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.

Big Data Analytics and Business Intelligence in Industry

Abstract: The pervasive nature of digital technologies as witnessed in industry, services and everyday life has given rise to an emergent, data-focused economy stemming from many aspects of human individual and business activity. The richness and vastness of these data are creating unprecedented research opportunities in several fields including urban studies, geography, economics, finance, and social science, as well as physics, biology and genetics, public health and many others. Big data is the term for a collection of large and complex datasets from different sources that are difficult to process using traditional data management and processing applications. Big data is the description of a large amount of either organized or unorganized data that is analyzed to make an informed decision or evaluation. The data can be taken from a large variety of sources including browsing history, geolocation, social media, purchase history and medical records. Big data consists of complex data that would overwhelm the processing power of traditional simple database systems (Hung 2016). There are three main characteristics associated with big data (Dave 2013):

Towards a Process Model for Data-Driven Business Model Innovation

Abstract: The growing importance of data and analytics in a broad range of industries has led to an increased attention of business model literature on this matter. The question of how to capture the business potential associated with data, drives researchers to explore the impact of data and analytics on the business model concept. However, little research has been conducted on how an organization should approach to enrich its existing business model with data or, yet, to develop a completely new, data-driven business model.Our research aims to address this issue by providing a process model for data-driven business model innovation. Therefore, we conduct an extensive review of existing business model innovation processes and derive distinct requirements, taking into account both data specific and business model innovation specific characteristics. The requirements are used to assess the suitability of the identified processes for the development of a data-driven business model. We disclose that existing processes are not appropriate for creating such business models. Based on these findings, we design a new, data-driven business model innovation process that meets the data specific requirements.

Towards a Conceptualization of Data Analytics Capabilities

Abstract: Data analytics is an emerging domain in information systems literature and an increasing number of companies try to use data analytics to make sense out of their environment. Accordingly, the ability of companies to leverage digital data to explore new business opportunities becomes important to develop competitive advantage in digitalized world. Consequently, extant literature investigates different dimensions of data analytics, such as technical and organizational dimensions. In spite of all the insights provided and the practical relevance of the topic, extant literature does not provide a coherent view on data analytics and is relatively silent when it comes to measuring the effects. In this paper we conceptualize data analytics capabilities based on IT capabilities literature and provide a measurement instrument allowing to test effects of data analytics capabilities.

Business process improvement by means of Big Data based Decision Support Systems: a case study on Call Centers

Abstract: Big Data is a rapidly evolving and maturing field which places significant data storage and processing power at our disposal. To take advantage of this power, we need to create new means of collecting and processing large volumes of data at high speed. Meanwhile, as companies and organizations, such as health services, realize the importance and value of "joined-up thinking" across supply chains and healthcare pathways, for example, this creates a demand for a new type of approach to Business Activity Monitoring and Management. This new approach requires Big Data solutions to cope with the volume and speed of transactions across global supply chains. In this paper we describe a methodology and framework to leverage Big Data and Analytics to deliver a Decision Support framework to support Business Process Improvement, using near real-time process analytics in a decision-support environment. The system supports the capture and analysis of hierarchical process data, allowing analysis to take place at different organizational and process levels. Individual business units can perform their own process monitoring. An event-correlation mechanism is built into the system, allowing the monitoring of individual process instances or paths.

Problems in Big Data Analytics in Learning

Abstract: When the National Academy of Engineering issued its grand challenges – specifically the one on “advancing personalized learning” (National Academy of Engineering, 2008) – it called for the development of new instrumentation, tools, and methodologies to bring learning closer to the learners and their personal choices. Big data plays a critical role in beginning to meet this grand challenge. A tremendous amount of data on students’ learning and behavioral experiences is captured in a wide variety of institutional systems. These data range from student demographics and socio-economic backgrounds to data about academic progress and extend down to individual mouse clicks when students are accessing course materials or their time spent viewing a screen of course information. Data captured from automated software-based learning environments in combination with more traditional forms of educational data provide a unique opportunity to understand how learning occurs and to engineer these processes in unprecedented ways. Data by themselves have only limited value. True transformation of educational ecosystems lies in converting these data into actionable intelligence (meaning insights and knowledge that enable learners and other stakeholders to act). Learning data vary significantly in modality (such as visual, auditory, and tactile) and dimensionality (range of observed characteristics). More important, the contexts from which these data are derived may vary enormously. It is this unique combination of factors that makes big data in learning such an interesting research artifact. The ability to couple data about learners (actors or agents) and the system structure (courses, schools, university, or industry settings) within which they function is powerful. Many proponents of big data in learning believe that these data, considered as a single unified ecosystem, could eventually uncover the distributed nature of human cognition that is embedded in a thick network of human behaviors. The ability to shed light onto the so-called “ghost in the machine” (Koestler, 1967), where thoughts are embodied in learner actions, has a powerful appeal. In this guest editorial, we discuss the notion of big data and its potential for transforming learning and educational ecosystems.