Data-Driven Organization

The previous section showed that data science is not necessarily just hype, but a strategic and systematic approach to using data. Using data in organizations is also called business analytics or evidence-based management. There are also specific approaches, such as Six-Sigma, that use statistical analysis to improve business processes. Many advocates of data science claim that the old and new approaches are different. Most definitions of data science focus on pattern recognition using large sets of data through machine learning. (Kelleher & Tierney (2018)). How does data science relate to its predecessor buzzwords? To understand this difference, we need to explore the early history of using data in business.

The idea that management can be science is just over a century old. Frederick Taylor was an American engineer who was dissatisfied with how factories were managed. He was a hands-on engineer who spent much time on the factory floor. Taylor noticed how workers used rules of thumb, instead of analyzing problems systematically. He writes, in The Principles of Scientific Management (1911), how he improved the process of manually loading massive lumps of iron at the Midvale Steel Company by measuring processes and analyzing the data. (Taylor, F.W. (1997). The Principles of Scientific Management. Mineola, N.Y: Dover Publications)

Although Taylor revolutionized the way we manage organizations, he despised laborers. Taylor believed that it "would be possible to train an intelligent gorilla to become more efficient" than a factory worker. His quest for scientific management was driven by an urge to remove power from the workforce and look at business processes in an abstract mathematical sense. His work was controversial in his own time, as it was the subject of a formal government inquiry. This background about Taylor is not just a bit of trivia, but a valuable lesson about ensuring to include a human dimension in what we analyze. The positive legacy of Taylor is that he planted the seed for a scientific approach to managing an organization. All methods share his ideal of using data to prevent biases in management.

Managers are faced with deciding what to do next in uncertain environments and often use their experience and intuition to determine the next course of action, instead of data and logic. While experience and intuition are highly valuable, our minds are prone to biases and non-rational thinking. Our rationality is not unlimited but is bounded by factors outside of our control. The amount of information, the time available to solve a problem, and our mental capacity are all limited. Our brains are wired to quickly recognize patterns in nature because it helps us in our daily lives. Mental shortcuts, the rules of thumb despised by Taylor, help us to make fast decisions in emergencies, but they can also lead to sub-optimal outcomes.

The world of business is not something we have necessarily evolved to navigate, and we are thus not very good at interpreting large amounts of abstract data. Because our minds are programmed to recognize patterns, we often see regularities where there are none, which psychologists call pareidolia. This condition causes us to recognize animals in clouds or see the image of Jesus in a piece of toast, or a face on the surface of Mars. Pareidolia serves us well because it enables graphical communication, but it becomes a hindrance when analyzing large sets of data. Interestingly, neural networks can also be trained to experience pareidolia. When manipulating the settings of image-recognition software, computers can be taught to recognize images in random data and effectively hallucinate a new reality. (Mordvintsev, A., Olah, C. & Tyka, M. (2015). Inceptionism—http://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html: Going Deeper into Neural Networks. Retrieved 15 February 2019)

Besides inherent biases through the limits of our rationality, social circumstances can also prevent us from optimizing decisions. Groupthink and office politics are often strong drivers of decisions in organizations. Social belonging is a strong motivator for our behavior and is one of the major driving forces behind advertising. Asch's conformity experiment illustrates how strong these social biases can be. Solomon Asch demonstrated that even when people are fully aware of the rational answer to a simple question, they will in most instances yield their opinion to match that of the group, even when it is clearly the wrong choice. (Asch Conformity Experiment (YouTube—https://www.youtube.com/watch?v=TYIh4MkcfJA). Downloaded 14 February 2019)

One of the greatest revolutions in human thinking is the 15th-century Copernican twist. From our limited perspective, the earth seems flat and the sun and moon revolve around us. When Copernicus looked through a telescope to amplify his naked-eye observations, a new reality emerged, and with it, a better model of our solar system. What we learned from Copernicus is that we need to enhance our perception and thinking skills with technology to draw correct conclusions. Data science is to business what the telescope is to astronomy. Sound analysis of data helps us to remove our natural biases and replace our rules of thumb with logic.

Just like a long-enough lever can make us physically strong enough to lift the world, the tools of data science make us mentally stronger in understanding and controlling the world. While the uncertainties of the realities of business can never be eliminated, evidence-based management ensures that managers make decisions based on the best available data. Data science is the toolkit that assists managers to base their decisions on evidence. Using the principles of data science will improve the way managers decide between alternative courses of action.

Using a scientific approach to data is, however, not a simple road to success. Data science is a human activity that encompasses all the biases and limitations. The results of data science are also not ethically neutral and require a moral perspective to ensure that no harm is done. The key to minimizing these biases is to use a systematic approach.