Knowledge Management and the Digital Native Enterprise

Knowledge Management and the Digital Native Enterprise

Deonie Francesca Botha (Deloitte & Touche, South Africa)
DOI: 10.4018/978-1-5225-9188-7.ch008

Abstract

This chapter will provide insights into the positioning and manifestation of knowledge management in a digital native enterprise. The findings of the literature review will be enhanced with the findings from three use cases reflecting on the infliction point between knowledge management and work performed in the digital native enterprise. The aforementioned will enable early insights into the role and contribution of knowledge management in an ecosystem where people and devices are seamlessly connected and strategic decisions are needed in respect of the positioning and manifestation of knowledge management, as well as the skillfulness required by knowledge managers within the construct of the digital native enterprise.
Chapter Preview
Top

Introduction

Technology is changing the way people perform work, hence the operating models of organisations will need to be re-evaluated and adjusted within the predictable future. Bhalla, Dyrcks and Strack (2017) states “a tidal way of change is coming that will soon make the way we work almost unrecognizable to today’s business leaders. In an age of rapidly evolving technologies, business models, demographics, and even workplace attitudes – all shifting concurrently – change is not only constant but also exponential in its pace and scope.” These changes result from the Fourth Industrial Revolution (4IR) and its emphasis on technological innovation and digital productivity in digital native enterprises (DNEs). Schwab (2016) continues by describing the extent and nature of the 4IR “In its scale, scope and complexity, what I consider to be the fourth industrial revolution is unlike anything humankind has experienced before. We have yet to grasp fully the speed and breadth of this new [fourth] revolution.

Although the impact of the 4IR on the operating models of organisations is not yet fully comprehendible it is evident that the expectations of clients and society at large will be such that organisations will need to be redesigned. Rosen et al. (2017) is of the opinion that “Digital transformation has progressed to where it is now an existential concern for many enterprises. Growing organisations strive to become “digital native” in the way they think, what they produce, and how they operate. Yet many organisations have difficulty in imagining what the new digital future could be.”

Timperley (2018) indicates that although technological and digital productivity have been a gradual process it will require workers to acquire an altered state of skilfulness and mindfulness. Workers need to find a new “state of competitiveness” in the so-called gig economy simply because the rules of employment engagement has changed. In this economy, employment opportunities will not be awarded on the experience or qualifications of workers but rather on their ability to perform work which machines are unable to do. Furthermore, workers need to create and develop the ability to work alongside and collaborate with machines that are able to learn, make decisions and perform a variety of cognitive functions. The aforementioned is of particular relevance to the manifestation and sustainability of knowledge management functions in DNEs.

DNEs will be characterised by formalised and explicit knowledge management strategies that are seamlessly integrated with organisation wide systems and embedded within workflows. These knowledge management strategies combine codification and personalisation approaches and allow for knowledge management initiatives to enable the improved productivity of workers and encourage ecosystem wide innovation (Venkitachalam & Willmot, 2017). Knowledge management should therefore form an integral part of the construct of operational functions organisations. However, in the DNE, “knowledge” will be in the form of “big data” received by sensors and transmitted to actuators and can be analysed in a cloud-based cyber-physical system (CPS). The analyses of big data sets will enable the seamless integration of the products developed and services delivered by organisations and society. All with relatively little intervention from workers and a low dependency on their ability to apply their acquired knowledge and experience to perform knowledge work or physical labour.

The primary objective of this Chapter is to develop insight into the positioning and manifestation of Knowledge management in a DNE. In an effort to achieve the aforementioned objective, Knowledge management in traditional organisations will be juxtaposed with the same in the setting of DNEs. This juxtaposition is augmented by a deliberation on the fundamental concepts that determine the nature of the relationship and the resultant expectancies between DNEs and Knowledge management. The afore is followed by an analysis of three use cases reflecting on the nature of the engagement between Knowledge management and the constructs of “work”, “work processes” and the “worker” in a DNE setting. The aim of the deliberation is to analyse the design principles of a DNE as well as the work that needs to be performed in the same in an effort to ascertain the relevancy and future manifestation of Knowledge management.

Key Terms in this Chapter

Big Data: Data sets created by the convergence of various devices, equipment, and systems and that are too complex for traditional data processing software.

Decision-Making: The process of identifying the best or preferred course of action.

Fourth Industrial Revolution: An economy characterized by digital transformation, digital disruption, and trends such as the internet of things, virtual reality, machine learning, and artificial intelligence.

Digital Native Enterprise: An organisation in which processes, transactions and interactions are largely technology-enabled.

Knowledge Management: The discipline concerned with creating an environment in which both the tacit and explicit knowledge of organizations can be managed. The aim of knowledge management is to increase productivity and support innovation.

Knowledge Management Processes: Knowledge management strategies focus on identifying, collecting, sharing, accessing, applying, and validating knowledge.

Codification: A knowledge management strategy aimed at capturing and organising knowledge in a database. The integration of the physical and technological environment. This environment is in most cases cloud based and it regulated, controlled, and monitored by computer-based algorithms.

Personalization: A knowledge management strategy aimed at sharing and leveraging highly contextualized knowledge and insights that can support decision making.

Complete Chapter List

Search this Book:
Reset