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.

Business Process Analytics Using a Big Data Approach

Abstract: Continuous improvement of business processes is a challenging task that requires complex and robust supporting systems. Using advanced analytics methods and emerging technologies--such as business intelligence systems, business activity monitoring, predictive analytics, behavioral pattern recognition, and "type simulations"--can help business users continuously improve their processes. However, the high volumes of event data produced by the execution of processes during the business lifetime prevent business users from efficiently accessing timely analytics data. This article presents a technological solution using a big data approach to provide business analysts with visibility on distributed process and business performance. The proposed architecture lets users analyze business performance in highly distributed environments with a short time response. This article is part of a special issue on leveraging big data and business analytics.

Broadening the scope and increasing the usefulness of learning analytics: The case for assessment analytics

Abstract: The author offers opinions learning and learning assessment analytics, which are defined as the measurement and analysis of data about learners for the purpose of understanding learning. It is argued that learning analytics have a limited usefulness in higher education because it is based on data generated by student interaction in online learning and social media environments, educational technology whose use remains far from universal. It is argued that the use of data analytics generated from educational assessment will be of more value in higher education, as that is a process in which all teachers and students are engaged.

Conceptual Model of Business Value of Business Intelligence Systems

Abstract: With advances in the business intelligence area, there is an increasing interest for the introduction of business intelligence systems into organizations. Although the opinion about business intelligence and its creation of business value is generally accepted, economic justification of investments into business intelligence systems is not always clear. Measuring the business value of business intelligence in practice is often not carried out due to the lack of measurement methods and resources. Even though the perceived benefits from business intelligence systems, in terms of better information quality or achievement of information quality improvement goals, are far from being neglected, these are only indirect business benefits or the business value of such systems. The true business value of business intelligence systems hides in improved business processes and thus in improved business performance. The aim of the paper is to propose a conceptual model to assess business value of business intelligence systems that was developed on extensive literature review, in-depth interviews, and case study analysis for researching business intelligence systems’ absorbability capabilities or key factors facilitating usage of quality information provided by such systems respectively.

Minding the analytics gap

Abstract: Senior executives increasingly recognize the importance of analytics to creating business value. XL Group plc, a global insurance and reinsurance company based in Dublin, Ireland, is a case in point. Like others in the insurance industry, XL has long relied heavily on data analysis to understand and price its products. XL produces increasingly complex analytics, and demand for analytical insights progressively permeates the organization. To understand the challenges and opportunities associated with the use of business analytics, MIT Sloan Management Review, in partnership with SAS Institute Inc., conducted its third annual survey of 2,719 business executives, managers and analytics professionals from organizations located around the world. The survey and interviews reveal a sizable gap between the production and consumption of analytics. Furthermore, this gap persists and may even grow as organizations mature analytically. Producers of analytics will likely continue to improve their ability to make more sophisticated analytical results, so managers need to find ways to become comfortable making decisions based on analytical results that they do not fully understand

Multitask Agency, Modular Architecture, and Task Disaggregation in SaaS

Abstract: We examine contract choices in the provision of "software-as-a-service" (SaaS), which is a business innovation that transforms information technology (IT) resources into a continuously provided service. We draw upon agency theory and modularity theory to propose that one of the central challenges in service disaggregation is that of knowledge interdependencies across client and provider organizations. The resulting lack of verifiability of certain tasks results in a multitask agency problem. Our key research questions involve (1) the suitability of high-versus low-powered incentives in SaaS contracts when the outsourced tasks involve business analytics that are difficult to verify, and (2) how such contract choices are affected by the modularity of interfaces between the client and the provider. Analysis of data collected from 154 providers of SaaS offering a range of IT services supports our contention that when contracting for business analytics characterized by knowledge interdependencies across clients and providers, incentives should be "low powered." Modularity in the interfaces of the service provider increases the desirability of high-powered incentives in such situations. Our results are robust after accounting for endogeneity issues arising from unobserved matching between service providers and the nature of IT services outsourced by clients. With the increasing importance of information systems in services, this paper suggests that arm's-length relationships and high-powered incentives may be ineffective in incentivizing providers to perform on complex business analytic tasks, unless accompanied by the modularization of interfaces.