ICAEW.com works better with JavaScript enabled.

Data analytics insight

Data Analytics Community members have access to articles from the Operational Research Society's Journal of Business Analytics and the Journal of the Operational Research Society.

Unable to click on an item below?

Articles from the Journal of Business Analytics are only available to Data Analytics Community members. To access them, you'll need to log in or subscribe.

Data science for better productivity

When it comes to performance evaluation, productivity analytics, and benchmarking, Data Envelopment Analysis (DEA) is the most widely used tool. In particular, DEA is data-enabled analytics (Zhu, 2020). It embraces four types of analytics, namely descriptive, diagnostic, predictive, and prescriptive, depending on application. The research question(s) posed on the data will determine the type of analytic lens adopted by DEA. The issue includes twelve research articles by authors from Australia, China, Germany, Spain, Taiwan, the United Kingdom, and the United States; and it spans a spectrum of research areas, encompassing business administration and resource allocation, corporate diversification, pension funds and financial loans, banking, traffic congestion, healthcare, entertainment and hospitality, among others.

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

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. AstraZeneca is making significant investments in analytical capabilities and believes that investment decisions should not be strictly determined by monetary objectives, instead corporate Core Values should be used as guiding principles.

Refund fraud analytics for an online retail purchases

The case study considered is fraud mitigation in return – refund process managed by the customer services of an online retail business. Predictive analytics approach was used to identify early indicators of agent refund fraud – a rare event. The technique used to solve the problem was a Penalised Likelihood based Logistic Regression model. The proposed model allowed the business to select top 5% sample of refund transactions with a higher likelihood of fraud as indicated and queue them for an audit. Implementation of this model resulted in an incremental lift in fraud capture rate.

Management of analytics as a service - results from an action design research project

The ability to generate business-relevant information from data and to exploit it to improve business processes, decision-making, products, and services (business analytics) is a key success factor for businesses today. Answering the call for further research on success-relevant practices and instruments for managing business analytics, we report on the results of a three-year action design research (ADR) project at a global car manufacturer. Drawing on the socio-technical systems theory, we identify seven meta-requirements and specify four principles for the design of an instrument to manage Analytics-as-a-Service (AaaS) portfolios.

Organisational project evaluation via machine learning techniques: an exploration

This study explores ways an organisation can save time; review all proposed innovative, internal ideas; and, identify relevant start-up companies able to bring these ideas to fruition within a knowledge management framework. It uses text-mining techniques, including Python for data extraction and manipulation and topic modelling with Latent Dirichlet Allocation and Jaccard similarity indexes as a basis for evaluation of potentially valuable project ideas.

Business analytics capability, organisational value and competitive advantage

Business analytics makes the assumption that given a sufficient set of analytics capabilities exist within an organisation, the existence of these capabilities will result in the generation of organisational value and/or competitive advantage. Taken further, do enhanced capability levels lead to enhanced impact for organisations? Capability in this study is grounded in the four pillars of governance, culture, technology and people from the Cosic, Shanks and Maynard capability framework.

Optimizing management of emergency gas leaks

Managing the response to reported gas leaks is of significant importance to both utilities and regulators. This paper uses a business analytics approach to investigate strategies for managing gas leak response while balancing the objectives of both utilities and regulators. The paper highlights the analytic methods and the related “soft skills” that must be managed in the business analytics context to ensure an outcome that is acceptable for all stakeholders.

Alignment of business and social media strategies: insights from a text mining analysis

Organisations use social media technologies for various customer engagement and external-facing activities. This research examines the extent to which the business and social media strategies of organisations are aligned.

Topic modelling for analyzing open-ended survey responses

Open-ended responses are widely used in market research studies. Processing such responses requires labour-intensive human coding. This paper focuses on unsupervised topic models and tests their ability to automate the analysis of open-ended responses.

Business analytics and firm performance: role of structured financial statement data

Although business analytics has received its fair share of attention, extant research has paid insufficient attention to establishing and communicating a general understanding of the relationship between analytics and performance. To reduce the identified knowledge gap, this study proposes a comprehensive, theoretical framework to explain the key types of business analytics, their relationships, and how use of business analytics impacts operational and financial performance.

From analytics to artificial intelligence

Analytics have been employed by companies for several decades, but now many firms are interested in building their capabilities for artificial intelligence (AI). Many AI systems, however, are based on statistics and other forms of analytics. Companies can get a “running start” on AI by building upon their analytical competencies. The focus of this article is how to transition from analytics to AI.

Terms of use: You are permitted to access articles subject to the terms of use set by our suppliers and any restrictions imposed by individual publishers. Please see individual supplier pages for full terms of use.