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.

Data Insights: New Ways to Visualize and Make Sense of Data

Abstract: Data Insights offers multi-disciplinary perspectives and useful information about how visualizations can open your eyes to data. This thought-provoking book takes a conversational approach to presenting an overview of the subject, while also focusing on key details. It highlights the ideas and work of a variety of people who are actively contributing to this still emerging field. Case studies from business analytics, healthcare, games, security, and network monitoring, among others, portray what is going on in data visualization today. A diverse blend of original illustrations and real-world examples, both classical and cutting-edge, help fill in the picture. This book provides an approachable overview of important aspects of data visualization, and... Demonstrates, with a variety of case studies, how visualizations can foster a clearer and more comprehensive understanding of data Answers the question, "How can data visualization help me?" with discussions of how it fits into a wide array of purposes and situations Makes the case that data visualization is not just about technology; it also involves a deeply human process Demonstrates, with a variety of case studies, how visualizations can foster a clearer and more comprehensive understanding of data Answers the question, "How can data visualization help me?" with discussions of how it fits into a wide array of purposes and situations Makes the case that data visualization is not just about technology; it also involves a deeply human process Table of Contents Chapter 1 From Terabytes to Insights Chapter 2 A More Beautiful Question Chapter 3 Winning Combinations: Working with the Ingredients of Data Visualization Chapter 4 Pathways, Purposes, and Points of View Chapter 5: Views You Can Use Chapter 6 Thinking...Machines Chapter 7 Hindsight, Foresight, and Insight

Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows

Abstract: The widespread availability of Customer Relationship Management applications in modern organizations, allows companies to collect and store vast amounts of high-detailed customer-related data. Making sense of these data using appropriate methods can yield insights into customers’ behaviour and preferences. The extracted knowledge can then be explored for marketing purposes. Social Network Analysis techniques can play a key role in business analytics. By modelling the implicit relationships among customers as a social network, it is possible to understand how patterns in these relationships translate into competitive advantages for the company. Additionally, the incorporation of the temporal dimension in such analysis can help detect market trends and changes in customers’ preferences. In this paper, we introduce a methodology to examine the dynamics of customer communities, which relies on two different time window models: a landmark and a sliding window. Landmark windows keep all the historical data and treat all nodes and links equally, even if they only appear at the early stages of the network life. Such approach is appropriate for the long-term analysis of networks, but may fail to provide a realistic picture of the current evolution. On the other hand, sliding windows focus on the most recent past thus allowing to capture current events. The application of the proposed methodology on a real-world customer network suggests that both window models provide complementary information. Nevertheless, the sliding window model is able to capture better the recent changes of the network.

Managing big data in coal-fired power plants: a business intelligence framework

Abstract: Purpose Many power producers are looking for ways to develop smarter energy capabilities to tackle challenges in the sophisticated, non-linear dynamic processes due to the complicated operating conditions. One prominent strategy is to deploy advanced intelligence systems and analytics to monitor key performance indicators, capture insights about the behavior of the electricity generation processes, and identify factors affecting combustion efficiency. Thus, the purpose of this paper is to outline a way to incorporate a business intelligence framework into existing coal-fired power plant data to transform the data into insights and deliver analytical solutions to power producers. Design/methodology/approach The proposed ten-step business intelligence framework combines the architectures of database management, business analytics, business performance management, and data visualization to manage existing enterprise data in a coal-fired power plant. Findings The results of this study provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NOx and consequently explain and predict the leading causes of variation in the emission of NOx in the combustion process. Research limitations/implications Once the framework is integrated into the power generation process, it is important to ensure that the top management and the data analysts at the plants have the same perceptions of the benefits of big data and analytics in the long run and continue to provide support and awareness of the use of business intelligence technology and infrastructure in operational decision making. Practical implications The key finding of this study helps the power plant prioritize the important factors associated with the emission of NOx; closer attention to those factors can be promptly initiated in order to improve the performance of the plant. Originality/value The use of big data is not just about implementing new technologies to store and manage bigger databases but rather about extracting value and creating insights from large volumes of data. The challenge is to strategically and operationally reconsider the entire process not only to prepare, integrate, and manage big data but also to make proper decisions as to which data to select for the analysis and how to apply analytical techniques to create value from the data that is in line with the strategic direction of the enterprise. This study seeks to fill this gap by outlining how to implement the proposed business intelligence framework to provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NOx and to explain and predict the leading causes of variation in the emission of NOx in the combustion process.

From Data to Knowledge: A Cognitive Approach to Retail Business Intelligence

Abstract: Consumer-oriented companies can no longer afford to make decisions or measure results based on gut feeling. They must be able to take advantage of all available data. Advanced analytics makes it possible to capture value and benefit from big data, however, this isn't a given. Companies must hire, develop, and retain skilled analysts, who can distinguish relevant from irrelevant data, draw the right assumptions, and translate information into insights. To lighten the burden on companies and support big data analytics, this paper presents a KID (Data-Information-Knowledge) model based on a cognitive approach which can accumulate experience and gain knowledge by continuously perceiving data, interpreting data into meaningful information, absorbing incoming information, and updating knowledge as humans do. This is a process of from data to knowledge and knowledge about correlations among attributes, making assumptions, and testing the assumptions with appropriate algorithms which are constantly updated and summarized in this data-information-knowledge cyclic process. This approach is applied to a retail business for understanding customer purchasing and product sale situations, so as to support provision of better service and timely adaptation of business strategy.

The Affordances of Business Analytics for Strategic Decision-Making and Their Impact on Organisational Performance.

Abstract: Increasingly, business analytics is seen to provide the possibilities for businesses to effectively support strategic decision-making, thereby to become a source of strategic business value. However, little research exists regarding the mechanism through which business analytics supports strategic decisionmaking and ultimately organisational performance. This paper draws upon literature on IT affordances and strategic decision-making to (1) understand the decision-making affordances provided by business analytics, and (2) develop a research model linking business analytics, data-driven culture, decision-making affordances, strategic decision-making, and organisational performance. The model is empirically tested using structural equation modelling based on 296 survey responses collected from UK businesses. The study produces four main findings: (1) business analytics has a positive effect on decision-making affordances both directly and indirectly through the mediation of a data-driven culture; (2) decision-making affordances significantly influence strategic decision comprehensiveness positively and intuitive decision-making negatively; (3) data-driven culture has a significant and positive effect on strategic decision comprehensiveness; and (4) strategic decision comprehensiveness has a positive effect on organisational performance but a negative effect on intuitive decision-making.