Capabilities for Big Data: An Empirical Study in a Global Pharmaceutical Company

The increasing availability of large quantities of digital data (Big Data) and advanced analytic tools is driving many industries to change their practices. Currently there are limited theoretically informed, in depth empirical studies of the processes and activities that needed to leverage Big Data strategically. This research is a response to calls by international scholars for more in depth and theoretically informed studies of on Big Data (e.g. Brynjolfsson et al., 2011; McAfee et al., 2012; Wamba et al., 2015; Braganza et al., 2017; Mikalef et al., 2017). The study frames Big Data as a new resource and adopts the Resource Based View (RBV) and the Dynamic Capabilities (DC) perspective in order to explore the enhancement of existing capabilities and development of new capabilities required to leverage this new resource. The empirical context of this study is a global pharmaceutical company. Employing a qualitative in-depth case-study approach, this research investigates why a Multinational Corporation (MNC) in the pharmaceutical industry adopted Big Data adoption and how it identified and developed capabilities for this new resource. Data from 24 in-depth interviews and observations of Europe, the Middle East and Africa (EMEA) and Global managers in two project teams were analysed and synthesised using thematic analysis methods. The findings from this study show that the use of Big Data required new and enhanced capabilities that were developed through the action of dynamic capabilities, which operated as mediators between existing capabilities and the new and enhanced capabilities. Although some elements of these dynamic capabilities were embedded in the organisational processes, the activities of senior managers played a crucial role in their development and use. Further, the findings show that the organisation’s cultural transformation was critical for the operation of the dynamic capabilities identified and the new and enhanced Big Data capabilities. In the case study company the development of a Big Data capabilities was found to be an incremental, extended process. The study makes a number of contributions. It provides an in-depth case study of Big Data preparation in the specific context of a MNC pharmaceutical company that is of value to both academics and practitioners. It provides a theoretically based and empirically validated model of the development of capabilities associated with Big Data adoption. Finally, it makes a contribution to academic theory by contributing to the ongoing discussion in the academic literature of the utility of the concept of Dynamic Capabilities

A Transforming Insurance Company and the 4 Types of Health Data Challenges that Arise: A Finnish Case Study

Abstract This paper aims to identify the challenges of health data in the context of an insurance company that is transforming from a reactive company into a proactive one. Using 23 interviews from a case study of an insurance company in Finland, we revealed 4 key areas of challenges that arise during this transition. The identified areas were found to be the following: Access, Ownership, Sharing, and Use. These findings are then discussed in context of the shift towards a proactive paradigm for organizations. The customer experience is suggested to be pivotal for organizations to create value and for managing the 4 identified health data challenges

Big Data and the Transformation of Operations Models: A Framework and A New Research Agenda

Big Data has been hailed as the ‘next big thing’ to drive business value, transform organisations and industries, and “reveal secrets to those with the humility, willingness and tools to listen” (Mayer-Schönberger and Cukier, 2013: 5). However, despite growing interest from organisations across industry sectors, Big Data applications appear to have concentrated on delivering incremental change and operational efficiency improvements, with little evidence on using Big Data to facilitate strategic, transformational change. In this paper, we explore how Big Data is actually being can be used across different sectors in leading organisations and examine the ways in which it is fostering change in the core operations models of organisations. A definition of ‘operations model’ is developed and the core components dimensions of an operations model are then examined, namely capacity, supply network, process and technology, and people development and organisation. Through a series of case studies, we examine the role of Big Data in affecting some, or all, of these components dimensions in order to generate value for the organisation by optimising, adapting or radically transforming the operations model. Following our analysis, we develop a tentative framework which can be used both for understanding how Big Data affects operations models, and for planning changes in operations models through Big Data. We set out a new research agenda to systematically understand the full potential of Big Data in transforming operations models

Digitalisation of Development and Supply Networks: Sequential and Platform-Driven Innovations

We draw from an eight-year dataset of 98 organisational entities involved in pre-competitive innovation networks across the UK pharmaceutical sector. These data map into three networks that are representative of: (i) a product development-led sequential pathway that begins with digitalised product development, followed by digitalisation of supply networks, (ii) a supply network-led sequential pathway that starts with digitalised supply networks, followed by digitalisation of product development, and (iii) a parallel — platform-driven — pathway that enables simultaneous digitalisation of development, production, and supply networks. We draw upon extant literature to assess these network structures along three dimensions — strategic intent, the integrative roles of nodes with high centrality, and innovation performance. We conduct within-case and cross-case analyses to postulate 10 research propositions that compare and contrast modalities for sequential and platform-based digitalisation involving collaborative innovation networks. With sequential development, our propositions are congruent with conventional pathways for mitigating innovation risks through modular moves. On the other hand, we posit that platform-based design rules, rather than modular moves, mitigate the risks for parallel development pathways, and lead to novel development and delivery mechanisms

A Context-Aware Architecture for Personalized Elderly Care in Smart Environments

Much research has focused recently on the development of smart environments and services for human-centered applications for personalized care and improved quality of life. This is especially relevant to support the elderly to lead an active and independent life. Recent efforts exploited the state of art development in the Internet of Things, Smart Sensors grid, Embedded and Wearable systems as well as Cloud Computing to build mathematical models of personal behavior and lifestyle largely driven by big data analytics. In order to overcome the range of challenges associated with the size and heterogeneity of the related data, hardware and software, as well as of the human and social factors involved, a context-aware architecture appropriate for smart environments is needed. This paper describes ACTiVAGE (ACTiVe AGeing sErvices), a conceptual framework for developing Personalized Elderly Care services that leverage big data analytics for context-awareness in smart environments