Is there a role for knowledge management in saving the planet from too much data?

Abstract

From a societal perspective, the huge growth in data being generated by organisations is clearly correlated to technological advancements enabling far greater capacity for data acquisition and storage (e.g., data centres) than has ever been previously available. Data centres alone account for 3% of the global electricity supply and consume more power than the entire United Kingdom (UK), contributing 2% of the total global greenhouse gas emissions (Bawden, 2016). The “store it all” approach adopted by many organisations as evidenced in the migration to the cloud, for instance, is a significant threat to the pursuit of netzero, given that the energy sector already accounts for 35% of the total global emissions (UN, 2022). The exponential growth in digital data generation, which according to Statista (2022) will be as high as 79.4 zettabytes worldwide by 2025, thus poses a huge potential threat to global net-zero efforts. To illustrate, early estimates have suggested that 4% of global greenhouse gas emissions can be attributed to digitalisation (Teuful & Sprus, 2020). The digital data carbon footprint should, therefore, be of critical concern to organisations and public administrations alike. With the increasing need for organisations to report the greenhouse gas emission associated with their direct, indirect, and supplychain activities as well as policy targets to reduce greenhouse gas emissions across developed economies, it is surprising to note that the digital data carbon footprint is not considered. As Jackson and Hodgkinson (2022) highlight, while decarbonisation is clearly a policy priority for developed governments, there remains no mention of the role of digital data in recent policy documents. It is important to be clear, as others have (e.g., Teuful & Sprus, 2020), that digital data and indeed digitalisation is not inherently “bad” for the environment, but rather, it is what we as individuals, organisations, and society make of it that dictates the impact on the environment. This is central to the digital decarbonisation movement, which concerns how knowledge and data are used, and reused, by organisations and the promotion of digital best-practices in sustainability strategies to reduce data CO2 (Jackson & Hodgkinson, 2022). Research on responsible management practices remains largely detached from the abundant work on organisational learning and the knowledge management (KM) field more broadly (Dzhengiz & Niesten, 2020). This is despite there being a clear relationship with how organisations draw on new and existing knowledge, and the health of the environment. Technological progress has changed how knowledge is managed in organisations and particularly in the way in which new knowledge is acquired, assimilated, transformed and exploited through organisations’ absorptive capacity, an established learning capability of the organisation (e.g., Dzhengiz & Niesten, 2020; Fosfuri & Tribó, 2008; Yuan et al., 2022). Several recent studies illustrate how emerging technologies have shaped knowledge processes in organisations (e.g., Stachová et al., 2020) and the relationship between modern technology and knowledge management processes in organisations (e.g., Almeida et al., 2019; Archer-Brown & Kietzmann, 2018; Benitez et al., 2018; O’connor & Kelly, 2017; Sher & Lee, 2004; Skok & Kalmanovitch, 2005; Wild & Griggs, 2008). Yet, the consequences of using modern technological solutions within the absorptive capacity process for individuals, organisations and society is not clear. At the individual and organisational levels, technological advancements have changed the cognitive patterns and knowledge-related behaviours of employees (Ward, 2013). For instance, employees spend less time on direct interactions with colleagues and more time on individual computer work (Kleszewski & Otto, 2020), resulting in reduced direct information exchange. Such behavioural changes are deemed to impede socialisation and group processes, which are known to be integral features of traditional knowledge processes (Nonaka & Takeuchi, 1995). Moreover, as knowledge workers have become more technologyreliant, they have become more efficient in using justin-time knowledge (Jackson & Hodgkinson, 2022). Consequently, they are more prone to surface learning (Gursoy et al., 2008) instead of gaining a deeper understanding of a subject or topic (Dennett & KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE 2023, VOL. 21, NO. 3, 427–435 https://doi.org/10.1080/14778238.2023.2192580