Companies Adopt Data-Driven Decision Making Unevenly

Companies have been adopting data-driven decision making processes at different rates, depending on factors as diverse as company size and employee education level.
According to the Harvard Business Review:

At their most fundamental level, all organizations can be thought of as “information processors” that rely on the technologies of hierarchy, specialization, and human perception to collect, disseminate, and act on insights. Therefore, it’s only natural that technologies delivering faster, cheaper, more accurate information create opportunities to re-invent the managerial machinery.
At the same time, large corporations are not always nimble creatures. How quickly are managers actually making the investments and process changes required to embrace decision-making practices rooted in objective data? And should all firms jump on this latest managerial bandwagon?
We recently worked with a team at the U.S. Census Bureau and our colleagues Nick Bloom of Stanford and John van Reenen of the London School of Economics to design and field a large-scale survey to pursue these questions in the U.S. manufacturing sector. The survey targeted a representative group of roughly 50,000 American manufacturing establishments.
Our initial line of inquiry delves into the spread of data-driven decision making, or “DDD” for short. We find that the use of DDD in U.S. manufacturing nearly tripled between 2005 and 2010, from 11% to 30% of plants. However, adoption has been uneven. DDD is primarily concentrated in plants with four key advantages: 1) high levels of information technology, 2) educated workers, 3) greater size, and 4) better awareness.

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