Big data analytics capabilities and value creation at the work-practice level: A South African case study

18 pages•Published: July 18, 2022

Abstract

While much effort has been expended on understanding the adoption and implementation of big data analytics in organisations, less effort, comparatively speaking, has been put into investigating the business value that can be derived from such investments. Recent research on the resources and capabilities required to leverage big data analytics value offers promise in this regard. The purpose of this research study is to describe big data analytics capabilities required to create business value that arises from the work practice level. This study was a single case qualitative semi-structured interview research study. The study found that the functions using big data analytics at the work- practice level included marketing, customer relationship management, and product development. Capabilities identified include strategic alignment, human expertise, technology, culture, investment and time, and governance. The work practices enacted included inductive, deductive and abductive approaches, as well as algorithmic and human-based intelligence. Product innovation, market penetration, customer satisfaction and revenue growth represented the business value accrued to the organisation.

Keyphrases: big data analytics, big data analytics capabilities, business value, work practice level

In: Aurona Gerber (editor). Proceedings of 43rd Conference of the South African Institute of Computer Scientists and Information Technologists, vol 85, pages 196-213.

Links:	https://easychair.org/publications/paper/cwFW
	https://doi.org/10.29007/c23q

BibTeX entry

@inproceedings{SAICSIT2022:Big_data_analytics_capabilities,
  author    = {Friedah Moseneke and Irwin Brown},
  title     = {Big data analytics capabilities and value creation at the work-practice level: A South African case study},
  booktitle = {Proceedings of 43rd Conference of the South African Institute of Computer Scientists and Information Technologists},
  editor    = {Aurona Gerber},
  series    = {EPiC Series in Computing},
  volume    = {85},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/cwFW},
  doi       = {10.29007/c23q},
  pages     = {196-213},
  year      = {2022}}

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