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17

The Industrial Revolutions

and Data Science

This chapter provides an industrial context into the advance of data science. This context is
important because it illustrates an important role for CAOs and senior managers – namely,
identifying macrotrends based on evidence from disparate sources and positioning their com-
panies to take advantage of them.
Consider five work environments:

1. Craft activities. An artisanal system where workers learn their trade through apprentice-
ship. Learning accumulates through experience, and little data is kept.
2. Repetitive activity. Machinery makes it possible to complete repetitive work more effi-
ciently. The iconic breaokthrough is the water‐powered loom, and it characterizes the first
industrial revolution. Data from inspections is used to certify products.
3. The factory. Fully contained factories utilizing repeatable processes produce better, cheaper
goods for the masses. These characterize the second industrial revolution. Data is used for
process control.
4. The automated factory. In the third industrial revolution, computers started managing
processes. In principle, a suite of integrated applications supports the entire factory, from
managing inventory to tracking work orders.
5. Industry 4.0. The fourth industrial revolution is unfolding now. It features unprecedented
levels of data, including sensors that track heat expansion, vibration, and noise in produc-
tion, cyberphysical systems, IoT devices, and advanced analytics to process it all.

The term Industry 4.0 is applied in many contexts, for example, Healthcare 4.0, Hotel
Industry 4.0, Food 4.0, and Education 4.0. All follow trajectories that parallel those taken in
manufacturing.

The Real Work of Data Science: Turning Data into Information, Better Decisions, and Stronger Organizations,
First Edition. Ron S. Kenett and Thomas C. Redman.
© 2019 Ron S. Kenett and Thomas C. Redman. Published 2019 by John Wiley & Sons Ltd.
Companion website: www.wiley.com/go/kenett-redman/datascience