THE PROBLEM

Each of us at this conference is living proof that the demand for ethnographic knowledge is at an all-time high. From the many backgrounds that we represent, the knowledge we generate is being disseminated across ever-wider fields of activity. That is a good thing. But increased demand exerts a powerful force, tending to transform any production resource into a commodity. That force applies even when the resource in question is an intangible one like knowledge. This in turn has serious implications for the way we do our work, and my talk today will review some warning signs of a particular problem known to labor economists as de-skilling.

For a long time, the status of ethnographic labor in the business world benefited from the relative naïveté of many of our clients, which allowed us to behave like artisans with secret skills—like a pre-capitalist European guild or a traditional craft union at the peak of the industrial age. Our work was rooted in our professional traditions, passed on through various kinds of apprenticeship, and was essentially under our direct control. But as more businesses want more of what we offer, they need to more tightly integrate us with the control systems that underpin all modern companies. Our work needs to be more predictable, more standardized, have tighter linkage to ROI, faster throughput, and quicker turnaround. One client put it to me this way: “How can I get 80% of the value of a full-blown ethnographic study, for 60% of the cost, in about half the time?”

Those numbers tell a familiar story. In fact, most of business history can be summarized in that one sentence. Companies must continually ratchet up their resource efficiency or they will lose the game. When the resource in question is people—us, in this case—I have observed that there are three strategies for achieving this:

1. Increase the number of workers, which may include simplifying the definition of a qualified worker to make it easier to be one—in other words, expand the labor pool.
2. Require workers to work longer and/or faster for the same amount of money— speed up the labor pool.
3. Increase efficiency by transforming an “artisanal” and self-directed work process into a fully rationalized one—de-skill the labor pool.

Option one is not something the business world has much control over yet, although it constantly tries by accepting the vaguest possible definition of what the work consists of and who can or should do it. Option two is self-explanatory: I suspect all of us have had to work longer hours and more intensively in recent years, without always seeing our salaries go up correspondingly. Option three is a bit more complicated, and it is what I want to focus on with examples from my own experience.

But first, a definition of de-skilling. Here’s how to de-skill any job: First, you break it down into pieces, and parcel those pieces out among multiple actors who get paid variable rates based on the minimum expertise needed to do their part. Then you remove human autonomy and variability by making people work according to standard routines. To the degree possible, you take the humans out altogether and replace them with machines (of the hard or soft variety). If you can extract the creative mental elements from the job and turn them into private property, you do that too.

Despite my alarmist description, de-skilling can be beneficial. By embedding human knowledge into machines and routines, de-skilling allows its opposite, namely re-skilling. People can concentrate on higher-order activities and leave the boring work to the robots. But at the same time it usually has negative impact on the people most directly affected. For one thing, average wages go down. For another, people tend to lose their workplace autonomy—how they do what they do, and how they relate to their co-workers who may be doing some other portion of the work. As usual, Marx has an appropriate bon mot: “In order to make the collective laborer, and through him capital, rich in social productive power, each laborer must be made poor in individual productive powers” (Marx 1967 [1887]:361). In factories, the enforcer of this regime may be a time and motion specialist who determines the optimal sequence of steps for a job. There still are such people, and there’s a steady business in time and motion analysis software. For business ethnographers, it is done by a more subtle set of forces intrinsic to the way the global economy works, and for the most part we do it to ourselves.

DE-SKILLING AS A BUSINESS MODEL

A few years ago, together with two colleagues at a leading market research company, I helped create a product called HomeView®, in which a database of ten thousand photographs of domestic interiors in twelve countries was linked to a global consumer survey. It was designed as an open-ended creative tool for generating connections and hypotheses, a hybrid device that blended some of the benefits of qualitative material culture research with the rigor and certitude of quantitative studies. To work as intended, we had to first analyze the contents of all ten thousand images, making judgments about things like people’s propensity to create collections of objects, or the degree of intentionality in how their spaces are decorated, or the presence of a “node” where activities intersect, and then tagging those judgments as meta-data. Complexly interrelated criteria for the analysis of material culture had to become multiple choice entries in a coding interface.

Coding each photo took at least ten minutes, if you worked fast and somewhat unthinkingly. Ten minutes times ten thousand is sixteen hundred and sixty-six hours, so to be cost effective, we planned to send the job to our company’s outsourcing partner in India, where it would be done by general-purpose research workers paid far less than my colleagues and me, and minimally trained to produce fairly consistent interpretations (we hoped) of what the photos were telling them about hundreds of brands and products in situ.