Abstract
The goal of this opening chapter is to lay a conceptual foundation for what is intended to offer a broad overview of the core aspects of organizational learning, conceptualized as mechanisms by means of which modern commercial, non-profit and other organizations can use available data to fuel not only their decision-making processes, but also broader operational functioning. What is framed here as organizational learning imperative is seen as an inescapable consequence of the interplay among macrotrends that are profoundly changing how work is done and how lives are lived; those macrotrends include the shifting foci of economic value creation from tangible assets towards intangible know-how, digitization and digitalization of commercial and non-commercial aspects of life, the emergence of big data and data-enabled automation, the rise of enduring hypercompetition, and socio-politico-economic volatility becoming the new face of ‘normal’. Set in the comparative context of the ‘old’ ideas of thinking organizations and organizational thinking and the reasons those initially promising perspectives ultimately failed to deliver lasting organizational value, a broad Learning with Data framework is introduced, encompassing MultiModal Organizational Learning typology, an overview of distinct levels of data analytic know-how and a discussion of the elements, as well as the underlying process of analytic learning.
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Notes
- 1.
In chaos theory, a branch of mathematics focusing on the study of seemingly random systems, the butterfly effect captures the idea of systems’ sensitive dependence on initial conditions, where a small change can result in disproportionately large effects; the name itself is derived from a metaphorical example of the flapping of the wings of a butterfly in one part of the globe and can, several weeks later, affect the formation of a tornado in another, distant part of the world.
- 2.
It is worth noting that even exploratory, i.e. open-ended search for patterns and relationships, data analyses are ultimately geared towards forming hypotheses, which are then to be empirically tested.
- 3.
This term is derived from (published) DARPA (Defense Advanced Research Projects Agency) research, more specifically, from an article by A. Prabhakar, the former director of DARPA, published in January 27, 2017, issue of Wired magazine.
- 4.
The full expression, as commonly quoted, which came from Newton’s 1675 letter to fellow scientist (and perhaps the great scientist’s biggest antagonist) Robert Hooke, reads: ‘If I have seen further, it is by standing on the shoulders of giants’.
- 5.
Epistemology is a branch of philosophy concerned with understanding the nature of knowledge and belief.
- 6.
It is worth noting that many of his contemporaries, especially those in Athens’ ruling elite, were significantly less enamoured with his approach, as evidenced by the fact that Socrates was brought to trial (before a jury of 500 of his fellow Athenians) on charges of failing to acknowledge gods recognized by Athens and corrupting the youth; he was not able to sway his jurors and was subsequently sentenced to death.
- 7.
Credit for this idea should be given to Derek Sivers and his 2010 TED (technology, entertainment, design) talk.
- 8.
The Society of Actuaries, which is a global professional organization for actuaries, uses a series of rigorous exams to bestow two Associate-level designations (Associate of Society of Actuaries and Chartered Enterprise Risk Analyst), and its highest designation, Fellow of the Society of Actuaries.
- 9.
I delve considerably deeper into the details and ramifications of the academic–practice gap in Evidence-Based Decision-Making (New York: Routledge, 2019).
- 10.
I discuss that topic at length, as a part of the Empirical & Experiential Evidence framework, in Evidence-Based Decision-Making, New York: Routledge, 2019.
- 11.
It is worth noting that there are numerous well-documented cognitive biases, such as base rate fallacy, neglect of probability bias, overconfidence effect or confirmation bias (all discussed in more detail in the Evidence-Based Decision-Making book mentioned earlier), that shed light on psychology of distrust of advanced data analytics.
- 12.
In practice, the line demarking statistical and machine learning methods is somewhat blurry because some techniques, such as linear or logistic regression, are often included under both ‘statistics’ and ‘machine learning’ umbrellas; as used in this book, statistical methods are those based on defined mathematical distributions (such as the well-known standard normal distribution that forms the basis for linear regression), and machine learning methods are techniques based on principles of mathematical optimization.
References
Banasiewicz, A. D. (2019). Evidence-based decision-making: How to leverage available data and avoid cognitive biases (1st ed.). New York: Routledge.
Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. New York: Doubleday/Currency.
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Banasiewicz, A. (2021). Out with the Old – In with the New. In: Organizational Learning in the Age of Data. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-74866-1_1
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