What do we really need to know about Data?
The major arguments in this post were first introduced in an article published early this year.  As this week’s exam for our second-year graduate students focuses in part on the nature and value of data, I’ve updated the contribution to highlight several key points.
Data is everywhere — we have produced more data in the last two years than in the history of mankind . Digital strategies are no longer designed around websites, but upon digital platforms designed to capture consumer actions, preferences, and motivations. Data Science is the lifeblood of the Fourth Industrial Revolution  — data and our capacity to make sense of the data provide the new foundations of sustainable competitive advantage.
This said, data remains nothing more than an imperfect representation of the business realities around us. Data in has no intrinsic value, its perceived value is inherently tied to its use in addressing business challenges. Data isn’t readily comparable: quantitative, qualitative, ordinal and ratio values have very little in common. People don’t view the data the same way, for our cognitive filters weigh the numbers and qualities based on our prior experience. Little wonder that despite continuous advances in information technology, the gap between the facts and the figures hasn’t disappeared.
Not all data can be found in a database — specialists estimate that 80 to 85 percent of customer data come from “non-structured conversations” in audio, textual or video form . Most data isn’t (yet) accessible on our laptops, for data is all around us in the sights, sounds, emotions and ambitions of our managers, customers and teams. Data in itself isn’t information, but reducing the entropy of data is closely related to information gain. What do we mean by making a better use of data?
Business analytics involves leveraging data to reduce the risk, uncertainty, and ambiguity inherent in human decision-making. Data can be organized to clarify the nature of the problems we face (simple, complex, deterministic, stochastic…) and to identify the corresponding good, better, and/or memorable decisions. Not all organizations use data in the same way, certain organizational cultures use the data to confirm (or infirm) their strategies, others build their strategies from a constant examination of the data at hand.
Big Data is neither a data type nor a technology. Big Data analytics refers to a subset of Data Science that deals with five specific issues in analytics today: the tremendous volumes of data that are potentially available for analysis; the variety of types of data that co-exist today, the velocity of data in real-time analytics, the veracity of the data our disposal, and the value of the data we capture.  The value of data has little to do with how much data we “own”, but the extent to which we use data to improve managerial decision-making.
The practice of business analytics is heart and soul of the Business Analytics Institute. In our Summer School in Bayonne, as well as in our Master Classes in Europe, our focus on digital economics, data-driven decision making, machine learning, and visual communications we can help you put analytics to work for you and your organization.
Lee Schlenker is a Professor at ESC Pau, and a Principal in the Business Analytics Institute http://baieurope.com. His LinkedIn profile can be viewed at www.linkedin.com/in/leeschlenker. You can follow us on Twitter at https://twitter.com/DSign4Analytics
 Schlenker, L. (2017) Data isn’t just data. Medium, April 27th.
 Marr. B. (2015). Big Data : 20 Mind-boggling Facts that Everyone Should Read, Forbes
 Schwab, K. (2017). The Fourth Industrial Revolution. 1st ed. Random House Inc.
 Grimes, S. (2008). Unstructured data and the 80 percent rule, August 1, 2008
 Xsi, The V’s of Big Data: Velocity, Volume, Value, Variety, and Veracity, February 15, 2017