What even IS this? Why tech companies are still failing us

Why do we know so little about the social implications of technology? It plays a starring role in everyday life, as essential as food, shelter, and clothing. A huge share (70%) of Americans use social media, and even 65% of senior citizens use Facebook  – that’s more than the number of people who eat family dinner at home, attend church, or have a pet.

Yet, we know so little about technology’s impact on everyday life. We are only just now recognizing problems like coordinated disinformation, breaches of personal data, and algorithmic discrimination.  Clearly, technology companies are falling short on understanding the social implications of their tools before and after they build them. But why? Why are tech companies failing us?

Woman in kitchen.
Woman in kitchen. Source: Art Institute of Chicago

Sadly, it is all too predictable that technologists underestimate, misjudge, or otherwise underappreciate how humans will interact with their technology. This is for one simple reason: engineering, as a discipline, does not bother to ask:  “What is this?”

Engineers are not scientists, much less social scientists. They typically have no knowledge of basic human behavior such as loss aversion or impression management, even though these are the building blocks of social interaction – and entry-level knowledge for social scientists.

Engineers could ask, “What is this?” but instead choose to ask: “Does this work?”

“Does this work?” underpins research within tech companies. Once upon a time, tech companies hired engineers they called research scientists and stuck them in labs to tinker endlessly with pieces of hardware and scraps of computer code. Even today, there are over 7800 job postings for “research scientist” on LinkedIn, which are typically engineers or computer scientists. A posting for an Uber research scientist intern is instructive. In addition to having a Master’s degree in a “technical field,” the intern is also encouraged to engage in “risk taking” and to “turn the dreams of science fiction into reality.”  Another job posting for a research scientist at Facebook asks for skills in the scientific method, but then specifically narrows that down to “evaluate performance and de-bug.” In other words: Does this work? Notably not mentioned: the ability to develop basic knowledge.

Academics would see much of this activity as more akin to prototyping than to scientific inquiry. Indeed, these engineers produced many technology prototypes, but not much in the way of generally applicable knowledge, or what the rest of us might call “science.” In other words, they never seem to stop and ask, “What is this?”

Painting by Salvador Dali
Inventions of The Monsters

Today, tech companies need to ask things like “What is a digital public sphere?” and “What is the nature of privacy?” and “What is artificial intelligence versus human intelligence?” Tech companies need typologies of human-computer interactions, motivations, fears, and human foibles. They need to create a system of knowledge around key questions of technology like artificial intelligence and social media.

Some argue that technology development doesn’t have time for “understanding,” that asking “What is this” takes too long and is too expensive. But this is a false economy. Philosopher Martha Nussbaum tells us plainly that we need that understanding, not for understanding’s sake but because it guides our planning:

“Understanding is always practical, since without it action is bound to be unfocused and ad hoc.” — Martha Nussbaum

In other words, if you don’t know “What is this” you’re probably going to build the wrong thing.

We can see this pattern of building the wrong thing in technology, over and over again. The term “user friendlywas invented way back in 1972. Curiously, “user hostile” wasn’t invented until 1996, just before Microsoft’s infamous Clippy appeared in 1997. Clippy’s abrupt entre onto the desktops of the world indicated that technology “researchers” had no idea what they had made. Word famously exploded from what appeared to be a digital typewriter, to a swollen behemoth that did everything from create a newsletter to automate mailing labels. Pick a lane, people. Clippy was there to tell users how to make Microsoft Word work, but no one bothered to find out much less explain what Microsoft Word actually was.  Word is still so swollen that a new user today can credibly ask “What even IS this?”

Clippy the paperclip
Source: NYMag.com

Flash forward to today, and the so-called “lean startup” approach to building technology is really just a faster, even more facile way to ask “Does this work.” In reality, tech companies still don’t know, “What is this?” even after they’ve built a working prototype.

In my former role as a hiring manager at a major tech company, it took an average of 100 days to hire just one ethnographer and more often than not, the job remained open much longer than that. These are the very people who can tell us, “What is this?” The demand for these social scientists only grows. Yet, the tech industry as a whole has not yet figured out they need to ask “What is this?” before they build something.

Were tech companies to ask, “what is this,” they would learn the basic properties of their tools, their coherence, intelligibility, performance, and affordances. Instead, they are fully occupied with “does this work,” and create horrific blights on our collective consciousness like Tay, the racist AI Bot on the relatively innocuous end of the scale, and Compass, the racist parole algorithm at the full-on evil end of the scale.

Technologists do not know what they do not know. Ethnographers hope for the day when they can just ask “What is this” without worrying about whether it works, because it doesn’t even exist yet. But tech development continues apace.

It’s time for ethnographers to stop this sad venture, and instead insist on asking: What IS this? Before another Tay, before another Compass. Technologists too must take responsibility because if we don’t, the 21st century will become even more technocentric, and even less intelligible. Let’s find out what’s going on before we build anything else.

Qualitative research and innovation

Qualitative research is not generally considered “real” research, and this has terrible implications for innovation. Companies’ thirst for operational effectiveness begs for quantitative data. But quantitative data does not and cannot form strategy. Qual data are a key ingredient to strategy, or the development of new and differentiated products.

Many people are familiar with Michael Porter’s famous paper, “What is strategy?”  Porter famously argued that many companies mistake operational effectiveness for corporate strategy. Operational effectiveness, according to Porter, is about quality, productivity, and speed. It is about doing the same thing as others, but doing it better.

Strategy, by contrast, is about being different. It is about doing entirely different activities to deliver value to customers. Other authors have called this the “blue ocean” or finding a place in the market that is calm, unoccupied, and yours for the taking.  A “red ocean” is full of competitors, doing exactly the same thing as you are, and demanding ever higher performance.

porter

Framed this way, it is clear that operational effectiveness relies heavily on quantitative data. How efficient are we? How do we stack up against the competition? How good are our products? How fast do we make them?

Operational effectiveness simply begs for quantitative data, and now that we have access to petabytes of passively collected data relating to productivity, quality, and speed, it is easier than ever to be operationally effective.

Or it should be. We know that quantitative data requires a great deal of cleaning, massaging, and managing, not to mention analysis, to make it useful for operational effectiveness.

The shift to data-driven operations has demanded a great deal of companies’ attention, mostly because data collection and analysis is not as easy as most think it to be.

But let us not mistake this for strategy.

There is nothing inherent to benchmarking performance that lends itself to strategic advantage. Quantitative data does not reveal how or in what ways customers are making their own workarounds. Quantitative data shows us how many products meet a particular standard, how many products are produced or sold, or how fast a company makes them. It can tell you the average satisfaction a customer may have, but it cannot reveal any of the detail behind that satisfaction.

In their insightful Harvard Business Review article, “An Anthropologist Walks Into A Bar,” Christian Madsbjerg and Mikkel Rasmussen argue that qualitative research gives companies the ability to bridge the “complexity gap,” or what a study of 1500 CEOs revealed as their main challenge. Why do customers do what they do? You must do qualitative research to find out. And, by extension, you must do qualitative research to innovate.

mads

 

Qualitative research is explicitly about revealing detail. Qualitative research shows how people are using products, or how these products sit and gather dust in the corner of the kitchen. Qualitative research, particularly field-based research like ethnography, offers that path to delivering truly different products.

Companies that do ethnography regularly uncover entirely new or different ways to deliver value to customers. Oftentimes, this is done unsystematically. Skillful product and brand managers know that observing everyday life can reveal the how and the why of product failure.

Nike is a great example of a company that is in touch with culture. Its marketers are largely acknowledged to be among the best in the world. Its product innovation is continual, and its brand equity is unparalleled. Even their lab-based researchers like the fabulously named Gordon Valiant are active members of the running community.  His lab-based practice is complemented by regular participation in running events, where he comes into contact with other runners.

Valiant conducts in-lab studies systematically, but observes human behavior in an ad hoc way. Imagine the advantage to companies that do this research systematically. Imagine having a steady stream of insight into real people and why they do what they do. Imagine having thick description of painful workaround work, and regular replenishment of unmet customer needs.

That can only come from systematic, regular, and rigorous qualitative research.

Compare that to a company tirelessly benchmarks its quality, productivity, and speed. The company with qualitative insight into human behavior will have almost limitless potential to do things differently, to deliver new products or services, to find entirely unexamined oceans of product innovations.

Yet we spend almost nothing on qualitative research.  Esomar, the international market research association, estimates that in 2013, corporations spent $6.6B USD on qualitative research, worldwide. The vast majority of money spent on qualitative research is on focus groups, but Esomar estimates almost $1.6B is spent on the more interpretive methods of in-depth interviewing and ethnography. This amount is dwarfed by the $32.4B USD spent on quantitative market research.

It could be argued that many companies need to start with operational effectiveness. Fair enough. But no company can survive on quality, productivity, and speed alone. It is too competitive a marketplace. Qualitative research, therefore, is a cheap way to guide the company toward different product offerings, and ultimately, toward innovation.