams by establishing symbiotic values.
For example, Sebastien said there are instances where he needs to import a data source from a system he is unfamiliar with, and he’ll have to rely on the IT department to help him. While this requires the IT team's time, energy and resources, Sebastien says they have a culture where the support is paid forward.
“They may be developing new tools for employees to use to book meeting rooms more easily,” he says. “I can support them and their work by prioritizing more meeting room analysis and identifying key opportunities. That way, we can each benefit.”
Data Quality: The Invisible & Critical Work
Reliable data and analysis fuel and foster a strong culture for workplace data science. Within Booking.com, Sebastien says ensuring data quality is critical to maintaining successful collaboration, especially with senior leadership.
“It’s very key that there is trust in the reliability of the data,” he says, “so when a decision needs to be made, we’re no longer questioning if a sensor reading is accurate, but what is the action based on those results.”
Sebastien says he spends roughly 20% of his time simply maintaining databases and ensuring they continue to track data sources. This invisible work allows him to respond rapidly to requests.
“It’s key to be ready to go when the moment arises,” Sebastien says. “Having the data clean and prepped — that you understand the schema and granularity — so that when your stakeholder comes, you can be quite agile. Then they have much more trust in coming to you with their questions.”
"Have the data clean and prepped— so that when your stakeholder comes, you can be quite agile. Then they have much more trust in coming to you with their questions.”
Sebastien says he’s found that even senior stakeholders can struggle to understand how data can add value to their decision-making process.
“When you interview them, it’s a process of understanding what they want to do, what they’re focused on, and what is important to them, and then what sort of data sources can add value to those decisions. Be present, and talk with them frequently.”
According to Tom, quality and reliable data are the essence of a data team’s authority with decision-makers. Tom says it's natural for stakeholders and leaders to bring assumptions to the table when approaching a query, and scientists need to be able to hold their ground like “knights in shining armor” because they’re confident in their work and the results.
“You’re telling them your data knows more than they do, and that it’s undeniable and that there is a positive effect from that,” he said. “More often than not, being proven wrong is a really valuable tool for discovering things that we didn’t know that we didn’t know.”
Conclusion: Successful Workplace Data Science Built on Trust
Unleashing the potential of workplace data teams requires a strategic, culture-focused approach. As Sebastien P. Moeller of Booking.com and Tom Keeling of Cordless Consultants have demonstrated, success in data science is about more than just technology and running models. Successful data science requires an environment where data teams have the freedom and trust to invest time in ensuring data quality and to forge strategic relationships within the business.
Organizations can also unlock transformative insights and drive significant business impacts by avoiding the rush to implement data solutions and instead allowing room and space to build a firm foundation of data literacy and stakeholder engagement.
In part 2 of Mapiq’s discussion with Tom and Sebastien, we will explore the pitfalls and best practices for workplace data, exploring it as a creative iterative art form and what future research and analysis will look like.
