How advanced analytics create (Core) value: an example from a pharmaceutical company, AstraZeneca

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.

Keywords:

• Principal-agent problem
• Advanced analytics
• Machine learning and artificial intelligence
• corporate Core values
• Pharmaceutical
• Objective hierarchy networks
• AstraZeneca

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. This procedure is similar to “focussed coding” in Grounded Theory (Glaser, 1978).

2. This procedure of synthesising and organising the data is similar to axial coding in Grounded Theory (Creswell, 1998).

3. “Our Future Reimagined” is a strategic initiative to optimise the design of clinical studies.

4. The objectives initially identified have been identified as “objective (initial)” in the “Internal survey results” and the “External survey results” sections.

5. Once an innovative method has been introduced to the Group it is important that the method will be routinely used by the Group.

6. For natural and constructed attributes elements are numbered 1 to 5, the score is equal to this number.

7. For proxy attributes, the score equals the number of True elements.

8. The object what the objective aims to optimise (e.g., cost).

9. The direction of travel is how the object is optimise (e.g., minimise cost).

10. Note that the attributes associated with the objectives have not been developed.

11. https://www.astrazeneca.com/our-company.html.