It’s indisputable that technology is displacing many of today’s jobs. The question is: what should, or can, we do about it? This series explores the possible consequences of this shift and how information use and decision making support can, and should, drive a better outcome through enhanced and expanded Business Intelligence. Part 3 is dystopian; I apologize in advance.
“The massive forces of globalization and technological progress are removing the need for a lot of the previous kind of white-collar workers,” according to Andrew McAfee of the Center for Digital Business at the M.I.T. Sloan School of Management in a recent New York Times article. It’s just the logical outcome of the trends described in Part 1 and Part 2 of this series. The outcome is the increasing technological displacement of traditional middle and lower-middle job types, combined with continuing downward pressure on wages for these jobs. In response, job seekers are forced to accept lower skilled and paid jobs, often as gig work, without long term security, as well as holding down multiple jobs to make ends meet. Lower incomes and less leisure time will drive down consumption of mass-produced goods, and cause producers to cut costs further, driving further unemployment. A classic race to the bottom. This simple analysis applies to Western economies. In developing economies, further factors come into play, which deserve deeper consideration, but the end result would appear to be largely the same. The economic impacts are severe. In fact, the economic model—as we have operated it for more than two centuries—becomes untenable.
This is not at all about the allegedly coming Singularity; it’s simply about the progression of technology. When technology displaces some yet to be determined percentage of labor, this system becomes unbalanced: there are simply not enough people with sufficient money to buy the products made, no matter how cheaply. We have not yet reached this tipping point because, throughout most of the past two hundred years, the new jobs created by technology have largely offset the losses. However, recent employment trends in the Western world suggest that this effect is becoming less effective.
The subsequent societal disruption can be imagined to be catastrophic. Increasing inequality, already visible today, drives social unrest. Strikes, both legal and “wildcat” become widespread. Many people, whose sense of identity and self-worth is tied to a productive job, drop out, abuse drugs and self-destruct. Violent protest against technologically driven change, already visible around Uber, grows by leaps and bounds. Economic migration, within and across national borders, in search of sustenance becomes endemic. Ghettoization of society ensues: vast sprawling near-shanty towns house the disempowered, while the reducing numbers of the elite retreat into gated communities behind high walls, razor wire and armed patrols.
The outcome may not (yet) reach the “Mad Max” scenario, but a visitor to Brazil or South Africa—to name but two of many examples—can immediately get an idea of how such a dystopian society can emerge, and is already doing so. Crime becomes a way of life, corruption abounds and society disintegrates. I believe that the most likely outcome of the “head in the sand” stance being taken by many economists and most politicians today is to end badly in a dystopian nightmare.
Whither BI in such an environment? With marketing, customer service and, even, worker productivity become memories of a bygone era, the role of BI must inevitably move to the maintenance of wealth and power for those who have them. While Mad Max may focus on mean machines built from scrap automobiles—and they make for more visceral movies—the dispossessed will continue to hack communications and computer security, making the role of data analytics as a defense even more important. But it’s a restricted and increasingly inwardly-focused BI in dystopia.
As a technologist or data management expert following this series, I imagine that the head-in-the-sand and dystopian stances make, at best, distressing reading. Must it end like this? Is there anything that we can do to avoid the Fall? I believe there is. There is a better way, and as I shall demonstrate in the fourth and final part of this series, business intelligence, big data and analytics will be important enablers of the utopian stance.