Playing To Win
The Myth of Objectivity & Strategy
An Island of Objectivity in an Ocean of Subjectivity
Last week, a longtime friend, ex-colleague and fellow writer, Steve, encouraged me to write a piece on my long-held view that the rise of science in the world has generated detrimental confusion concerning the concept of objectivity. I will tackle the subject in this Playing to Win/Practitioner Insights (PTW/PI) piece called The Myth of Objectivity & Strategy: An Island of Objectivity in an Ocean of Subjectivity. And as always, you can find all the previous PTW/PI here.
Enthusiasm for Science
The modern world is extremely keen on science, not without reason. The Scientific Revolution led to the Industrial Revolution, which produced a sharp increase in economic progress and standards of living in the newly industrialized countries. The scientifically derived inventions of the first and second Industrial Revolutions, such as steam power and electricity transformed lives. Science, featuring testing of hypotheses with objective data, helped the world advance its knowledge and escape from the bonds of superstition. All good!
The enthusiasm for science eventually made its way to business with Frederick Winslow Taylor applying science to management in his landmark 1911 book, The Principles of Scientific Management, to become one of the 20th century’s handful most important management thinkers. Another member of that handful, W. Edwards Deming, applied the science of statistics to management to create the field of Total Quality Management, further ensconcing science in business.
The following quote has always been attributed to Deming, though there is no proof that he said or wrote it: In God we trust; all others must bring data. Regardless, it encapsulates well the transformation business made during the 20th century to a discipline dominated by science.
By the last quarter of the 20th century, every business school in North America had begun teaching business as a scientific discipline in which decisions must be made based on objective data.
Arguably, science is the most powerful and intense light in the modern economy generally and the field of business more specifically.
The Shadow
But as I have argued before in series, the stronger the light shining in your face, the darker the shadow behind you. The dark shadow of the powerful light of science is the myth of objectivity, which holds that since the data is objective, what the data says is right and everything else is wrong.
However, there is a consistent and problematic confusion between the objectivity of the data itself and the objectivity — or lack thereof — of its collection and use.
I will illustrate with a familiar business data context: quantitative customer research. Let’s assume we carry out market research by surveying 1000 customers with a questionnaire composed of twelve questions, the ninth of which asks which is more important in making their purchase decision: speed of delivery or level of customization. Let’s further assume that the data show that 70% of consumers checked the speed of delivery box and 30% the customization box. That data itself is objective in that 100 people could independently tabulate the data because the 1000 customers had to check one box or the other and the tabulators would all agree that it shows a 70%/30% split.
But how was that objective data collected? How was it decided which 1000 customers to survey? Why was it 1000 customers? Which customers agreed to participate versus not? Why was this question placed ninth in the sequence of twelve questions? Why was it worded the way it was worded? How do we know how customers interpreted the meanings of ‘speed of delivery’ and ‘level of customization?’
Each of these elements that underpin the objectively verifiable data is highly subjective. While there is clearly reasoning behind each of those data collection decisions, most observers would not agree that any of those six questions above has a singular objective answer. Arguably, the 70%/30% split amounted to a tiny island of objectivity in a vast sea of subjectivity — respondent selection, question placement, question phrasing, respondent interpretation, etc.
Then we move to how that 70%/30% data is used. Let’s say that it is used to justify spending much more on speed than customization — let’s say for sake of argument, 70% versus 30% in keeping with the customer preference. Once again, that business decision is anything but objective. For example, it is possible that a competitor has an advantage on speed completely locked up and thus investments in speed would do little to improve results, while investment in customization for the 30% segment that cares deeply about it would be a superior response. And those are only two of the many possible ways to interpret and use the data to make a decision.
Proponents of data-based decision-making — and under the influence of the myth of objectivity — will hail whatever decision is made as being objective, scientific, and data-based due to the statistically-significant, quantitative survey. But in making this decision, the tiny island of objectivity was surrounded by a veritable ocean of subjectivity. However, the modern world focuses entirely on the tiny island to justify the decision as objective, scientific, and data based.
Long ago in 2007, I had a fun and memorable little public editorial battle with Bill Gates on this sort of myth problem. In conjunction with a visit to Canada, Gates penned an editorial in Canada’s leading business newspaper, The Globe & Mail, in which he argued that Canada’s future competitiveness and prosperity would be in danger unless we convinced more high school graduates to go into science and engineering programs. The objective data he used to ‘prove’ his argument was the steep decline in students entering first year of computer science programs in the US between 2000 and 2004 (FYI, 2004 was the latest year for which he had statistics available when he penned the piece, so I have no argument with his chosen end-year).
In my editorial response, I pointed out that the 2000 college entry year reflected a one-year spike in computer science enrollments in the US, right at the absolute height of the dot.com bubble, when students still thought that a computer science degree guaranteed wild riches. When I looked at the enrollment numbers, it is clear that when the bubble burst, lots of students interested in science and engineering decided on a discipline within science and engineering other than computer science specifically — like computer engineering — because enrollments in science and engineering overall over the 1985–2004 period had been remarkably stable. It turns out that the year 2000 was only year across those 20 years that Gates could have used as the base year to support his ‘dramatic decline’ argument.
Gates cited one objective fact — the 2000–2004 decline in entering computer science students in the US (not Canada) — and surrounded it with an ocean of subjectivity, such as selection of the base year, inferences about the link between computer science enrollment numbers and economic prosperity, and translation into impact in another country (Canada, whose data on enrollments was entirely different — doubt if he looked at those). Yet, given it was from a certifiable tech god, the hopelessly devoted Canadian press treated it as a fully objective argument — so gullible!
Impact on Polarization
Over my adult life, the myth of objectivity has gotten stronger and its negative impact on society worse — particularly with respect to driving polarization.
When we are under the delusion that our conclusions are based entirely on objective truths, if others disagree with our conclusions, we automatically see there as being only two possibilities. Either those disagreeing are stupid — they don’t understand the realities of the situation — or they are evil — they understand the realities but are denying them for nefarious reasons.
Why would anyone want to respect the opinion of and discuss further with a stupid or evil person? No. They are beneath contempt and should be ostracized by the people like me whose thinking is objectively based. In fact, they should be destroyed if possible.
And meanwhile, those stupid/evil people symmetrically believe that their views are entirely objective and that we are the stupid or evil people ones who are beneath their contempt and should be ostracized and/or destroyed.
There you have it: polarization in action. And built directly on top of the foundation of the myth of objectivity. The true magnitude of the differences between people haven’t gotten greater. Each side just feels to an ever-greater extent that it is objectively correct and other is objectively wrong. This is the very dark shadow of science. And it is quite ironic given that the purpose of science is to drive forward collective human understanding, not collective human misunderstanding.
Impact on Strategy
Closer to home, the myth of objectivity has three seriously detrimental impacts on strategy.
First, it reduces the quality of the strategy dialogue as participants quickly become attached to the (faux) objective veracity of their respective points of view and the (therefore) erroneous views of anyone who dares to see things differently. Since great strategy is the product of considering alternative possibilities and choosing the one for which the most compelling argument can be made, stifling dialogue stifles strategy.
Second, the myth of objectively makes strategy overly dependent on elements that are perceived to be objective but only contain a tiny sliver of objectivity — as with the 70%/30% consumer data or the 2000–2004 computer science data above. It is like building a tall building on a sand dune. That whole logical structure is built on an unstable foundation and is going to come crashing down.
Third, the myth of objectivity causes people to attempt to make objective features a bigger portion of the decision criteria than they can or should be. These are the folks that reject all qualitative research because it isn’t ‘objective’ and therefore will lead to a ‘biased’ decision. No good strategy decision will ever be made (though lots of dreadful ones will) solely based on quantitative data analysis — in part because, as I have written before, there is no data about the future, which is the era in which the strategy in question will operate. All good strategy is based more on judgement than on quantitative analysis, but the myth of objectivity convinces strategy people otherwise to their great detriment.
Practitioner Insights
The modern world, modern business, and modern strategy are all profoundly influenced by the myth of objectivity. It is not an exaggeration to say that the myth holds much of modern practice in its sway. Do not underestimate its dangerous power. You have been taught the myth in your formal education, had it reinforced in the media, and had it enforced in your business organization.
You need to be vigilant in your own thinking and work. Recognize that no strategy decision you are ever going to make will be entirely objective. The closest it can ever get is faux objective — as with Bill Gates above. Be as objective as you can be — but recognize the limits of objectivity. If your quantitative customer research contradicts what your salespeople believe about customers, don’t assume the salespeople are wrong because they aren’t ‘objective.’ Recognize that your quantitative customer research only contains a sliver of objectivity.
Use whatever objective elements you can garner but combine them with subjective insights to make the best bet about the future that you can make. And work hard to develop your qualitative appreciation skills. They are more important for a strategist than quantitative manipulation prowess, as I have discussed before.
Also, recognize that in convincing those enraptured by the myth of objectivity you need to emphasize the objective (even if it is faux objective as with Gates) elements of your decision because it will make them feel more comfortable and confident — even if they are delusionally so.
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As a reminder, I previewed in January 2025 that I am doing a PTW/PI podcast series with friend Tiffani Bova. The fourth in the series is on LinkedIn here on Wednesday, April 23rd at 12 noon EST and 9am PST. Look forward to seeing you there!
