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Igor Perko(University of Maribor, Slovenia), Raul Espejo(World Organisation of Systems and Cybernetics, UK) Big data analytics organisational learning

Abstract. Purpose. We will identify the potentials that big data analytics (BDA) have on the of the learning processes of an organisation. We are particularly interested in the speed of these learning processes;on the memorising and sharing of knowledge, on the ability to recognise the environmental feedback information and on the impact to micromanaging internal organisational processes. Design/methodology/approach. To assess the current state, we offer a theoretic background of the organisational learning processes and the BDA related research reports. To analyse the BDA supported organisational learning processes, we invoke the Viable system model (VSM) and especially the Viplan methodology. Based on the results, a universal BDA supported organisation learning model is proposed. Findings – A universal organisational model, focusing on BDA supported learning processes. Originality/value – to elaborate an organisational learning model, encapsulating the BDA toolset. Research limitations – the proposed results rely on published research reports and arenot validated in a real life experiments. Research/Practical/Social/Environment implications – For the researchers the model will provide a new organisational paradigm and articulate multiple research directives. Members of the professional community will better understand the BDA potentials for organisational learning.Because of the universality of the model, it will have the potential to be applied on all organisational levels, ranging from individualsto society and environment.

Keywords: Systems thinking, Cybernetics, Big Data analytics, Learning processes, Viable system model, Viplan methodology

1. Introduction

1.1 Problem situation.Current information technology, particularly social media such as Facebook, Goggle, LinkedIn and many more, are hugely increasing data flows and interactions in society and organisations. Algorithms and artificial intelligence or BDA are dealing with the related data, suggesting that they can handle these data flows in the benefit of people‘s decisions and actions. We believe that this perception of increased observational and action capacity needs revision. BDA have a great capacity to deal with data and articulate options but these new capabilities may increase people‘s illusion that they have an improved understanding of their relevant situations and also an increased capacity to deal with them effectively. We want to test and improve these perceptions and argue, in conceptual and methodological terms, that there are dangers in an unrestricted data management driven by sophisticated BDA; what drawbacks are they posing to aspects such as organisational effectiveness, individual autonomy, privacy and fairness.

The organisation helps people to learn and understand the state and dynamics in the environment, as well as state and dynamics within the system. With accessibility to high volumes of data, the personal capacity to understand and react upon the data may be overpowered. Prior to the BDA approach, using the business intelligence, the means of reducing variety was mostly focused on financial results and summarisation was used to provide views on the higher levels in the organisation (Kimball, 2002). Based on this lack of variety, business intelligence had serious problems, especially it lacked understanding of the implications of high-level decisions, and focusedmostly on the financial aspects of the business it measured.