Activity-Based Working (ABW) is a new way of working that is not bound by time or place and is becoming increasingly popular around the world. To gain a deeper understanding of ABW, we interviewed members of Veldhoen + Company (V+C), a company working on ABW projects at the forefront, and will be presenting a series of interviews with them about what they saw on the ground.
Our second interview with V+C is with Marco van Gelder, Global Lead for Data Research in the Netherlands, where the company is headquartered. We spoke to him about data-driven work styles and the three data-based approaches that V+C is pursuing.
Marco's biography
── Marco, please tell us about your career before becoming an ABW consultant.
Marco: I used to work as a strategic advisor to the Chief Human Resources Officer (CHRO) of KPN, a major telecommunications company in the Netherlands. The telecommunications industry is highly relevant to work styles, as technologies such as mobile phones and the Internet have greatly changed the way people connect over the past 30 to 40 years. Therefore, our organization has always been attracted to work style transformation using technology.
This experience of putting technology into practical use, obtaining data, and trying out how it can be used to change the way we work has led me to work with many clients, including KPN's largest clients in the banking industry, such as ABN and Rabobank, logistics companies such as Kuehne + Nagel, and airlines such as KLM Royal Dutch Airlines. There are also many other technology companies in the Netherlands, and I have worked with a variety of companies, from DSM to Shell. I was fortunate to be able to work on something that I found really rewarding: implementing innovative ways of working using technology.
I had worked with V+C several times since then, and had also had contact with Eric, the founder of V+C. Then, one day, I contacted V+C to take the next step from KPN, and chose to take a more serious approach to work style transformation with all my experience in research and data.
The importance of reflecting on work patterns based on objective data
――How has your approach to collecting data on working styles changed in recent years?
Marco: The pandemic changed the way we handle data at work.
Until then, telecommuting was only available to a select few, such as senior-level and managerial employees, and many people still worked in the office all day and went home at night. Therefore, the work data we used was basically office-related. We focused on how workers were using the physical environment and how we could make it more efficient.
But the pandemic has shifted one important variable — location — and forced us to consider other variables that impact work patterns: many previously in-person tasks have moved online or virtual, and asynchronous as well as synchronous collaboration has become important.
As a result, understanding what workers are doing, and more importantly, what is happening within an organization, has become more difficult than ever before. With this increased organizational complexity comes an opportunity to use data in a different way. We have moved from looking at what workers are doing in the office to understanding organizational behavior patterns by considering new variables: where they are and what they are doing, which is a new dimension in the data world.
At V+C, we have started collaborating with Microsoft to use virtual platforms to collect data. Even at this moment, when we are conducting interviews using Teams, the data that we have held a meeting is stored in a database. If we open this database and investigate, we will find that there was a meeting with three people that started at a certain time, and the participants are all from different domains and in different time zones. This is just one layer of abstraction that gives us the opportunity to understand behavioral patterns within and between organizations.
―― I see. So this provides a starting point for discussing work styles based on objective data.
Marco:Yes, we are actually using data to understand how our clients work, how it has changed before and after the pandemic, and the impact on productivity and employee well-being, and to derive useful insights. We know that frequent meetings, the difficulty of finding time to focus on tasks, and long working hours in a remote work environment can affect employee health. Data from virtual workspaces like this can be very useful in discussions between workers, managers, executives, and other levels of the organization.
What I find interesting is that on this topic of work, everybody has an opinion. There is no owner of the discussion, and everybody talks about work from a different perspective. Real estate, HR, senior leadership in the company, employees -- everybody has different interests. So data, especially objective data, helps bridge the gap between different stakeholders to have an objective conversation.
── That's right. Data is necessary for organizations to understand and analyze how people work and come up with better solutions. However, people often start collecting a lot of data without a purpose, and end up wasting it without being able to fully utilize it. How can we collect the objective data that we really need?
Marco:That's a good question. Data is beautiful, but the most important thing is to ask the right questions of the data. And that's one of the things we help our clients do. And the first thing we do in research and data is to ask our clients, "What is the question or business problem that you're facing?"
For example, we were consulted by a major company in the past and supported them in redesigning their new headquarters. This project was carried out before the pandemic and was a large-scale project that would normally take 4-5 years. We used the data obtained from research at that time to design the headquarters based on ABW, but then the pandemic occurred and it became difficult to grasp the situation.
So we used data from Microsoft's Office 365 usage to analyze collaboration patterns, and we made a big discovery: Before the pandemic, large, in-person meetings were common, but after the pandemic, small meetings suddenly became more common, and these meetings were often held within a department.
Through this data analysis, we have been able to understand the challenges and changes our clients face and propose appropriate solutions. Data is an objective source of information and a valuable tool to promote discussion from diverse perspectives and provide evidence for our clients' decision-making.
Data analysis approach 1: Social network analysis
――What other discoveries did you make through your data analysis?
Marco: Data analysis of changes in work patterns before and after the pandemic showed that collaboration between multiple departments has shifted to focus on collaboration within departments. Therefore, when many people, for example 1,000 to 3,000 people, use the same space, it is important to redesign the environment to promote collaboration for different purposes.
In another project for a Japanese client, data analysis was conducted to understand how a newly implemented hybrid work policy affected workers' behavioral patterns. Upon closer inspection, we found that while hybrid work itself was working, the tendency to plan everything in advance meant that most of the day was filled with meetings, limiting individual focused work and effective collaboration. As a result, we decided to introduce social network analysis as an alternative method.
Social network analysis is a simple approach that asks workers, "Who are you collaborating with?" For example, I think the two interviewers have many collaborations, including meetings about this interview. This means that there is a very thick line between the two of them. On the other hand, the interviewer and I only met for the first time today, so there is only a thin line between us. Social network analysis visualizes informal collaboration structures and patterns that are not shown in organizational charts, such as who is collaborating with whom. It is useful for understanding the characteristics and challenges of an organization.
An example of social network analysis. Each dot represents a workers, and the larger the dot, the more people the worker collaborates with.
The reason behind the social network analysis in this project was that this Japanese company wants to evolve from being just a car manufacturer to being an automotive company that is involved in everything from understanding user needs to product planning, manufacturing, and selling cars. This means that cross-team collaboration is very important. Through the analysis, we can see which departments collaborate with which departments frequently and what is holding back innovation progress, which can help us redesign the physical environment. We also need solutions from a behavioral perspective, and think about how we can find each other more easily and collaborate more easily virtually.
This data-driven approach plays a key role in understanding how organizations work and finding the right solutions to improve it, from the physical design to the behavioral aspects of how work is redesigned to enable more effective collaboration and increased productivity.
――How to capture performance through data
Marco:As I said, objective data helps move the conversation from an objective perspective to an individual perspective on how work works. I mentioned collaboration earlier, but another area I'm interested in is performance, because if we're talking about organizations, we're all employed by companies to perform.
The performance equation consists of two important elements. One is productivity. Productivity is further made up of two elements: effectiveness and efficiency. In other words, it is important to know whether we are performing the work and tasks that are considered effective in achieving organizational goals (effectiveness) and whether we are working efficiently with each other toward those goals (efficiency). These elements can now be discussed using objective data.
The second important factor in the performance equation is engagement. Even if an organization has talented people, if they are not motivated, in other words, if they are not engaged, high performance cannot be achieved. This is why understanding engagement is important, but like productivity, engagement also has two factors (= variables). One is whether you feel connected to people within your department or team, and the other is whether you have connections with people in other departments. This is where the aforementioned social network analysis comes in.
I also touched on this topic of performance in a paper I recently wrote in collaboration with human resources personnel from a major Dutch company and Tilburg University. The question of "what activities will be performed," which is the basic idea of ABW, is important, and various variables can be added to the "place" where the activities are performed for each organization and individual. When looking at actual performance, it is important to understand how collaboration is affected by management style and culture from the perspective of productivity, and in terms of engagement, it is important to provide opportunities that utilize people's appropriate skills and motivations.
Data analysis approach 2: Team maturity scan
Marco: In addition to this social network analysis, we also have an approach called the Team Maturity Scan.
I don't know if this still applies in Japan, but in Europe, in addition to the original mix of different cultures, there is now a lot of discussion about how the passion part of engagement can be maximized due to the increased opportunity for individuals to work in a dispersed environment thanks to work remotely. We have incorporated a data approach to these discussion points in our team maturity scan.
Based on the six elements used in the team maturity scan, each item is quantified to understand the current situation.
This is also a basic psychological framework, where workers discuss with each other whether they feel psychologically safe to be themselves. If there is no psychological safety, it is necessary to understand where the problem lies and promote ABW from that perspective. This method is not something that must be used. However, it can be useful for looking back at the way an organization works from a data perspective and holding more precise discussions on realizing ABW working styles.
Data Analysis Approach 3: Healthy Mind Platter
── In terms of working style, apart from performance, are there any other approaches to data analysis related to employee health and well-being?
Marco: As I mentioned earlier, our working habits have become busier since the pandemic. Our schedules are packed with meetings, long working hours are the norm, and our well-being is at greater risk than ever before. Some people now start working early in the morning when there are no meetings, and even use weekends for personal work. While there may be times when this kind of working style is necessary, it is far from sustainable.
As one way to reconsider the negative effects of hybrid work, we offer a workshop called the Healthy Mind Platter. The Healthy Mind Platter asks participants to reflect on their current work style based on the seven components that make up daily mental well-being advocated by Daniel J. Siegel, clinical professor of psychiatry at the UCLA School of Medicine, and consider the ideal ratio of work styles for their health.
Seven Elements of the Healthy Mind Platter
Analyze the current time ratio based on seven elements and consider the ideal ratio
In ABW, the focus is on workers activity, but from the perspective of well-being, we want to emphasize the importance of rest, in other words, time to do nothing and relax the brain. Even moving from one meeting to another requires switching your mind, which consumes a lot of energy. The same is true when doing concentrated work. When working in a hybrid environment, it is important to remember to think from a comprehensive perspective, including your own health condition.
Finding the right balance between these elements is key to maximizing organizational results, which is why a data-driven approach provides our clients with the tools they need to collaborate effectively and be more productive every day.
Conclusion: A message to Japan
── As a data expert at Veldhoen + Company, do you have any advice for us about hybrid working and ABW in the Japanese market?
Marco: Japan is a very attractive country with beautiful culture and traditions. However, it seems like a country that is looking for ways to optimize things and has a strong interest in how to do so. In that respect, data is a very powerful weapon.
Today's new ways of working require solving a complex puzzle that takes into account many new variables. In addition to the physical environment of the office, which has been the focus of attention up until now, there are many other factors, such as the activities workers perform, the places they prefer to perform those activities, and collaboration and engagement. If we can use data to more actively discuss work styles based on activities, the transformation of work styles will be more interesting.
