Рефлексивные процессы и управление. Сборник материалов XI Международного симпозиума 16-17 октября 2017 г., Москва - стр. 13

Big data is produced by the huge number of transactions natural to all situations. The problem is their management. Crucially to focus on relational aspects we use Ashby's Law of Requisite Variety (Ashby, 1964)and the ideas of variety operators to balance performance at satisfactory levels. Dealing with data requires considering how they are absorbed by the structures affected by them, as well as their responses. It is in absorption that the structural, ethical and technical issues of big data and people come together.

It is not always the case that an enterprise shows the property of closure necessary for a desirable autonomous behaviour in its environmental context. To improve its structure we focus on processes of individual and organisational learning. Individual learning is increasing their capacity to take effective action and organisational learning is increasing effective action in their environments. Among several factors restricting this learning are poor models of these environmental situations. "Every good regulator of a system must be a model of that system" (Conant & Ashby, 1970). But it is not useful to be a good regulator of a poorly structured situation, hence the duality of structure and data models that we explore in this paper. Overcoming structural fragmentation helps making data more meaningful to those affected by the contextual changes.

Multiple models explain organisational learning processes. Koskinen (Koskinen, 2012)for instance explores the potential of process thinking to open up new ways to understand organizational learning, particularly through problem absorption within problem solving. In organizations existing rules and norms are usually used as the basis for solving new problems even when this means stretching those rules. Such absorption of new problems by rules reduces the need to explore and develop new solutions and to encode those solutions into new rules.(Argote & Miron-Spektor, 2011) propose a theoretic framework for analysing organizational learning. According to the framework, organizational experience interacts with the latent component and an active component of contextsthrough which learning occurs. However, most of these models regard learning separately from the other processes in the organisation. We offer amore holistic perspective as provided by the VSM and Viplan methodologyl(Beer, 1981; Espejo, Bowling, & Hoverstadt, 1999)

2.1 Big DataAnalytics (BDA).Even though the technical aspect of big data generation calls for potent data tools, capable of receiving, storing, understanding and reacting to the vast quantities of big data, especially the variety part hides a dark secret. Digital recording of real world transactions only create data models,partially capable of reflecting their complexity. Though we may agree that capturing unstructured data has the potential of improving our perception of an event, we can only speculate about the effect of storing unstructured, loosely connected event data,to our understanding of its complex dynamics.Organisational and individual learning are important to overcome this uncertainty.