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

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.

Figure 1. The technical perspective on the Big Data three V’s

A vast majority of the literature that deals with Big Data related issues is focused on the technical aspects of data collection (Addo-Tenkorang & Helo, 2016; Hashem et al., 2015), whilst its value added and its implications on the organisation‘s performance is analysed rather sparsely(Addo-Tenkorang & Helo, 2016).(Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016)similarly state that in most organisations investment is focused on developing BDA capabilitiesrather than on their positive effects enhancingthe their performance(Wamba et al., 2017),(Gupta & George, 2016). In our view this is a point also related to organisational learning.

BDA is employed to increase the understanding of the areas that so far have been dominated by guts and intuition (McAfee & Brynjolfsson, 2012)about how to introduce precise management, effective interventions, and the new level of organisational agility. It is particularly focused onbusiness environment(Perko & Ototsky, 2016), especially that closely related organisations, as for instance customer behaviour or partners in the supply chain (SC), but also uses IOT (?)to measure detailed internal processes.From our perspective, these are efforts to integrate multiple views,(Sivarajah, Kamal, Irani, & Weerakkody, 2017) to present a holistic view of the Big Data challenges in organisations and BDA methods by analysing the published research reports on that subject.

We want to make, with the use of BDA, more transparent people‘s interactions and communications in organisations. What are their demands for learning in these situations? The risk is that an insensitive use of BDAmay overwhelm people and organisations. Management of big data needs awareness of our cognitive, ethical and organisational capabilities. For instance, with reference to the justice system, it is dangerous to have judges sentencing individuals with the support of big data, based on appreciating their current and possibly their future behaviour, without the monitoring of these individuals‘ contextualised situations, that is, without efforts to improve organisational processes. Also, in commercial activities, it may be ineffective for companies to use marketing algorithms that ignore individual and structural competencies. Overwhelming people with marketing data, abusing their privacy, is a recipe to alienate them. To use BDA to understand and predict individuals‘ insights in their organisations is necessary. Many, among them (Bello-Orgaz, Jung, & Camacho, 2016)use social media data analysis methodology for that purpose. (Perko, 2017) analyses the behaviour of finance officers to predict their future actions. (Bellomo, Clarke, Gibelli, Townsend, & Vreugdenhil, 2016) explore behaviour in evacuation crowd dynamics to prevent or mange critical situations.