We are workplace scientists, specialising in the understanding of the interplay between spatial design and organisational behaviour in working environments. We are particularly interested in how office space affects ‘Zusammenarbeit’ – that is, how people work together.
When it comes to moths, the scientific evidence is very clear: moths are attracted to light and tend to fly towards a source of light (such as the moon), especially when in danger. But what about humans and their relationship with light – specifically, their attraction to daylight? Intuitively, most of us would agree that daylight is important to us and therefore must also matter in the workplace. Over the years, researchers have studied daylight in the workplace and how it affects human behaviour, performance and ultimately productivity. However, when you start digging deeper, you find surprisingly little empirical proof of the positive effects of daylight on workers. So is this all just a myth? Or are we, like moths with the moon, attracted to natural light, searching for it and happier when we find it? We were curious to find out.
Daylight has a deep connection to our daily rhythms as human beings. Light controls our circadian rhythm, the 24-hour cycle also known as the internal body clock. The 2017 Nobel Prize for medicine was awarded to researchers who discovered the underlying mechanisms of maintaining circadian activity in every cell. Generally speaking, daylight suppresses the secretion of the hormone melatonin, which induces sleep, hence light governs wakefulness. In addition, the ultraviolet wavelengths in daylight affect the production and synthesis of vitamin D and positively affect the immune system. Therefore, daylight is generally known to impact wellbeing. But how does it matter in workplaces?
To start with, it is important to understand what kind of light we are talking about here, and that is natural daylight coming into the workplace through windows. As opposed to artificial light, natural daylight is brighter and more varied; it allows people to understand seasons and time of day and thus links in with our circadian rhythm. We are not considering what you can see from your office window (which is often called prospect), how large your windows might be, which way they are facing (north or south, east or west), or how distant your desk might be from a source of daylight. While all of these matter in practice, we want to focus on the impact of daylight on the whole (as opposed to working in dimly lit, or just artificially lit workspace).
Research studies suggest that there is a connection between access to daylight and increased employee satisfaction. If asked, humans consistently express explicit preference for natural daylight in offices and cite psychological comfort, health and aesthetics as main reasons. It is also argued that daylight has an impact on productivity, since happy and satisfied employees are also more productive at work. The question remains how significant this impact is and whether it is measurable.
Several case studies from the US seem to have confirmed that there is something in this. A study of Lockheed Martin moving into their new intensively daylit office headquarters for 3,000 staff in Sunnyvale, California in the 1980s reported large increases in productivity based on reducing absenteeism by 15%. A similar case is described in the book ‘Welcome to your world‘ by Sarah Goldhagen. Nine months after moving into a new headquarters in Zeeland, Michigan, furniture manufacturer Herman Miller reported staff productivity had risen by 20%. The building in question was called the ‘Green House’ for its ample use of courtyards, internal gardens and skylights.
Still, productivity at work is often difficult to measure. Therefore, looking into comparable settings with readily defined productivity metrics can be insightful. Here are the most interesting studies we found, showing measurable effects and thus rigorously linking daylight access to increased performance and wellbeing.
Workers in workplaces with windows slept 46 minutes longer per night than those without access to natural daylight in their workplace.
So yes, there is robust scientific evidence that natural daylight has an impact on human wellbeing: it eases concentration and learning; raises productivity; reduces stress; and means better and longer sleep at night. Most of these findings however, stem from research in settings other than office-based workplaces, so it will be interesting to see further research in the field, specifically targeted at office buildings, to follow the lead of the study mentioned above on longer sleep at night for those workers having enjoyed daylight during the day.
This cat knows how to enjoy the sunshine.
Many offices in the UK are not designed with daylight in mind. This is partially due to regulations, which recommend daylight in workplaces, but do not demand it. In contrast, other countries regulate daylight access much more rigorously, among them Sweden and Germany. Without regulation, designing in daylight can easily fall down the priority list, especially when the drive for maximising usable space leads to the creation of deep floor plates in many commercial developments.
But the evidence is clear. If you care about the wellbeing and productivity of your office staff, then prioritising access to daylight could be an easy win.
I’ve only met Neil Usher once, but now I’d definitely like to get to know him better. In his new book ‘The Elemental Workplace’, Neil reveals himself as a natural writer with strong opinions – on a soapbox – but always authoritative and speaking to the reader as an adult. As I read the 200 or so pages I frequently found myself nodding in violent agreement. It felt like a meeting of minds. More than that he made me laugh out loud several times.
This is a book that introduces the ‘ridiculous idea’ that everyone deserves a fantastic workplace and that it is ‘ridiculously easy’ to implement one. What follows is a very practical ‘how to’ guide, organised into sections, broadly: why, how and what. The aim is not to overcomplicate and to keep things jargon free. It isn’t until page 50 that the reader reaches the end of the section on ‘why’. That is, ‘why’ should organisations invest in their physical workplaces at all? That is the right number of pages. All credit to Neil for taking the space to realistically explain the possible legitimate reasons and the arguments that can be used to convince others, when you need to work with real decision makers in real organisations. Neil’s healthy dose of scepticism looms large from the off in this section. And having debunked many a workplace myth, around the measurement of productivity and millennials as just two examples, what could have sounded like his own too easy soundbites are quite the opposite – they are, by and large, nuggets of gold. He also understands and helpfully expounds on the broader challenges of the organisational change journey itself.
Throughout this early part of the book there is a lot of trailing of the ’12 Elements’ which are the meat of the ‘what‘ section which comes later in the book. This is a shame because the ‘why’ and the ‘how’ are just as relevant, if not more so, and the reader finds themselves compelled to hurry along to what is being billed as the main course. In fact to a certain extent the structure as a whole did feel to me a little bit constraining. Towards the end, when writing about the ‘possibles’ and ‘probables’ – the workplace design aspects that you may need to think about but that don’t fit into the ‘Elemental’ framework – Neil seems to hit his stride in a free flowing few pages of damned good advice and where he provides the biggest of many laughs in the book when he mentions badgers and boardroom tables. You will have to read the book yourself to find out more.
The book’s main course: The 12 Workplace Elements
Aside from the entertainment value, the book exists to try to extract simplicity out of unnecessary complexity in workplace design. And this is where I thought I might find myself disagreeing with it. Not that I don’t endorse the sentiment. Neil states that simplicity is not the same as simplistic. The 12 elements themselves are at first sight a simple checklist of issues you would ignore at your peril if you aspire to creating a decently effective workplace. Neil provides compelling evidence for why each of the 12 should be there. With a strong statement early on that workplace is not a complicated subject, lurking within the 12 elements are hints at the complexity that I firmly believe nevertheless does exist. For example, the risk that in fact, as Neil puts it ‘a collection of poorly considered, ill-specified and badly designed settings in illogical locations will deliver a terrible workspace’; the ‘delicate balance’ required to make a ‘refresh’ space, which he calls an ‘Elemental Workplace’ in microcosm. And in the excellent section on the social workplace, where Neil refers to how easy it is to misinterpret appearances of activity in the physical workplace and advises that ‘a deeper awareness and understanding is vital’. These things are about fine and informed judgements, taking into account the interrelationships between the many factors at play at the same time relating to space and people. And I don’t think that quite all the workplace data we need to help us to understand this is already in existence, as perhaps the book implies. It isn’t always as simple as we might like it to be. There. That is my quick turn on the soapbox that Neil has provided me with. But he has got up there first and shouted loud and proud about how everyone deserves a fantastic workplace and he should be listened to.
If we ask you ‘what is the function of a building?’, your answer might well include ‘to give shelter’, or ‘to provide space for certain activities’. Indeed you might well say it depends on the type of building; a factory is there to manufacture products, a school allows children to learn. And of course you’d be right about all of these functions. But stop to consider a more fundamental function that all buildings possess. A social function first posited by Bill Hillier and Julienne Hanson in their seminal book ‘The Social Logic of Space‘. That is, that all buildings can either bring people together or keep them apart.
When we think of workspaces, we mainly have the function of bringing people together in mind: we talk about the office as a place where people bump into others; we think of collaboration and knowledge exchange and working together; we might even go as far as to suggest that the main rationale for office space is for people to ‘feel less lonely’, as a recent analysis of co-working spaces suggested. Even critiques of the dreaded open-plan office focus on the presence of others (which is then often perceived as a nuisance).
What is not so much talked about, however, is how exactly togetherness is orchestrated. Who are you together with? Who do you meet most often? And what effect does that have on the organisation as a whole and the way work is organised?
There is of course a simple way togetherness is organised in offices: through a seating plan. Apart from recent trends of ‘activity-based working’, where employees choose a different location in the office based on their task at hand (although that is often still based around team zones), your workplace tells you where to be: at your desk. Once you are assigned a desk, we also know that you spend 44% of your time (on average) at that desk. Research has also established something else: who you talk to most often. Maybe unsurprisingly, physical proximity governs the majority of face-to-face contacts, since the most frequent everyday encounters happen within a range of 10-22 metres from your own workstation. This means seating plans are incredibly powerful tools, as they will foster relationships with other people seated close to you. This is what in research terms we would call a ‘spatial solidarity’, i.e. a social bond induced and maintained by means of space.
Unfortunately, many organisations do not use the power of the seating plan strategically. Often department A will sit in this corner, department B over there and so on depending on availability of space and sometimes who shouts loudest. You may not have noticed, but something rather important has happened here: the organisational silo has emerged. But how so? By overlaying a second important rationale that drives day to day interaction on top of physical proximity: departmental affiliation. You are much more likely to interact with other colleagues in your own department because you often share goals, tasks, experience, disciplinary backgrounds and identities; and so sales people talk to sales people, developers talk to other developers, the creatives talk to other creatives and so on. This ‘silo mentality‘ is often quoted as a main reason why cross functional collaboration does not flourish in organisations which may result amongst other things in needless duplication, business process delay and missing potential business opportunities.
Let’s unpick in a bit more detail how space can contribute to silo mentality. We’ve already introduced proximity as a motivation for people to connect with one another. In addition to this spatial solidarity, we overlay a second layer of solidarity, that of departmental affiliation. In the research literature, this has also been called ‘transpatial solidarity’ (a solidarity able to overcome, or transcend spatial barriers), since it describes who we are and what brings us together (gender, age, background, job role, etc). When both of these solidarities, the spatial and transpatial overlap very tightly, we speak of a correspondence model. The left image below illustrates this with a grouping of blue and purple dots. Imagine blue and purple are two different departments and they are also grouped together spatially. This is what almost every typical seating plan looks like: sales in one corner, marketing in another, product development in yet another place, etc. The stronger the organisational divides are between departments and the larger the distances or spatial barriers (such as being on a different floor of the office), the more pronounced the silo effect is. The illustration on the right shows a non-correspondence model: people from different departments are mixed up in the same space.
Two models of overlapping solidarities: in correspondence or non-correspondence
Management scholar and author Tom Peters has already discussed this effect almost 30 years ago in his paper ‘Get Innovative or Get Dead’:
“I’ve said many times, to the surprise of many people: physical location – in particular, jamming people from disparate functions together in the same room or workspace or cubby hole – is the number one culture change tool that I’ve discovered! Move the accountants to the manufacturing floor: within six weeks the accountants will appreciate the manufacturers, the manufacturers will appreciate the accountants. Put the designers, engineers, manufacturers, and marketers in one location working on a joint product development process: something close to a miracle will invariably occur.” (Tom Peters, 1990: page 23-24)
But why is it that this insight still has not made its way into common practice? We can only speculate. Certainly many organisations value teamwork within departments and proximity enables just that. It might be that it is the most straightforward solution and mixing up people would require additional efforts and might mean overcoming barriers and resistance to change. It might be that organisations are only just beginning to realise the impact space can make on the way their business is run. Space planners might not have understood quite how strategically important seating plans can be.
If anyone needs further convincing of the virtues of mixing people of different departments up and distributing them among space, we can only suggest to visit the headquarters of juice and smoothie producers Innocent. In their office in Ladbroke Grove, Innocent have done just that: people belonging to a department sit dotted around the various floors of the office. Their next desk neighbours are mostly from different departments and so organisational cohesion, trust and knowledge is spread throughout the business. Curious to see this in action? Luckily, anyone can visit Innocent. They do extend an invite to anyone who wants to come, and print this invite handily on each bottle of juice or smoothie they produce. And they mean it, as described in this blog post of someone who took them up on their offer.
What can we learn from this? We would argue that spatial layouts matter in enabling connections between people. Who sits next to whom will drive the frequency of day to day encounters. This can be put to good use to foster relationships between those who might normally not meet each other. It is astonishing to think that Tom Peters suggested this almost 30 years ago and very little has changed in the way that seating arrangements work. So the next time someone tells you they want to break down organisational silos and increase cross-functional collaboration, ask them whether they have considered the functional power of their office building and whether they could change their workplace seating plan.
The run up to Christmas is a timely reminder of the perils of too much choice. I don’t watch much telly, but I have already become aware of the annual barrage of perfume adverts, for brands which I honestly can’t tell apart. And that is before we mention the now traditional cute supermarket ads. All of this is designed to tempt us into buying from the plethora of goods available online and in store, so we can create the perfect festive season.
Before you, perhaps rightly, accuse me of bah humbug, take a moment to consider what psychologists have called the ‘paradox of choice’. The phrase, coined by Barry Schwartz in his book first published in 2004, refers to the phenomenon that too much choice, particularly when it comes to consumer goods in affluent western societies, creates dissatisfaction. This is contrary to what he called the ‘official dogma’ that the way to maximise the welfare of our citizens is to maximise our individual freedom and the way to do that is to maximise our choice.
When choice is de-motivating: a psychological study showed that people faced with a choice of 24 types of jam bought less jam than when they were only offered 6 types.
He cited the ‘jam study’ (run in 2000 by Sheena Iyengar of Columbia Business School and Mark Lepper a psychologist at Stanford), which showed that, simply put, consumers faced with a choice of 24 types of jam bought less jam than when they were offered only 6 types of jam. Schwartz put forward 4 reasons why too much choice can have a negative psychological impact:
Regret and anticipated regret – we may become paralysed and anticipate regret about a choice that could turn out to be less than perfect.
Opportunity costs – the feeling of missing out on an alternative, which we think might have been better.
Escalation of expectations – when multiple choices imply that near perfection is possible and then those high expectations are unlikely to be met.
Self-blame – when we find out that we have made a poor choice relative to expectations or alternatives, we are more likely to blame ourselves for our failure to make a better choice! If there is only one type of jam and it tastes bad, then we can blame someone else.
As Schwartz says ‘everything was better when everything was worse.’
So how does all this relate to the workplace? In recent months I seem to have heard several pronouncements on the virtue of diversity and choice of workplace settings, especially when it comes to open plan and agile working environments. And being the scientific rationalists that we are at brainybirdz, we naturally question broad assertions, especially when there is evidence which doesn’t seem to fit.
We recently ran a workshop on the subject of visibility in open plan workspaces. It was held in an office environment with multiple furniture and locational settings and as part of the event we asked participants to choose where they wanted to work from for both a group collaborative task and an individual task. Afterwards we asked them to reflect on the emotions they felt about the setting they had ‘chosen’ and experienced. Most were positive about their choices, but a small minority were not. It seemed to us that some of this was to do with just the sort of factors that Schwartz talks about. In a variation on the theme of expectations not being met and the possibility that there was another location that would have been better, some found themselves unable to work in the place they originally had in mind because it proved to be a popular spot and other people got there first. In workplaces the choice, even if it is there, is inevitably limited. People very quickly work out favoured places to go based on such factors as access to daylight, privacy, comfort etc. The provision of choice can inadvertently create hierarchy. If the popular locations are ‘always full’ or maybe dominated by people more senior in the organisation, then it is not unreasonable to assume that people could feel more disadvantaged than if everyone had the same identical, uniform, bland workstation to work from.
We’re not saying that choice in the workplace in itself is bad. But neither is it good per se. At another simpler level, if employees perceive there is too much choice of where to work from, they may also, like the jam buyers and like me as a confused perfume consumer, decide the best choice is not to bother at all. Merry Christmas!
Last year in “Is open plan the collaboration magic bullet?” we blogged about the spatial effects on collaboration of an organisation that moved into a single floor open plan workspace from a highly partitioned office accommodated on two floors. Over the last few months we have delved a bit deeper into the evidence and have recently presented our findings at the European Social Networks conference EUSN17, an academic symposium hosted by The Johannes Gutenberg University in Mainz, Germany.
A quick recap. The organisation in question appeared to suffer a noticeable decrease in interactions both within and across departments after they moved to an open plan environment. The opposite of conventional wisdom. We hypothesised about a range of spatial phenomena that might have been at play. For example, we suggested that a 26% decrease in communication within one department in our case study, might be to do with a loss of “intimacy”. In the old office, this department occupied its own enclosed space which was segregated from the rest of the organisation (perhaps allowing communication within the department to flourish), in the new office, this department was in a very open location close to the social hub.
So we decided to take a closer and more scientific look at ‘intimacy’ in workplace settings. We measured this as the size of the 180° visual field from each workstation: if you don’t see very much from your desk, your degree of intimacy is high. We found that there was indeed a significant correlation between intimacy and interaction frequency, but only in the partitioned workspace. In the open plan workspace there was no measurable effect at all. The visibility fields were, as you would expect, much greater on average in the open plan space – intimacy was completely lost. So in the partitioned workplace people communicated less frequently with others face-to-face when their visual fields were large (and hence intimacy low).
In a highly partitioned layout, the less intimate the space the less interaction there was within each department. The same relationship was found with interaction outside departments.
However, we did find that there was another spatial visibility effect at work in open plan space and that was about ‘control’. Control is the phenomenon that is behind our choices of where to sit (which is the topic of another one of our blog posts). That is, we are more likely to seek out a location where we can see what is happening in front of us but cannot be seen by others from behind – we are in ‘control’ of the situation. We measured this as the ratio of the sizes of two different visual fields or isovists: the area of the 180° visual field as a representation of what a person can see from their desk divided by the area of the 360° visual field, which represents the areas from which that person can be seen. The higher this ratio is, the more a person is in control of their immediate surroundings.
Example of one desk with medium levels of control: 180° visual field versus 360° visual field
In the new open plan space we found a direct correlation between control and interaction frequency within departments: the more in control, the more face-to-face communication occurred. Control did not seem to be related to interaction with other departments and was not a significant factor for any type of interaction in the previous partitioned workspace.
In an open plan layout, the higher the visual ‘control’ of the surrounding space the more interaction there was within each department.
It seems as if in open plan space, when intimacy is low everywhere, there are no measurable effects of relative intimacy to be found on interaction patterns. Instead control starts to make a difference. Perhaps control is the next best thing when visibility and openness is ubiquitous and no intimacy can be found.
Of course these findings are based on one case study alone and we should not ignore the effects of culture on the needs for intimacy and control to support more interaction. Another organisation in the same open plan office layout might behave very differently. Nevertheless what we do have is definitive evidence for how maximising visibility may in some cases produce the opposite effect when it comes to interaction and collaboration.
If you are interested in learning more about the effects of visibility in the workplace, our half day short course on 10 Nov 2017 ‘The Visibility Experience’ might be for you.
In our fourth and final blog in our series, we discuss the organisational reality of workplace data collection.
The truth is organisations and businesses of all kinds routinely collect data. The most obvious being financial results. How many products have been sold this week? What is our total revenue for the year? How much have costs gone up this month compared with last month? These are all nicely quantifiable and can be broken down into cost and revenue categories and for many are the core of any performance reporting. In a business context this type of data can easily gain the attention of decision makers and senior management.
So can data about the workplace command the same levels of attention as financial performance or will it simply languish at the back of a report that never gets read?
At one level the answer comes back to the issue we explored in our second blog in this series ‘It depends’. If there is a clear link between the data you are collecting and organisational performance in a way that ties in with key strategic objectives then it should be a no brainer, shouldn’t it? We gave some examples of these kind of linkages in that blog post. Unfortunately this is where organisational reality gets in the way.
It was back in 1992 that Robert S Kaplan and David P Norton published their first article in the Harvard Business Review which introduced the idea of the balanced scorecard – a set of financial and, crucially, operational measures that would give ‘top managers a fast but comprehensive view of the business’ which they likened the to ‘dials and indicators ‘in an airplane cockpit that are essential for ‘the complex task of navigating and flying’. The concept they developed and revisited in 2010, was based on research into 12 companies ‘at the leading edge of performance measurement’ and was subsequently adopted by many other organisations who were interested in looking at their businesses from four different and interrelated perspectives: financial/shareholder, customer, internal processes and, innovation and learning. So far so good; this approach has led companies today to a common understanding that a broad based set of performance indicators can be valuable, even if the specifics of the BSC as originally envisioned by Kaplan and Norton are not always rigorously in place. This is a business environment that should provide scope for many organisations to see the value of workplace data, perhaps in the context of measuring internal capability, especially data collected on a continuous basis. Indeed some have attempted to develop standard workplace metrics. The difficulty first comes in finding enough evidence to make any link with the overall business vision, strategy and objectives, and second, even if that evidence can be demonstrated, the advocate may still struggle to convince the right decision makers. This leads on to another difficulty – fighting for space in the data crowd.
The template for the balanced scorecard as represented by Kaplan and Norton HBR July-August 2007
Kaplan and Norton noted that ‘companies rarely suffer from having too few measures’. They argued that developing a balanced scorecard forced companies to focus on the ‘handful of measures that are most critical’. If you are a senior manager you don’t have time to plough through pages of statistics, you want the executive summary.
Nevertheless, some have tried to create standard workplace metrics, which have relevance to business performance and therefore should make sense within a real organisational context. We have already blogged about Jacob Morgan and his concept ‘Employee Experience Advantage’ and Neil Usher who advocates a very simple approach to workplace measurement also comes to mind. Still, the growing landscape of workplace data collection can look confusing and the complexities of linking workplace design decisions to organisational performance indicators are also not to be underestimated.
At brainybirdz we specialise in understanding the organisational realities, especially in helping organisations to think of the physical workplace as a catalyst for greater collaboration. Generating data is only relevant if it fits into the existing performance measurement landscape and if decision makers are convinced that it does fit and is relevant. Sometimes that task is made easier if data collection is positioned as part of a one off strategic review where presenting evidence about how the workplace is performing can be used as a basis for a decision to invest in a new office fit out or re-location. This may then pave the way for a more continuous approach to collecting and analysing workplace data at a later stage, ideally with some link back into personal or team performance reward systems.
This is our final blog in this four part series about workplace data collection. The other three parts are available here:
Big Data is one of those buzzwords that has risen to popularity over recent years. It sounds important and great. Not just data, no, BIG data. What adds to the myths and mysteries of Big Data is that analysts describe it as the next big frontier and a way to improve innovation, competition and productivity. For the very reason that Big Data seems to solve all business problems, a sense of excitement surrounds the topic. The Big Data hype has reached most industries by now and construction is no different. In 2014, ARUP published a thought piece on the topic (though without explicitly labelling it as ‘big data’) arguing that data will be the new currency in construction. But what exactly is Big Data and is it really superior?
Many different definitions of Big Data exist. Some people describe it simply as larger data sets, others focus on its complexity. One popular definition comes from analysts at Gartner who focus on the 3 V – volume, velocity and variety. So Big Data according to this definition is data that is high in volume, gathered and analysed speedily and constantly, and is of a greater variety than usual, often combining different sources and data sets. A brilliant and critical definition of Big Data comes from social media scholars Danah Boyd and Kate Crawford, who define Big Data in their article ‘Critical Questions for Big Data‘ as:
“We define Big Data as a cultural, technological, and scholarly phenomenon that rests on the interplay of:
(1) Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets.
(2) Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims.
(3) Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.”
Boyd and Crawford then go on to dispel many Big Data myths, among them the claim that bigger is better and that Big Data is more objective and accurate. They also criticize Big Data for creating new digital divides, taking data out of context and raising ethical issues.
How does this relate to the construction industry and data we might collect in projects around work and the workplace?
One thing to notice is that Big Data is only making a very slow and careful appearance in the realm of workplace. Notable exceptions such as Humanyze aside, Big Data in the field of workplace is rather in its infancy. This is not to say that Big Data studies do not exist. Data sets such as the Leesman index or the Gensler workplace study are increasing in size; comparative studies of workplaces are on the rise, for instance the one we were involved in some years ago on the ‘Generative Office‘, which evaluated 61 office buildings in 2012, or the most recent analysis of social behaviours in the workplace by Koutsolampros et al. But on the whole, Big Data in the workplace is certainly not the norm.
Is this a problem? Definitely not. Big Data can undoubtedly lead to new insights into patterns that weren’t visible with traditional methods of data analysis. But so can carefully crafted studies collecting ‘small data’. There are so many things we do not yet know in the workplace that any type of rigorously collected data will add insights, for instance qualitative data gathered in interviews, ethnographic observations or statistics derived from a clearly defined single source data set. Big is not necessarily better. Yet again, the answer to whether you should collect Big Data is “It depends”. If the problem you need to solve requires real-time analytics, automatically collected data and the combination of diverse sets of data sources, plus you have the resources required including data science capacity and skills, then by all means go for it. Most often though, a clearly defined small scale study, collecting exactly the data that is needed to answer a question can be much more valuable to businesses than jumping on the bandwagon that is Big Data.
Hopefully you are convinced that there is virtue in collecting data as a means to aid decision making, especially if you have read the first part of our blog series ‘What data should we collect?’. Now it’s time to think about the core question: what data is it that you should collect and how can you make the most of it? Our answer? It depends.
“It depends” is a fantastic reply to almost any relevant question that goes beyond yes or no. It was also the theme of a discussion I’ve recently enjoyed with like-minded professionals, business leaders, consultants and freelancers, brought together to discuss the concept of the ‘Minimum Viable Workplace‘ (i.e. what is the minimum that needs to be provided for people to be able to do their jobs). The suggestion that there should be a hashtag on twitter for questions to which the answer is #ItDepends got my little group discussion going on the day. But I’ve used “It depends” many times before in discussions with people, specifically about data collection strategies.
“It depends” as an answer could be seen as lazy or indecisive. But what a good “It depends” answer actually does, is to open up further debate. The conversation could go like this:
What this shows, is that data collection depends on the decision to be made. Too often, especially in a workplace context, we find that people get excited about a particular technology that collects some data without reflecting on what the business needs to decide and act on. In this case the technology (such as a sensor, or a mobile phone app, or a survey tool) dictates what data is collected rather than a thought through business strategy aiding decision making.
Let’s look at some examples:
An organisation might be interested in reducing their energy costs. Sensors collecting data on energy consumption over time, which could then be analysed and aggregated by floors in the building, by teams or possibly by devices using energy (lights in unoccupied rooms, computers left on over night) can highlight where the biggest savings could be made.
A growing business might be thinking about how to accommodate an increasing number of staff in its existing premises. Collecting data on occupancy of desks, meeting rooms and break out areas can put a spotlight on utilisation rates of specific areas, show remaining capacities of spaces and pinpoint potentially under-utilised areas. Low levels of desk occupancy could lead to implementing an activity-based working strategy.
A Sales Director could be concerned about the time the sales force is spending with customers. Again, gathering insights into the numbers of times desks of sales staff are occupied, could provide baseline figures to understand how actual patterns of mobility play out over time, and whether different strategies to get the sales team out on the road are required.
Retention of staff could be high up on the agenda of a business suffering from increasing staff turnover. An employee engagement survey could highlight issues of motivation, leadership or satisfaction. Different data collection strategies could be employed, too, as discussed in a recent study by McKinsey, which used personality tests and a sensor-based data collection of interaction behaviours to solve the same issue and bust long-held myths of the senior management team on what works and what doesn’t.
A co-working space might want to know which shared facilities to offer and where to place them in order to attract new members and keep existing ones. Collecting data on how well facilities are used, possibly combined with a survey of its members (or an app that rates facilities) can give clues on preferences of co-working users. A spatial analysis of where facilities are placed and how easily reachable they are to what group of users could be another data collection strategy to aid this decision on the provision and placement of facilities.
What decision makers do with the data once it is collected is a crucial part of the question about what data to collect. You should always ask: How does my data inform decisions? How does it guide actions? The long answer to “What data should we be collecting?” is therefore “When you know the decisions that need to be taken and you understand the range of possible actions that might be decided on, you will know what data to collect”.
What is also clear, is that there is not a single answer to the question which data helps with which decision. In the examples above, seat occupancy data is useful in two different scenarios (accommodating growth and encouraging the sales force to spend time with customers). It also works the other way, since a single problem such as staff retention could be addressed with different data collection strategies. Which one works best is then a question that depends again, this time on what is feasible, practical, available and manageable. (We will talk about this one in more detail in our next blog, so watch this space).
So when someone answers your request for advice on workplace, or in fact any other data collection with a thoughtful “It depends…”, ask back “On what?”, then lean in and learn from the discussion that unfolds.
It’s time for a confession: we are data scientists but very frustrated ones. Data can shine a light on so much, yet there seems to be much confusion and even anxiety about it. In the face of mounting options of easy and automated methods of workplace data collection, we often get asked what data a business should collect. So here comes our answer in several parts, in the shape of a series of blog posts under the heading ‘What data what should we collect’. Welcome to Part 1: Why bother?
Businesses have taken decisions for hundreds of years, some good ones, some bad ones. So why all this fuss about data, analytics and data science now? Why should businesses collect and analyse their own data, for reasons other than that it appears trendy (and because Google does it, and that must mean it’s right)?
We would argue that data helps business leaders to take better decisions. In workplace design, a data-driven approach can address the needs of an organisation more profoundly than relying on intuition, opinion or office politics alone. The same goes for business strategies, where data can bring intelligence to the table. Ex-CEO of Netscape, Jim Barksdale’s purported statement ‘If we have data, let’s look at data. If all we have are opinions, let’s go with mine’ (as quoted in: ‘How Google works‘) emphasises the point.
But what role does data play exactly in decision-making?
Data can be seen as the raw material of bits and bytes that can contribute to information, as human beings begin to contextualise data and understand what they mean. For instance, 15 degrees Celsius in outside temperature is a data point. In the context of a day in August in London, that data point turns into information which tells us about a lousy British summer. Further interpretation turns information into knowledge, which includes guidance on how to act (in this case: wear a coat. Or go abroad for your holiday). Knowledge therefore means ‘knowing what could be done’ and as such has action possibilities already embedded. Through decision-making, possibilities turn into realities and have consequences in the world (if I wear a coat, I won’t freeze). The relationship between data and decision making is visualised below. If you want to read more about the data – information – knowledge relationship, I would recommend chapter 2 of Tina Chini’s ‘Effective Knowledge Transfer in Multinational Corporations‘.
You might now argue that you do not need data in decision making, since good leaders can decide based on their intuition or experience. That’s of course correct (at least partially) and many business decisions are made exactly like that. However, if you think about it very carefully, both intuition and experience follow the same logic from data to information to knowledge to decision making with the only difference that the process is more hidden and less obvious. If I decide based on intuition, I might have a hunch about something and might not be able to verbalise exactly why and how I think this is the right thing to do, but implicitly and subconsciously, I’m likely to follow the above logic. For experience, this is even clearer. Experience comes from accumulated knowledge over time. If I’ve experienced many British summers, I know what to expect and will be prepared. This means the difference between a data-driven decision making strategy and an intuitive, experience-based one is transparency and openness. Collecting data in a logic, open and rigorous way allows others to follow decision making processes. But it might also lead to challenging the unknown biases we all have. Nobel prize winner Daniel Kahnemann has explained the inevitable prejudices built into our reasoning as a problem of ‘Thinking fast and slow‘. Fast thinking, or intuitive judgments are often biased, because we don’t have the full story and processing statistics for example requires slow, effortful thinking. Thus a data-driven approach might not only be more transparent, but also more likely to lead to better results.
A particularly concise way of describing the relationship between data and deciding comes from Scott Berkun in his excellent book “A year without pants“, which tells the story of his work at WordPress.com. Scott contends that:
“Data can’t decide things for you. It can help you see things more clearly if captured carefully, but that’s not the same as deciding.”
This sums up nicely what data does: allowing you to see things more clearly. Berkun is also addressing another important point – the need to capture data carefully according to scientific principles. So data may lead to decision making by turning data into information and then knowledge, but data alone does not decide. It is human beings that do that and, hopefully, powered by data.
What happens when 21 people volunteer to take part in a live workplace experiment? An experiment that tests what factors govern the choices people will make when asked to find a work setting for a collaborative team task?
A couple of weeks ago we embarked on what was in itself an experimental formula: a kind of mash up of a spatial theory seminar, interactive workshop and live experiment, with some data collection.
Our ‘lab rats’ gamely volunteered themselves for this process; professionals from a range of backgrounds though mainly from workplace design and Facilities Management. Our laboratory was kindly provided by Herman Miller, in the shape of their newly fitted out showroom in Aldwych, London. This meant we had a range of furniture types and spatial locations to play with.
When we at brainybirdz first discussed the idea and then went on to design the ‘experiment’, it soon emerged that there was so much potential in the format, that it could easily take a day to run. But we only had 1 ½ hours at our disposal. With that caveat, we think we have nevertheless gained some significant insight from the event. And luckily our participants seemed also to find their own insights, which was an important objective too.
Our main finding was perhaps a surprising one. The participants were allowed to choose a workplace setting for a collaborative team task having first explored the showroom. We had expected there to be convergence around maybe two or three favoured locations, but with 10 teams, 8 favourites were identified. Then, when the teams were asked to go and occupy a work setting to actually complete the task, only 4 teams ended up in the location they originally chose.
Based on the feedback we received and our own previous research, it appears several inter-related factors were at work in these divergent choices. These were:
Perception of the task
Spatial visibility is a way of describing the spatial quality of any location within a complex floorplan in terms of how visible it is to everywhere else. We use a software tool to measure this, but broadly speaking an central open space with good lines of sight will show up with high visibility and an enclosed space in a tucked way in a corner somewhere will have low visibility or be secluded. In our experiment, four of the favourite work settings identified were in visible locations and six in secluded locations. Even though several teams ended up in a different location to the one originally chosen, team preferences for seclusion or visibility did not change, meaning for example that a team unable to work from their first choice secluded location then found themselves an alternative secluded setting. One team who chose and remained in a visible location, admitted that they had benefitted from seeing what other teams were doing in the collaborative task. Teams who chose to be more segregated mentioned the need for uninterrupted focus on the task.
Daylight was a factor specifically mentioned by some teams. One team that wanted a secluded spot managed to find one that also gave them access to daylight, which they reported was important to them. Three of the teams in visible locations within the showroom were also close to the window and two of them explicitly mentioned light as being a factor in their choice and the other team mentioned light as an important quality of their location.
Secluded and with daylight: one of the favoured team task locations
Furniture had an impact in a number of ways.
Comfort was the most often mentioned factor (9 out of 10 teams). People said they were comfortable in a number of different settings not just in upholstered sofas or chairs.
The way the furniture was positioned to allow those, in the mainly two person teams, to communicate and look at the same materials together was also mentioned as was having a large enough table top both to write on and to put down cups of coffee etc.. Capacity was also an issue – one team reported that they ruled out some locations because they seemed to big for just two people and clearly one-person settings were not chosen.
However adaptability was the other most significant factor after comfort. For one team this meant being able to sit or stand, but interestingly, more generally there were several references made to liking the possibility of changing the environment around, depending on what it would turn out was needed.
A work-setting that afforded space for writing during the team task
Personality type was not a factor we explicitly investigated. But it became evident from the comments that some made, that the choices around secluded and visible locations might well have something to do with introverted and extroverted personality types. This has been researched to some degree elsewhere and is a line of investigation that we think needs more attention.
Team dynamics can perhaps be seen as an extension to the ideas on personality type. In our experiment the teams of two, and in one case three people, were constituted on the day between participants who didn’t previously know each other. Some reported that they chose their favourite location because both team members had previously liked it. Others may have made choices because there was one, more dominant team member, whose preferences held sway. If the task had been an individual one, then people’s choices would have been totally individual and the resulting occupancy pattern maybe very different. Something else to investigate further.
Perception of the task was also crucial when it came to location choices. The original briefing was simply that participants should choose a favourite work setting where they could do a collaborative team task. When participants were asked to go and occupy a setting for the task, they were given more information and had a better idea of what might be involved. This may have contributed to the 6 out of 10 changes in where teams ended up compared to the favourite they had previously identified. It was not just that the spots they wanted were already taken.
Frequency of adjectives mentioned to describe the settings used for the collaborative team task
So in what was admittedly a small scale experiment, it seems clear that the interplay of all these factors set against the pool of what work settings are actually available will lead to divergent outcomes. This means there is no such thing as the perfect work setting for everyone, even if the task is specified. Adaptability and choice has to be key with an underpinning of comfort for all. Although this is a mantra often heard in workplace design, we now have some real evidence to back it up and, as is always the case in science, we have a lot of new questions that still need further investigation. Watch this space.
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