Introduction to the Fourth Industrial Revolution
The 21st century has been marked by disruption by new technologies to the societies which rely on them for productivity and livelihood. This disruption has driven an ever-quickening pace of technological revolution, projecting human industry into a pan-industrial fourth industrial revolution. This, dear reader, is the topic of this series and today’s article, a glimpse into the future of decision making.
Fuel and Refinement
During the third industrial revolution, which occurred through the 20th century industrialized societies relied upon oil as a base and upon refined oil (gasoline) as its fuel. In evolution, the fourth industrial revolution is built upon data as its base, and refined data as its fuel. (You may already recognize the popular hype term “data-driven” at the heart of most modern pitch decks). I want to direct your attention to the value of refinement, which whittles away the mountains of ones and zeroes into patterns and useful conclusions. The process of data refinement can be completed by humans or by machines, or by a combination of both.
Examples
As a simple example, consider a marketing team who receives a report about their last month’s performance, a report full of statistics on views, engagement, hours watched, etc. On initial reception this raw data is overwhelming, perhaps even confusing. The manager summons interns who pore through the raw data, make sense of it, and organize it into a (hopefully) short PowerPoint presentation. That presentation is an example of refined data: valuable and useful information to guide the organization’s next moves.
As a technical example, consider a LIDAR sensor on a smart Tesla sedan. This sensor operates by projecting light to a target then measuring the time it takes to “bounce” back, repeating several hundred times per second. This system’s raw data, then, is a mountain of very small measurements of time. Rather than present the user with a screen full of nanosecond measurements, the system “refines” that raw data into a proximity alert which activates when the driver accidentally swerves toward another vehicle. This proximity alert is a prime example of raw data “refined” by a digital system into useful, valuable data.
As a human-driven example, consider the CIA. Put bluntly, the CIA is really a large pyramid of analysts who receive and condense situational reports, passing them up and up the food chain to where directors make critical decisions. Compared to the LIDAR example, however, there are dozens if not hundreds of levels of refinement where at each level is another set of eyes trained to 1) make sense of the wide range of data served to them, and 2) extract that data deemed important, flag that deemed critical, and discard that deemed irrelevant.
Let’s not be naive: refined data has been valuable for much of human history. However, the advent of digital computing and the remarkable technologies available today mean that more raw data than ever before can be collected, refined, and applied in useful ways, particularly in technical or digital examples, like reactive robots or smart manufacturing. This concept has and will continue to be used in thousands of applications across dozens of industries and their verticals.
Producing Value
The notion to producing value in the fourth industrial revolution is thus: increase your flow of process data with sensors or similar inputs, optimize the refinement/processing of that data with powerful analytics programs, apply that knowledge to the process, improve, and continue to iterate at higher productivity.
This revolution is pan-industrial, meaning that it is not restricted to a small group of verticals. Any organization in nearly any industry can take advantage of powerful feedback loops to improve their operations. Competitive advantages stem from the ability to gather, process, and apply refined data to the operation. Waste can be understood in the amount of data gathered and discarded by the lack of systems in place to process it. Just as oil was the asset du jour of the third revolution, data is the fuel of the fourth industrial revolution.
Thus most strategies of the fourth industrial revolution are based around considerations about that fuel: data acquisition, data storage, data processing, data security. In this series through the Griffin Review, we will examine each of those considerations and many applications through different industries.
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To tap into some of this refined data about the fourth industrial revolution, consider listening to my audiobook series of Klaus Schwab’s 2016 book The Fourth Industrial Revolution.
