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.

Stochastic programming and scenario generation within a simulation framework: An information systems perspective

Abstract: Stochastic programming brings together models of optimum resource allocation and models of randomness to create a robust decision-making framework. The models of randomness with their finite, discrete realisations are called scenario generators. In this paper, we investigate the role of such a tool within the context of a combined information and decision support system. We explain how two well-developed modelling paradigms, decision models and simulation models can be combined to create ''business analytics'' which is based on ex-ante decision and ex-post evaluation. We also examine how these models can be integrated with data marts of analytic organisational data and decision data. Recent developments in on-line analytical processing (OLAP) tools and multidimensional data viewing are taken into consideration. We finally introduce illustrative examples of optimisation, simulation models and results analysis to explain our multifaceted view of modelling. In this paper, our main objective is to explain to the information systems (IS) community how advanced models and their software realisations can be integrated with advanced IS and DSS tools.

Towards an Understanding of Business Intelligence

Abstract: Heading – abstract) Given the wide recognition of business intelligence (BI) over the last 20 years, we performed a literature review on the concept from a managerial perspective. We analysed 103 articles related to BI in the period 1990 to 2010. We found that BI is defined as a process, a product, and as a set of technologies, or a combination of these, which involves data, information, knowledge, decision making, related processes and technologies that support them. Our findings show that the literature focuses mostly on data and information, and less on knowledge and decision making. Moreover, in relation to the processes there is a substantial amount of literature about gathering and storing data and information, but less about analysing and using information and knowledge, and almost nothing about acting (making decisions) based on intelligence. The research literature has mainly focused on technologies and neglecting the role of the decision maker. We conclude by synthesizing a unified definition of BI and identifying possible future research streams.

Requirements of an Open Data Based Business Ecosystem

Abstract: Emerging opportunities for open data based business have been recognized around the world. Open data can provide new business opportunities for actors that provide data, for actors that consume data, and for actors that develop innovative services and applications around the data. Open data based business requires business models and a collaborative environment-called an ecosystem-to support businesses based on open data, services, and applications. This paper outlines the open data ecosystem (ODE) from the business viewpoint and then defines the requirements of such an ecosystem. The outline and requirements are based on the state-of-the-art knowledge explored from the literature and the state of the practice on data-based business in the industry collected through interviews. The interviews revealed several motives and advantages of the ODE. However, there are also obstacles that should be carefully considered and solved. This paper defines the actors of the ODE and their roles in the ecosystem as well as the business model elements and services that are needed in open data based business. According to the interviews, the interest in open data and open data ecosystems is high at this moment. However, further research work is required to establish and validate the ODE in the near future.

Big data, big business: bridging the gap

Abstract: Business analytics, occupying the intersection of the worlds of management science, computer science and statistical science, is a potent force for innovation in both the private and public sectors. The successes of business analytics in strategy, process optimization and competitive advantage has led to data being increasingly recognized as a valuable asset in many organizations. In recent years, thanks to a dramatic increase in the volume, variety and velocity of data, the loosely defined concept of "Big Data" has emerged as a topic of discussion in its own right -- with different viewpoints in both the business and technical worlds. From our perspective, it is important for discussions of "Big Data" to start from a well-defined business goal, and remain moored to fundamental principles of both cost/benefit analysis as well as core statistical science. This note discusses some business case considerations for analytics projects involving "Big Data", and proposes key questions that businesses should ask. With practical lessons from Big Data deployments in business, we also pose a number of research challenges that may be addressed to enable the business analytics community bring best data analytic practices when confronted with massive data sets.

Business analytics in supply chains - The contingent effect of business process maturity

Abstract: The paper analyzes the effect of the use of business analytics on supply chain performance. It investigates the changing information processing needs at different supply chain process maturity levels. The effects of analytics in each Supply Chain Operations Reference areas (Plan, Source, Make and Deliver) are analyzed with various statistical techniques. A worldwide sample of 788 companies from different industries is used. The results indicate the changing impact of business analytics use on performance, meaning that companies on different maturity levels should focus on different areas. The theoretical and practical implications of these findings are thoroughly discussed.