Original Title: Using cognitive linguistics to help demystifying narratives around "data" : the case of the data supply chain

Using cognitive linguistics to help demystifying narratives around "data" : the case of the data supply chain

Type:
  • Bachelor Thesis Business Information Systems
Status:
offered
Tutor:

Abstract

Motivation:

The IT domain is characterized by a considerable amount of new developments and accompanying novel terminology. While such developments can unquestionably be useful, the novel developments are unfortunately also accompanied by hype and opportunism, expressed among others in mischaracterizations of the accompanying terminology, mostly with the aim of furthering particular stakeholder interests. One such area, where one observes potential mischaracteristization, is "data". Data, especially "Big Data" when combined with "analytics", is touted to be able to transform entire industries (see, e.g., Henke et al (2016)). For example IBM argues that, when combined with machine learning, companies have an opportunity to use Big Data as a point of departure for predicting the future (Hurwitz, J., & Kirsch, D. (2018)).

Objectives:

The main objective of this Bachelor-arbeit is to critically assess interpretations of terminology surrounding "(big) data". To this end, first you provide terminological foundations of data, big data, and related terms such as information, and knowledge. Second, as done in de Kinderen et al (2020) using instruments from cognitive linguistics you analyze a particular interpretation of a term that centers on data. We focus here on the case of a so-called "data supply chain" as defined in Accenture (2014) and Inetsoft (2014),  whereby supply-chain like concepts are used to argue how data moves through an organization, and how it can provide value. Third, and finally, you then confront the interpretation of "data supply chain" from Accenture (2014), to the foundations initially provided.

Einstiegsliteratur:

Ghasemaghaei, M., & Calic, G. (2019). "Can big data improve decision quality? the role of data quality and data diagnosticity". Decision Support Systems, 120 , 38 - 49.

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017).
"Critical analysis of Big Data challenges and analytical methods."
Journal of Business Research, 70, 263-286.

Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L.
(2016). Transformational issues of big data and analytics in networked business. MIS quarterly, 40(4).

de Kinderen, S., Kaczmarek-Heß, M., & Nolte, M. (2020), "Conceptual blending for analyzing possible interpretations of information systems concepts - the case of smart contracts", In 28th European Conference on Information Systems (ECIS), To appear.

Green, V. E., & Evans, V. (2007). Cognitive linguistics: An introduction. Edinburgh University Press.

Accenture (2014) "Data Acceleration: Architecture for the Modern Data
Supply Chain",
https://www.accenture.com/_acnmedia/accenture/conversion-assets/dotcom
/documents/global/pdf/technology_5/accenture-data-acceleration-archite
cture-modern-data-supply-chain.pdf,
last accessed 01-04-2020

Inetsoft (2016) "InetSoft Webinar: The Data Supply Chain - Its
Definition and How to Use It",
https://www.inetsoft.com/business/solutions/definition_of_data_supply_
chain,
last accessed 01-04-2020

Hurwitz, J., & Kirsch, D. (2018), "Machine learning machine learning
for dummies", IBM limited edition, Wiley,
https://www.ibm.com/downloads/cas/GB8ZMQZ3, last accessed 01-04-2020

Henke, N.,  Libarikian, A., & Wiseman, B. (2016), Straight talk about
big data, McKinsey Quarterly,
https://www.mckinsey.com/business-functions/mckinsey-digital/our-insig
hts/straight-talk-about-big-data,
last accessed 01-04-2020

Flyverbom, M., & Madsen, A. K. (2015). Sorting data out: Unpacking big data value chains and algorithmic knowledge production. Die Gesellschaft der Daten: über die digitale Transformation der sozialen Ordnung, 123-144.


Schwarz, M. (2008). Einführung in die Kognitive Linguistik, Tübingen. Skirl, Helge/Schwarz-Friesel, Monika (2007): Metapher, Heidelberg.


Gitelman, L. (2013). Raw data is an oxymoron. MIT press.


Lakoff, G. (1995). Body, Brain, And Communication.


Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.


Bateson (2001) Ökologie des Geistes (nur rund um S. 19) Rosenberg
(2013) Data before the Fact

Original Abstract:

Motivation:

The IT domain is characterized by a considerable amount of new developments and accompanying novel terminology. While such developments can unquestionably be useful, the novel developments are unfortunately also accompanied by hype and opportunism, expressed among others in mischaracterizations of the accompanying terminology, mostly with the aim of furthering particular stakeholder interests. One such area, where one observes potential mischaracteristization, is "data". Data, especially "Big Data" when combined with "analytics", is touted to be able to transform entire industries (see, e.g., Henke et al (2016)). For example IBM argues that, when combined with machine learning, companies have an opportunity to use Big Data as a point of departure for predicting the future (Hurwitz, J., & Kirsch, D. (2018)).

Objectives:

The main objective of this Bachelor-arbeit is to critically assess interpretations of terminology surrounding "(big) data". To this end, first you provide terminological foundations of data, big data, and related terms such as information, and knowledge. Second, as done in de Kinderen et al (2020) using instruments from cognitive linguistics you analyze a particular interpretation of a term that centers on data. We focus here on the case of a so-called "data supply chain" as defined in Accenture (2014) and Inetsoft (2014),  whereby supply-chain like concepts are used to argue how data moves through an organization, and how it can provide value. Third, and finally, you then confront the interpretation of "data supply chain" from Accenture (2014), to the foundations initially provided.

Einstiegsliteratur:

Ghasemaghaei, M., & Calic, G. (2019). "Can big data improve decision quality? the role of data quality and data diagnosticity". Decision Support Systems, 120 , 38 - 49.

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017).
"Critical analysis of Big Data challenges and analytical methods."
Journal of Business Research, 70, 263-286.

Baesens, B., Bapna, R., Marsden, J. R., Vanthienen, J., & Zhao, J. L.
(2016). Transformational issues of big data and analytics in networked business. MIS quarterly, 40(4).

de Kinderen, S., Kaczmarek-Heß, M., & Nolte, M. (2020), "Conceptual blending for analyzing possible interpretations of information systems concepts - the case of smart contracts", In 28th European Conference on Information Systems (ECIS), To appear.

Green, V. E., & Evans, V. (2007). Cognitive linguistics: An introduction. Edinburgh University Press.

Accenture (2014) "Data Acceleration: Architecture for the Modern Data
Supply Chain",
https://www.accenture.com/_acnmedia/accenture/conversion-assets/dotcom
/documents/global/pdf/technology_5/accenture-data-acceleration-archite
cture-modern-data-supply-chain.pdf,
last accessed 01-04-2020

Inetsoft (2016) "InetSoft Webinar: The Data Supply Chain - Its
Definition and How to Use It",
https://www.inetsoft.com/business/solutions/definition_of_data_supply_
chain,
last accessed 01-04-2020

Hurwitz, J., & Kirsch, D. (2018), "Machine learning machine learning
for dummies", IBM limited edition, Wiley,
https://www.ibm.com/downloads/cas/GB8ZMQZ3, last accessed 01-04-2020

Henke, N.,  Libarikian, A., & Wiseman, B. (2016), Straight talk about
big data, McKinsey Quarterly,
https://www.mckinsey.com/business-functions/mckinsey-digital/our-insig
hts/straight-talk-about-big-data,
last accessed 01-04-2020

Flyverbom, M., & Madsen, A. K. (2015). Sorting data out: Unpacking big data value chains and algorithmic knowledge production. Die Gesellschaft der Daten: über die digitale Transformation der sozialen Ordnung, 123-144.


Schwarz, M. (2008). Einführung in die Kognitive Linguistik, Tübingen. Skirl, Helge/Schwarz-Friesel, Monika (2007): Metapher, Heidelberg.


Gitelman, L. (2013). Raw data is an oxymoron. MIT press.


Lakoff, G. (1995). Body, Brain, And Communication.


Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage.


Bateson (2001) Ökologie des Geistes (nur rund um S. 19) Rosenberg
(2013) Data before the Fact