Business analytics

About: Business analytics is a research topic. Over the lifetime, 3593 publications have been published within this topic receiving 84601 citations. The topic is also known as: Business Analytics & business analytics.

How business analytics systems provide benefits and contribute to firm performance

Abstract: Organisations invest in Business analytics (BA) systems to improve firm performance and gain competitive advantage. BA systems use advanced analytical tools and techniques to analyse organisational data and generate insights. Decision makers at different levels of the organisations leverage the insights and take competitive actions. In this paper, we utilise Resource-based View (RBV) and business value of IT literature to develop a research model to explain the firm performance impacts of BA systems in customer relations. The research model proposes that BA capability creates informational benefits in customer relations using two pathways: (1) Direct creation of informational benefits, and (2) Indirect creation of informational benefits through developing higher-order Analytical CRM capability. Informational benefits once created, contribute to superior firm performance. The research model is evaluated using survey data in Large US-based organisations.

Strategies in managing risks in the adoption of business analytics practices

Abstract: – There are various risks that can derail the adoption of business analytics (BA) practice in a telecom service provider (TSP) thereby jeopardising the possibility to increase profitability and improved customer experience. The purpose of this paper is to analyse different associated risks using situation-actor-process, learnings-actions-performance (SAP-LAP) model and build mitigation strategies for the adoption. Also the risks are ranked using the interactive ranking process (IRP) methodology and the dominating matrix provides insight to the actions and actors that need attention to improve the processes and performance. , – A case study of a TSP (X1) was analysed through close interactions with experts within the company and externals involved in setting up the BA practice in X1. Using the SAP-LAP framework risks were identified and then the IRP was used to rank the actors w.r.t performance and actions w.r.t processes. , – X1 has taken initiatives for setting up the BA practice in order to improve the profitability and customer experience through data insights. The suggested conceptual SAP-LAP model helps to address risk mitigation strategies for its adoption and the IRP frameworks helps in understanding the prioritisation matrix (using the ranking) to be considered to mitigate the risks. , – The IRP framework is limited to certain relationships between actors, w.r.t processes and actions w.r.t performance for the prioritisation matrix of identified risks. This has scope to be further expanded to other relationships and therefore refining the findings. Also this approach could be used to study other industries too. , – SAP-LAP model identifies the risks in adopting the BA practice in a TSP. The synthesis of SAP leads to LAP, which bridges the gap by suggesting improvement actions based on the learning from the present situation, actors and processes. IRP provides the prioritisation matrix for mitigating the risks by identifying the dominating factors. , – BA practice plays a dominant role in a TSP. An approach to study the risks of its adoption using the SAP-LAP and IRP framework bridges the gap between the academic and corporate world. This paper is very relevant to managers involved in setting up a BA practice. For the academic, use of research model validates the identification of risks that are recognised in the corporate world and prioritising the risks that need to be addressed.

About "Learning" and "Analytics"

Abstract: This issue of the Journal of Learning Analytics features three special sections that look into topics of learning analytics for 21 st century skills, multimodal learning analytics, and sharing of datasets for learning analytics. The issue also features a paper that looks at models for early detection of students at risk in tertiary education. The editorial concludes with a summary of the changes in the editorial team of the journal.

The Business Impact of Predictive Analytics

Abstract: Metrics of financial business impact such as ROI and net profit are the key to success with predictive analytics in real world applications. Yet, current practices do not focus on such metrics and instead employ what may be termed “technical metrics.” As we will see, technical metrics tell an organization little about whether a predictive model will benefit the organization. This chapter will review these topics, explain the difference between financial business impact metrics and technical metrics, and show how financial business impact metrics can be calculated with little effort. Throughout the chapter, we will illustrate the use of technical and financial business impact metrics with running examples in the areas of insurance fraud prevention and predictive marketing. AbstrAct