Robert B. Cohen, PhD, Senior Fellow, Economic Strategy Institute, March 15, 2018
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Changes in the Macro Economy and the Rise of Big Data

Previous analysis1 predicted that nearly half of U.S. jobs would be at risk of being displaced due to the automation of more routine skills. A shortcoming of this work was its inability to estimate how the introduction of digital processes and operations built upon innovative computing and software might have more complementary impacts on key parts of the economy. This analysis addresses this gap.

As firms’ operations rely in a much greater way on data analytics and data-insights and Big Data, more intelligent analytics and improvements in software development processes are becoming more central to the success of operations and a key determinant of competitiveness. Once there is wider use of machine learning (ML), artificial intelligence (AI), visualization and other tools for sophisticated analytics, the performance improvements achieved by this new focus on data are likely to be more apparent:

1. 1. Firms will need to analyze how new processes impact their operations. This will put a premium on their ability to capture and interpret data in “real time”.
2. Big Data will become indispensable. Firms’ now focus on sharpening their perception of what data are critical to business decisions, but this will reinforce it. They also will amass crucial data sets and to hire those with skills to manage and oversee them. Data and how it is harnessed will help determine a firm’s competitiveness.
3. To strengthen the benefits of data analysis, firms will redesign how they develop software to assess data and interpret the performance of processes. This will accelerate the deployment of more streamlined tools.

Our approach to discern the impact of AI and ML is to focus on the scale and growth of intelligent functions. This perspective places analytics in a central economic role; analytics implement new efficiencies throughout the economy. If we add intelligence2 to our analysis of the roll-out or growing scale in which AI and ML are employed, we can define several levels of scaling or optimization that characterize how these technologies are applied in business processes:

1. 1. Asset optimization – based upon the Internet of Things, networks of sensors and data analytics. This reflects an increase in the management of individual pieces of equipment or services. Here, optimization relies upon data analysis and data science.
2. Facilities optimization – based on Application Performance Management (APM)3 as well as the initial stages during which machines will learn to make decisions about operations in concert with human operators and managers. “Brilliant” or intelligent factories define when firms reach this stage.
3. Fleet optimization - where fleets of planes or other means of transportation, groups of factories or large facilities such as hospitals employ analytics such as APM to manage operations in addition to ML. This stage represents a linking together of intelligent factories to create interactive and interconnected systems.
4. Network or Ecosystem optimization – where intelligent systems of operations – factories and service delivery facilities -- automatically decide how to adjust ecosystems of factories, planes, power systems, or hospitals. This will rely upon interactive, sophisticated, digital modeling.

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