Big Idea: Competing With Data & Analytics

Better Decision Making with Objective Data is Impossible

If you’re looking to data to guide your business, you may be looking in the wrong place.

Sam Ransbotham Reading Time: 3 min 

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Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
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Data Facts analysis

“Our world is awash in data, and data is not the same thing as facts,” argues Boston College’s Sam Ransbotham. “While data seems to promise objectivity, instead it requires analysis — which is replete with subjective interpretation.”

In the popular 1950s and 1960s television show Dragnet, Sargent Joe Friday, played by Jack Webb, craved objective facts, famously asking witnesses to provide “just the facts.”

Managers today similarly crave facts. The potential positives of working from objective facts are enticing. It’s expected that improved performance follows from basing decisions on facts, whether in traditionally heuristics-based industries such as health care or in causally imprecise contexts such as business strategy.

But our world is awash in data, and data is not the same thing as facts. Facts are much harder to come by than data. While data seems to promise objectivity, instead it requires analysis — which is replete with subjective interpretation.

Assuredly, having data is a necessary step toward making objective decisions. Yet the objectivity of data is a myth. Modern analytical methods afford creative and flexible uses of data that can support multiple perspectives and competing analyses about the same data sets.

For instance, more data makes it easier to find support for virtually any position — because more data provides more options, limited only by the creativity of the analyst. Analysis could choose to focus on a subset that shows the “correct” results. Or, data that counters a desired position could be filtered out as “erroneous.” Sophisticated tools support many different modeling methods and options; one is bound to find the “right” answer. Just keep adding and dropping variables or observations until the known “truth” shines through.

Using data and analytics to support pre-existing beliefs is called “confirmation bias.” This is a particularly acute problem for modern analytics due to the potent combination of access to massive amounts of data, sophisticated methods and the seeming irreproachability of data-based decisions.

Confirmation bias can advance personal and political agendas or technical outcomes in ways that are difficult to detect. It can take the form of looking only for evidence that supports a desired outcome.

Alternatively, another way confirmation bias manifests itself is by having a preconceived idea about when to stop data analysis.

Topics

Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
More in this series

About the Author

Sam Ransbotham is an associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Data and Analytics Big Idea Initiative. He can be reached at sam.ransbotham@bc.edu and on Twitter at @ransbotham.

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Comment (1)
Richard Ordowich
From this article’s tone it appears the author does not subscribe to the roles of Data Governance, Chief Data Officers or Data Stewards preferring more assertive roles and entertaining roles.

There is however one aspect of data that many ignore; that data is valueless. Data is not objective or raw. Data is not “fact”. Data embodies the biases and values (human) of those who selected it, designed it and processed it. Data is subjectively designed.

The first step in data analytics is to determine what values the data embodies. This should be done by a Data Archeologist and perhaps a Data Sociologist. Then those using this data for other purposes can determine what subjectivity values the data embodies rather than assuming it is fact!