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.

From Data to Insight Work Practices of Analysts in the Enterprise

Abstract: With greater availability of data, businesses are increasingly becoming data-driven enterprises, establishing standards for data acquisition, processing, infrastructure, and decision making. Enterprises now have people dedicated to performing analytic work to support decision makers. To better understand analytic work, particularly the role of enterprise business analysts, researchers interviewed 34 analysts at a large corporation. Analytical work occurred in an ecosystem of data, tools, and people; the ecosystem's overall quality and efficiency depended on the amount of coordination and collaboration. Analysts were the bridge between business and IT, closing the semantic gap between datasets, tools, and people. This article provides an overview of the analytic work in the enterprise, describing challenges in data, tools, and practices and identifying opportunities for new tools for collaborative analytics.

Building re-usable dictionary repositories for real-world text mining

Abstract: Text mining, though still a nascent industry, has been growing quickly along with the awareness of the importance of unstructured data in business analytics, customer retention and extension, social media, and legal applications. There has been a recent increase in the number of commercial text mining product and service offerings, but successful or wide-spread deployments are rare, mainly due to a dependence on the expertise and skill of practitioners. Accordingly, there is a growing need for re-usable repositories for text mining. In this paper, we focus on dictionary-based text mining and its role in enabling practitioners in understanding and analyzing large text datasets. We motivate and define the problem of exploratory dictionary construction for capturing concepts of interest, and propose a framework for efficient construction, tuning, and re-use of these dictionaries across datasets. The construction framework offers a range of interaction modes to the user to quickly build concept dictionaries over large datasets. We also show how to adapt one or more dictionaries across domains and tasks, thereby enabling reuse of knowledge and effort in industrial practice. We present results and case studies on real-life CRM analytics datasets, where such repositories and tooling significantly cut down practitioner time and effort for dictionary-based text mining.

Process Analytics: Concepts and Techniques for Querying and Analyzing Process Data

Abstract: This book starts with an introduction to process modeling and process paradigms, then explains how to query and analyze process models, and how to analyze the process execution data. In this way, readers receive a comprehensive overview of what is needed to identify, understand and improve business processes. The book chiefly focuses on concepts, techniques and methods. It covers a large body of knowledge on process analytics including process data querying, analysis, matching and correlating process data and models to help practitioners and researchers understand the underlying concepts, problems, methods, tools and techniques involved in modern process analytics. Following an introduction to basic business process and process analytics concepts, it describes the state of the art in this area before examining different analytics techniques in detail. In this regard, the book covers analytics over different levels of process abstractions, from process execution data and methods for linking and correlating process execution data, to inferring process models, querying process execution data and process models, and scalable process data analytics methods. In addition, it provides a review of commercial process analytics tools and their practical applications. The book is intended for a broad readership interested in business process management and process analytics. Itprovides researchers with an introduction to these fields by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in business process management to graduate courses in business process analytics. Lastly, it offers professionals a reference guide to the state of the art in commercial tools and techniques, complemented by many real-world use case scenarios.

Envisioning the power of data analytics

Abstract: It could be argued that the power of data is located in what they are used to reveal. Yet we have little understanding of the role played by the emerging industry of data analytics in the interpretation and use of big data. These data analytics companies act as intermediaries in the digital data revolution. Understanding the social influence of big data requires us to understand the role played by data analytics within organisations of different types. This particular article focuses very specifically upon the way in which data and data analytics are envisioned within the marketing rhetoric of the data analytics industry. It is argued that to understand the spread of data analytics and the adoption of certain analytic strategies, we first need to look at the projection of promises upon that data. The way that data and analytics are imagined shapes their incorporation and appropriation into practices and organisational structures – what I call here the data frontiers. This article draws upon a samp...