Keywords: Sensemaking, Big Data, Thick Data, Pattern Recognition
Sensemaking is the ability or attempt to make sense of an ambiguous situation. More exactly, sensemaking is the process of creating situational awareness and understanding in situations of high complexity or uncertainty in order to make decisions. The five problems with big data today are: Where to put it, Big bias, False positives, Big Complexity, and That’s not what I was looking for. Sensemaking helps with these problsms. The story of how Big Data Science was enabled through the marriage of technology in the form of the young discipline of computer science and the mature discipline of statistics was told by Gil Press in his (2013) Forbes piece titled ‘A Very Short History Of Data Science.’ The name “Data Science” is now the discipline charged with utilizing Big Data. But making sense of data has a much longer history and has been debated by scientists, statisticians, librarians, computer scientists and others for years. More recently, the ideas surrounding the importance of ‘context’ have been integrated into the use of big data in strategic decision making. Karl Weick (1993) introduced the concept of Sensemaking in organizational decision making to account for failures in data-driven decision making. This approach has been brought forward by Christian Madsjerg in his new book Sensemaking: The Power of Humanities in the age of the Algorithm (2017). However, McNamara (2005) has questioned whether or not many people really understand what Sensemaking is in practice, and Jones (2015) has argued that it is merely a collection of methodologies that are equivalent to thinking paradigms for doing research. This panel will explore Sensemaking and its relationship to Big Data Science today and offer examples of where it succeeds and fails.