Management challenges in creating value from business analytics

By Richard T. Vidgen, Sarah Shaw, David Bruce Grant. European Journal of Operational Research, 2017

474 citations recorded by the source corpus.

The popularity of big data and business analytics has increased tremendously in the last decade and a key challenge for organizations is in understanding how to leverage them to create business value. However, while the literature acknowledges the importance of these topics little work has addressed them from the organization's point of view. This paper investigates the challenges faced by organizational managers seeking to become more data and information-driven in order to create value. Empirical research comprised a mixed methods approach using (1) a Delphi study with practitioners through various forums and (2) interviews with business analytics managers in three case organizations. The case studies reinforced the Delphi findings and highlighted several challenge focal areas: organizations need a clear data and analytics strategy, the right people to effect a data-driven cultural change, and to consider data and information ethics when using data for competitive advantage. Further, becoming data-driven is not merely a technical issue and demands that organizations firstly organize their business analytics departments to comprise business analysts, data scientists, and IT personnel, and secondly align that business analytics capability with their business strategy in order to tackle the analytics challenge in a systemic and joined-up way. As a result, this paper presents a business analytics ecosystem for organizations that contributes to the body of scholarly knowledge by identifying key business areas and functions to address to achieve this transformation.

Subfields: Management Information Systems, Management Science and Operations Research, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/10332237227661d6a7cc0c678de1f5d3dcc4f755

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Citations in the corpus

Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment (2019, unknown): “…data analytics initiatives have proven to be complex matters, which depend on a firm’s ability to simultaneously harness multiple resources and capabilities including people, technology, data, processes and structures within a business context, and deploy them synergistically (Vidgen et al., 2017).”
Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities (2020, background): “Nevertheless, due to the constantly evolving technological landscape associated with such technologies, it is important that a logic of continuous learning is infused in organizations that invest in big data [17].”
Prescriptive analytics: Literature review and research challenges (2020, unknown): “To this end, business analytics has evolved beyond a simple raw data analysis on large datasets with the aim to provide organizations a competitive advantage (Mikalef, Pappas, Krogstie, & Giannakos, 2018; Vidgen, Shaw, & Grant, 2017).”
The Role of Leadership in a Digitalized World: A Review (2019, background): “…to connect more than 8 billion devices worldwide (World Economic Forum, 2018), modiﬁed information value and management, and started to change the nature of organizations, their boundaries, work processes, and relationships (Davenport and Harris, 2007; Lorenz et al., 2015; Vidgen et al., 2017).”
Achieving superior organizational performance via big data predictive analytics: A dynamic capability view (2019, background): “These resources help smooth out the challenges (Mikalef & Pateli, 2017; Vidgen et al., 2017) faced by organizations during implementation of BDPA in organizational operations.”
Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review (2022, background): “There is a need to understand the importance of the organisational cultural change that is critical to harness the value of big data analytics (Vidgen et al., 2017).”
Explainable Artificial Intelligence: Objectives, Stakeholders, and Future Research Opportunities (2020, background): “We leave a digital footprint everywhere (Vidgen et al., 2017), through, for instance, online shopping, social media conversations, or usage of mobile navigation apps.”
Big data analytics capabilities: a systematic literature review and research agenda (2017, background): “Despite the limited published research on big data, some studies have focused on the challenges that companies face during the implementation of big data projects (Gupta and George 2016; Vidgen et al. 2017).”