Abstract

Purpose
This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

Design/methodology/approach
This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Findings
The paper examines the multi-wave implementation of big data analytics in a Taiwanese bank.

Originality/value
The briefing saves busy executives, strategists, and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Challenges with big data analytics

Despite its potential, the adoption of big data analytics is hindered by numerous challenges. Financial consideration and human resources are the two main factors obstructing its implementation. Studies suggest many banks are yet to realize the full benefits and returns of leveraging big data. Most organizations also lack adequate managerial and technical skills to gain value from big data usage. Indeed, many banks unfamiliar with this new practice lack the capabilities to embed big data analytics into their operations and processes. A difficulty firms face is reducing a large and diverse dataset derived from several sources into easy-to-interpret results that can support decisionmaking. It becomes critical for firms to employ experienced data scientists who can uncover insights that can help improve the bank’s operation and performance. Banks also face many non-technical challenges in big data applications. Organizations need the buy-in from organizational members from all ranks-from senior management to lower levels across various functions. The adoption of new technologies necessitates changes in operating practices which may result in organizational resistance. Security, privacy and government regulations can also impact big data applications.

Impact of big data analytics in bank A

Using insights from customer clustering and product affinity models, Bank A was able to adapt its offerings across channels. For example, the organization used cluster affinity scores to target specific marketing campaigns for those customers. Bank A implemented these changes in three waves across ten months. The results showed that the different divisions (e.g. wealth management and personal finance) improved their response rates using the two data analytics methods.