Managing data science initiatives as exploratory projects : a new approach to program management

Mathur, Sandeep

Managing data science initiatives as exploratory projects : a new approach to program management

Mathur, Sandeep

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Publication Type:: Thesis
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Mathur, Sandeep
dc.date.accessioned	2024-09-27T04:50:18Z
dc.date.available	2024-09-27T04:50:18Z
dc.date.issued	2024
dc.identifier.uri	http://hdl.handle.net/10453/181039
dc.description	University of Technology Sydney. Faculty of Design, Architecture and Building.	en_US.UTF-8
dc.description.abstract	Data is increasingly ubiquitous in organisational life, informing business cases, delivering core scope elements, and tracking benefits realisation. Data science initiatives (DSIs) involve applying data science techniques to address complex problems, generate insights or create new products. Despite their popularity, DSIs have a low perceived success rate, with 85% of projects failing. The interdisciplinary and complex nature of DSIs necessitates a program-based approach for effective delivery. Current literature lacks comprehensive frameworks for program managers to guide DSIs through their life cycle, from business case construction to value realisation. This research addresses this gap by analysing seven DSIs at Transport for NSW, aiming to understand challenges and best practices in DSI management. The study proposes an evidence-based delivery framework using agile methods for flexibility and a minimum viable governance framework employing lean portfolio management. DSIs blend exploration (innovation) and exploitation (efficiency), requiring balanced management to navigate emerging technologies and evolving business landscapes. This research provides theoretical contributions by integrating explorative and exploitative concepts and practical insights for program managers. It also serves as a resource for academics and practitioners to enhance understanding and management of DSIs, addressing the need for effective program management in the field of data science.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/181039/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2024 Sandeep Mathur
dc.rights	au.edu.uts.lib/cph
dc.title	Managing data science initiatives as exploratory projects : a new approach to program management	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Data is increasingly ubiquitous in organisational life, informing business cases, delivering core scope elements, and tracking benefits realisation. Data science initiatives (DSIs) involve applying data science techniques to address complex problems, generate insights or create new products. Despite their popularity, DSIs have a low perceived success rate, with 85% of projects failing. The interdisciplinary and complex nature of DSIs necessitates a program-based approach for effective delivery. Current literature lacks comprehensive frameworks for program managers to guide DSIs through their life cycle, from business case construction to value realisation. This research addresses this gap by analysing seven DSIs at Transport for NSW, aiming to understand challenges and best practices in DSI management. The study proposes an evidence-based delivery framework using agile methods for flexibility and a minimum viable governance framework employing lean portfolio management. DSIs blend exploration (innovation) and exploitation (efficiency), requiring balanced management to navigate emerging technologies and evolving business landscapes. This research provides theoretical contributions by integrating explorative and exploitative concepts and practical insights for program managers. It also serves as a resource for academics and practitioners to enhance understanding and management of DSIs, addressing the need for effective program management in the field of data science.

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http://hdl.handle.net/10453/181039