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The Industrial Revolutions and Data Science 85

Second is automation of so‐called back‐office functions, such as inventory management
and product design. Take the development of an automobile suspension system designed
using computer‐aided design. The new suspension must meet customer and testing require-
ments under a range of specific road conditions. After coming up with an initial design con-
cept, design engineers use computer simulation to show the damping effects of the new
suspension design under various road conditions. The design is then iteratively improved
based on these results.
Third is integration. Thus, in parallel to the design of the suspension system, purchasing
specialists and industrial engineers proceed with specifying and ordering the necessary raw
materials, setting up the manufacturing processes, and scheduling production using computer‐
aided manufacturing (CAM) tools. Then, throughout manufacturing, tests provide the
necessary production controls. Finally, CAM pulls everything together. Ultimately, of course,
the objective is to minimize the costly impact of failures in a product after delivery to the cus-
tomer. Computer simulations required new experimental designs, including Latin Hypercubes
and Kriging models. In addition, modern advances in optimization of statistically designed
experiments have led to new designs that better address constraints and exploit optimality
properties (for details on these methods, see Kenett and Zacks 2014).

The Fourth Industrial Revolution: The Industry 4.0 Transformation
We are now in the midst of the fourth industrial revolution, fueled by data from sensors
and IoT devices and powered by increasing computer power. Information technology,
telecommunications, and manufacturing are merging, and production is increasingly
autonomous. Futurists talk of machines that organize themselves, delivery chains that
automatically assemble themselves, and applications that feed customer orders directly
into production.
There are many implications for data scientists. According to IDC (2018), the top analytical
technologies include:

• natural language generation, natural language processing, and text mining
• speech recognition
• virtual agents
• machine learning platforms
• AI‐optimized hardware
• decision management.

We emphasize three common Industry 4.0 themes of special relevance to data scientists:
data quality (Chapter 6), information quality (InfoQ (Chapter 13), and the need to move
quickly up the analytics maturity ladder (Chapter 16).

Implications
It is trite to observe that we are in a period of rapid technological change. If anything, we
expect the pace of change to accelerate. Look how much more quickly the fourth industrial
revolution followed the third, and the third followed the second! Further, change stems