UNDERLYING THE PRACTICE and study of management is the belief that it is a science and that business decisions must be driven by rigorous analysis of data. The explosion of big data has only reinforced this idea. In a recent EY survey, 81 per cent of executives said they believed that “data should be at the heart of all decision-making,” leading EY to proclaim that “big data can eliminate reliance on ‘gut feel’ decision-making.”
Many managers find this notion appealing. But is it true that management is a science? And is it right to equate intellectual rigour with data analysis? If the answers to those questions are no and no — as we suggest in the following pages — then how should managers arrive at their decisions?
In this article we will set out an alternative approach for strategy-making and innovation — one that relies less on data analysis and more on imagination, experimentation and communication. But first let’s take a look back at where — or ratherwith whom — science started.
Is Business Really a Science?
What we think of as science began with Aristotle, who as a student of Plato was the first to write about cause and effect and the methodology for demonstrating it. This made ‘demonstration’, or proof, the goal of science and the final criterion for ‘truth’. As such, Aristotle was the originator of the approach to scientific exploration, which Galileo, Bacon, Descartes and Newton would formalize as ‘the scientific method’ 2,000 years later.
It’s hard to overestimate the impact of science on society. The scientific discoveries of the Enlightenment — deeply rooted in the Aristotelian methodology — led to the Industrial Revolution and the global economic progress that followed. Science solved problems and made the world a better place. Small wonder that we came to regard great scientists like Einstein as latter-day saints. And even smaller wonder that we came to view the scientific method as a template for other forms of inquiry and to speak of ‘social sciences’ rather than ‘social studies’.
You can’t chart a course for the future or bring about change merely by analyzing history.
But Aristotle might question whether we’ve allowed our application of the scientific method to go too far. In defining his approach, he set clear boundaries around what it should be used for, which was understanding natural phenomena that “cannot be other than they are.” Why does the sun rise every day? Why do lunar eclipses happen when they do? And why do objects always fall to the ground? These things are beyond the control of any human, and science is the study of what makes them occur.
However, Aristotle never claimed that all events were inevitable. To the contrary, he believed in free will and the power of human agency to make choices that can radically change situations. In other words, a great many things in the world can be other than they are. “Most of the things about which we make decisions, and into which we therefore inquire, present us with alternative possibilities. All our actions have a contingent character; hardly any of them are determined by necessity,” he wrote. Aristotle believed that this realm of possibilities was driven not by scientific analysis but by human invention and persuasion.
We think this is particularly true when it comes to decisions about business strategy and innovation. You can’t chart a course for the future or bring about change merely by analyzing history. We would suggest, for instance, that the behaviour of customers will never be transformed by a product whose design is based on an analysis of their past behaviour.
Transforming customer habits and experiences is precisely what great business innovations do. Steve Jobs, Steve Wozniak and other computing pioneers created brand-new devices that revolutionized how people interacted and did business. The railroad, the motor car and the telephone all introduced enormous behavioural and social shifts that an analysis of prior data could not have predicted. To be sure, innovators often incorporate scientific discoveries in their creations, but their real genius lies in their ability to imagine products or processes that simply never existed before.
Can or Cannot?
Most situations in life involve some elements that you can change and some that you cannot. The critical skill is spotting the difference. You need to ask, Is this situation dominated by possibility (that is, things we can alter for the better) or by necessity (elements we cannot change)?
Suppose you plan to build a bottling line for plastic bottles of springwater. The standard way to set one up is to take ‘forms’ (miniature thick plastic tubes), heat them, use air pressure to mould them to full bottle size, cool them until they’re rigid, and finally, fill them with water. Thousands of bottling lines around the world are configured this way.
Some of this cannot be other than it is: How hot the form has to be to stretch; the amount of air pressure required to mould the bottle; how fast the bottle can be cooled; how quickly the water can fill the bottle. These are determined by the laws of thermodynamics and gravity — which executives cannot do a thing to change. Still, there is an awful lot that can change. While the laws of science govern each step, the steps themselves don’t have to follow the sequence that has dominated bottling for decades. A company called LiquiForm demonstrated that after asking, ‘Why can’t we combine two steps into one by forming the bottle with pressure from the liquid we’re putting into it, rather than using air?’ And that idea turned out to be utterly doable.
Executives need to deconstruct every decision-making situation into can and cannot parts and then test their logic. If the initial hypothesis is that an element can’t be changed, the executive needs to ask which laws of nature suggest this. If the rationale for cannot is compelling, then the best approach is to apply a methodology that will optimize the status quo. In these cases, let science be the master and use its toolkits of data and analytics to drive choices.
In a similar way, executives need to test the logic behind classifying elements as ‘cans’. What suggests that behaviours or outcomes can be different from what they have been? If the supporting rationale is strong enough, let design and imagination be the co-masters and use analytics in their service.
It is important to realize that the presence of data is not sufficient proof that outcomes cannot be different. Data is not logic. In fact, many of the most lucrative business moves come from bucking the evidence. LEGO chairman Jørgen Vig Knudstorp offers a case in point. Back in 2008, when he was the company’s CEO, its data suggested that girls were much less interested in its toy bricks than boys were: 85 per cent of LEGO players were boys, and every attempt to attract more girls had failed. Many of the firm’s managers, therefore, believed that girls were inherently less likely to play with the bricks. They saw this as a cannot situation; but Knudstorp did not. The problem, he thought, was that LEGO had not yet figured out how to get girls to play with construction toys. His hunch was borne out with the launch of the successful LEGO Friends line, in 2012.
The LEGO case illustrates that data is no more than evidence, and it is not always obvious what it is evidence of. Moreover, the absence of data does not preclude possibility. If you are talking about new outcomes and behaviours, then naturally there is no prior evidence. A truly rigorous thinker, therefore, considers not only what the data suggests, but also what could happen within the bounds of possibility. And that requires the exercise of imagination — a very different process from analysis.
Breaking the Frame
The status quo often appears to be the only way things can be — a perception that is hard to shake. As a result, the imagination of new possibilities first requires an act of un-framing.
We recently came across a good example of the status quo trap while advising a consulting firm whose clients are non-profit organizations. The latter face a ‘starvation cycle’, in which they get generously funded for the direct costs of specific programs but struggle to get support for their indirect costs. A large private foundation, for instance, may fully fund the expansion of a charity’s successful Latin American girls’ education program to sub-Saharan Africa, yet underwrite only a small fraction of the associated operational overhead as well as the cost of developing the program in the first place. This is because donors typically set low and arbitrary levels for indirect costs — usually allowing only 10 to 15 per cent of grants to go toward them, even though the true indirect costs make up 40 to 60 per cent of the total tab for most programs.
The consulting firm accepted this framing of the problem and believed that the strategic challenge was figuring out how to persuade donors to increase the percentage allocated to indirect costs. It was considered a given that donors perceived indirect costs to be a necessary evil that diverted resources away from end beneficiaries.
We got the firm’s partners to test that belief by listening to what donors said about costs rather than selling donors a story about the need to raise reimbursement rates. What the partners heard surprised them. Far from being blind to the starvation cycle, donors hated it and understood their own role in causing it. The problem was that they didn’t trust their grantees to manage indirect costs. Once the partners were liberated from their false belief, they soon came up with a wide range of process-oriented solutions that could help non-profits build their competence at cost management and earn their donors’ confidence.
Although listening to and empathizing with stakeholders might not seem as rigorous or systematic as analyzing data from a formal survey, it is in fact a tried-and-true method of gleaning insights that is familiar to anthropologists, ethnographers, sociologists, psychologists and other social scientists. Many business leaders — particularly those who apply design thinking and other user-centric approaches to innovation — recognize the importance of qualitative, observational research in understanding human behaviour. At LEGO, for example, Knudstorp’s initial questioning of gender assumptions triggered four years of ethnographic studies that led to the discovery that ‘girls are more interested in collaborative play than boys are’, which suggested that a collaborative construction toy could appeal to them.
Powerful tool though it is, ethnographic research is no more than the starting point for a new frame. Ultimately, you have to chart out what could be and get people on board with that vision. To do that, you need to create a new narrative that displaces the old frame that has confined people.
Why Metaphors Matter
We all know that good stories are anchored by powerful metaphors. As Aristotle once observed, “Ordinary words convey only what we know already; it is from metaphor that we can best get hold of something fresh.” In fact, he believed that a command of metaphor was the key to rhetorical success: “To be a master of metaphor is the greatest thing by far. It is a sign of genius,” he wrote.
When people link unrelated concepts, product innovations often result.
It is perhaps ironic that this proposition about an unscientific construct has been scientifically confirmed. Research in cognitive science has demonstrated that the core engine of creative synthesis is ‘associative fluency’—the mental ability to connect two concepts that are not usually linked and to forge them into a new idea. The more diverse the concepts, the more powerful the creative association and the more novel the new idea.
With a new metaphor, you compare two things that aren’t usually connected. For instance, when Hamlet says to Rosencrantz, “Denmark’s a prison,” he is associating two elements in an unusual way. Rosencrantz knows what ‘Denmark’ means, and he knows what ‘a prison’ is. However, Hamlet presents a new concept to him that is neither the Denmark he knows nor the prisons he knows. This third element is the novel idea or creative synthesis produced by the unusual combination.
When people link unrelated concepts, product innovations often result. Samuel Colt developed the revolving bullet chamber for his famous pistol after working on a ship as a young man and becoming fascinated by the vessel’s wheel and the way it could spin or be locked by means of a clutch. A Swiss engineer was inspired to create the hook-and-loop model of Velcro after walking in the mountains and noticing the extraordinary adhesive qualities of burrs that stuck to his clothing.
Metaphor also aids the adoption of an innovation by helping consumers understand and relate to it. The automobile, for instance, was initially described as ‘a horseless carriage’, the motorcycle as ‘a bicycle with a motor’ and the snowboard was simply ‘a skateboard for the snow’. The very first step in the evolution that has made the smartphone a ubiquitous and essential device was the launch in 1999 of Research in Motion’s BlackBerry 850, which was sold as ‘a pager that could also receive and send emails’ — a comforting metaphor for initial users.
One needs only to look at the failure of the Segway to see how much harder it is to devise a compelling narrative without a good metaphor. The machine, developed by superstar inventor Dean Kamen and hyped as the next big thing, was financed by hundreds of millions in venture capital. Although it’s a brilliant application of advanced technology, hardly anyone uses it. Many rationalizations can be made for its failure — the high price point, the regulatory restrictions — but we would argue that a key reason is that the Segway is analogous with absolutely nothing at all. It is a little wheeled platform on which you stand upright and
largely motionless while moving forward. People couldn’t relate to it. You don’t sit, as you do in a car, or pedal, as you do on a bicycle, or steer it with handles, as you do a motorcycle.
Think of the last time you saw a Segway in use. You probably thought the rider looked laughably geeky on the contraption. Our minds don’t take to the Segway because there is no positive experience to compare it to.
Choosing the Right Narrative
When you’re facing decisions in the realm of possibilities, it’s useful to come up with three or four compelling narratives, each with a strong metaphor, and then put them through a testing process that will help you reach consensus around which one is best. What does that entail? In the cannot world, careful analysis of data leads to the optimal decision. But in the can world, where we are seeking to bring something into existence, there is no data to analyze. To evaluate your options, you need to do the following:
1. CLARIFY THE CONDITIONS. While we have no way of proving that a proposed change will have the desired effect, we can specify what would have to be true about the world for it to work. By considering this rather than debating what is true about the world as it is, innovators can work their way toward a consensus. The idea is to have the group agree on whether it can make most of those conditions a reality — and will take responsibility for doing so.
This was the approach pursued many years ago by a leading office furniture company that had developed a new chair. Although it was designed to be radically superior to anything else on the market, the chair was expensive to make and would need to be sold at twice an office chair’s typical price. The quantitative market research showed that customers reacted tepidly to the new product. Rather than giving up, the company asked what would have to be true to move customers from indifference to passion. It concluded that if customers actually tried the chair, they would experience its breakthrough performance and become enthusiastic advocates. The company went to market with a launch strategy based on a customer trial process, and the chair has since become the world’s most profitable and popular office chair.
2. CREATE NEW DATA. The approach to experimentation in the can world is fundamentally different from the one in the cannot world. In the cannot world, the task is to access and compile the relevant data. Sometimes that involves simply looking it up — from a table in the Bureau of Labour Statistics database, for example. Other times, it means engaging in an effort to uncover it — such as through a survey. You may also have to apply accepted statistical tests to determine whether the data gathered demonstrates that the proposition — say, that consumers prefer longer product life to greater product functionality—is true or false.
In the can world, the relevant data doesn’t exist because the future hasn’t happened yet. You have to create the data by prototyping — giving users something they haven’t seen before and observing and recording their reactions. If users don’t respond as you expected, you plumb for insights into how the prototype could be improved — and then repeat the process until you have generated data that demonstrates your innovation will succeed.
Of course, some prototyped ideas are just plain bad. That’s why it’s important to nurture multiple narratives. If you develop a clear view of ‘what would have to be true’ for each and conduct prototyping exercises for all of them, consensus will emerge about which narrative is most compelling in action. And involvement in the process will help the team get ready to assume responsibility for putting the chosen narrative into effect.
The fact that scientific analysis of data has made the world a better place does not mean that it should drive every business decision. When we face a context in which things cannot be other than they are, we can and should use the scientific method to understand that immutable world faster and more thoroughly than any of our competitors.
But when we use science in contexts in which things can be other than they are, we inadvertently convince ourselves that change is not possible. Only when it is too late will we realize that an insurgent has demonstrated to our former customers that things indeed can be different. That is the steep price of applying analytics to the entire business world rather than just to the appropriate parts of it.
Roger L. Martin
is the former Dean of the Rotman School of Management (1998-2013) and is currently ranked as the world’s top management thinker by the UK-based Thinkers50. His most recent book is Creating Great Choices: A Leader’s Guide to Integrative Thinking
(HBR Press, 2017), coauthored with Jennifer Riel. Tony Golsby-Smith
is the CEO and founder of Second Road, a consulting firm based in Sydney, Australia that is now part of Accenture Strategy. ©Harvard Business Publishing; all rights reserved. Reprinted with permission.
This article appeared in the Fall 2019 issue. Published by the University of Toronto’s Rotman School of Management, Rotman Management explores themes of interest to leaders, innovators and entrepreneurs.
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