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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.


Papers
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Journal ArticleDOI
TL;DR: A three-layer value model for e-services is defined, including concerning its efficiency, effectiveness and impact on users' future behavior respectively, and this value model is used for collecting and processing service evaluation data from users.

38 citations

Journal ArticleDOI
TL;DR: In this paper, the authors applied relational view theory and natural resource-based view theory to the analytical results to create new sustainable food chain strategies and to extend the application of those theories to sustainable food supply chain management, finding that the two most important drivers of successful sustainability implementation are eco-innovation and food loss/waste.

38 citations

Journal ArticleDOI
Abstract: Purpose – Business cycles strongly influence corporate sales and profits, yet strategy research largely ignores the possibility that corporate management practices related to the business cycle influence profitability. This paper aims to offer initial empirical support for the view that high peformance firms use a variety of business cycle management (BCM) practices that low performance firms do not.Design/methodology/approach – This exploratory study examines the association of firm performance with business cycle management behaviors identified in the prescriptive literature and further developed from a set of case analyses. The empirical analysis uses a matched sample of 35 pairs of high vs low performers from the S&P 500.Findings – Discriminant and conditional logit analyses provide preliminary evidence that business cycle‐sensitive behaviors such as countercyclical hiring and investment associate positively with firm performance.Research limitations/implications – Future research should use larger da...

38 citations

Journal ArticleDOI
TL;DR: The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector.
Abstract: Purpose: This paper explores the current challenges and drivers for data mining in the AEC sector. Design/methodology/approach: Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics. Findings: The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals. Originality/value: With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and big data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for Data Mining in the AEC sector with a practical focus. Keywords: Business analytics, Data Mining, Data Analytics, AEC, Facilities Management

38 citations

Book
04 Nov 2014
TL;DR: The Business Intelligence Guidebook: From Data Integration to Analytics as mentioned in this paper provides practical guidelines for building successful business intelligence, DW and data integration solutions, including best practices and pragmatic approaches so readers can put them into action.
Abstract: Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled - projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget - turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. Provides practical guidelines for building successful BI, DW and data integration solutions. Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses Describes best practices and pragmatic approaches so readers can put them into action. Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources.

38 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023131
2022262
2021176
2020169
2019185
2018203