Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Ghost in the Machine: Theorizing data knowledge in the Age of Intelligent Technologies

Koukouvinou, Panagiota ; Ademaj, Gemza LU ; Sarker, Saonee LU and Holmström, Jonny (2023) Forty-Fourth International Conference on Information Systems p.1-9
Abstract
AI technologies have led to new ways of thinking about data, knowledge, and organizations. Despite the arguments that data speak for themselves, the era of datafication demands revisiting data and knowledge and reflecting on new ways of theorizing. Considering that working with data is important for most employees, there is a need to investigate how the knowing of data can be achieved. In this paper, we move beyond the factual view of data and the hierarchical view of data and knowledge, to introduce data knowledge as a new type of knowledge. We present a first step towards a theory of explanation of what is data knowledge in today ́s organizations. To investigate this, we apply an etymological lens, and review systematically the IS... (More)
AI technologies have led to new ways of thinking about data, knowledge, and organizations. Despite the arguments that data speak for themselves, the era of datafication demands revisiting data and knowledge and reflecting on new ways of theorizing. Considering that working with data is important for most employees, there is a need to investigate how the knowing of data can be achieved. In this paper, we move beyond the factual view of data and the hierarchical view of data and knowledge, to introduce data knowledge as a new type of knowledge. We present a first step towards a theory of explanation of what is data knowledge in today ́s organizations. To investigate this, we apply an etymological lens, and review systematically the IS literature. Our preliminary findings demonstrate unveiling data, balancing between intuition and data, acknowledging external and internal capabilities, and realizing data, as the four main concepts of data knowledge. (Less)
Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Data, Knowledge, Theorizing, Etymology, systematic review
host publication
ICIS 2023 Proceedings. 22 : AI in Business and Society - AI in Business and Society
pages
1 - 9
publisher
AIS Electronic Library (AISeL)
conference name
Forty-Fourth International Conference on Information Systems
conference location
Hyderabad, India
conference dates
2023-12-10 - 2024-03-13
ISBN
978-1-958200-07-0
language
English
LU publication?
yes
id
feb43802-416a-4300-a4a0-e246995c4054
alternative location
https://aisel.aisnet.org/icis2023/aiinbus/aiinbus/22
date added to LUP
2024-03-26 10:46:58
date last changed
2024-03-26 11:24:10
@inproceedings{feb43802-416a-4300-a4a0-e246995c4054,
  abstract     = {{AI technologies have led to new ways of thinking about data, knowledge, and organizations. Despite the arguments that data speak for themselves, the era of datafication demands revisiting data and knowledge and reflecting on new ways of theorizing. Considering that working with data is important for most employees, there is a need to investigate how the knowing of data can be achieved. In this paper, we move beyond the factual view of data and the hierarchical view of data and knowledge, to introduce data knowledge as a new type of knowledge. We present a first step towards a theory of explanation of what is data knowledge in today ́s organizations. To investigate this, we apply an etymological lens, and review systematically the IS literature. Our preliminary findings demonstrate unveiling data, balancing between intuition and data, acknowledging external and internal capabilities, and realizing data, as the four main concepts of data knowledge.}},
  author       = {{Koukouvinou, Panagiota and Ademaj, Gemza and Sarker, Saonee and Holmström, Jonny}},
  booktitle    = {{ICIS 2023 Proceedings. 22 : AI in Business and Society}},
  isbn         = {{978-1-958200-07-0}},
  keywords     = {{Data; Knowledge; Theorizing; Etymology; systematic review}},
  language     = {{eng}},
  pages        = {{1--9}},
  publisher    = {{AIS Electronic Library (AISeL)}},
  title        = {{Ghost in the Machine: Theorizing data knowledge in the Age of Intelligent Technologies}},
  url          = {{https://aisel.aisnet.org/icis2023/aiinbus/aiinbus/22}},
  year         = {{2023}},
}