How advanced analytics create (Core) value: an example from a pharmaceutical company, AstraZeneca. Issue 2 (2nd July 2020)

• Record Type: Journal Article
• Authors: Willigers, Bart
• Is Part Of: Journal of Business Analytics. Volume 3:Issue 2(2020)
• Page Start: 122
• Page End: 137
• Publication Date: 2020-07-02
• Subjects: Principal-agent problem -- Advanced analytics -- Machine learning and artificial intelligence -- corporate Core values -- Pharmaceutical -- Objective hierarchy networks -- AstraZeneca; Business intelligence -- Periodicals; Management -- Statistical methods -- Periodicals; Decision making -- Statistical methods -- Periodicals; 658.403
• DOI: 10.1080/2573234X.2020.1829508
• Languages: English
• ISSNs: 2573-234X

Abstract

ABSTRACT: Large investments in analytics demonstrate that the pharmaceutical industry has embraced the value proposition of data science. This excitement however does not imply that companies, currently, have a solid understanding how data science creates value. Management rely on data scientists for the value delivery of advanced analytics. Objectives of data scientists and management are not necessarily aligned. Choices made by data scientists might be suboptimal from a wholistic corporate perspective. Conversely management might lack technical expertise. This situation is an example of a principal-agent problem. AstraZeneca is making significant investments in analytical capabilities. AstraZeneca beliefs that investment decisions should not be strictly determined by monetary objectives, instead corporate Core Values should be used as guiding principles. The relationship between objectives and attributes are captured in an objective hierarchy network. This model reduces the information asymmetry between data scientists and its leaders by creating clarity regarding the objectives pursued by AstraZeneca.