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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 new framework for business models that takes into account disruptive technologies, which identifies four stages in the development of new technologies, and distinguishes between supply and demand business models.
Abstract: This paper suggests a new framework for business models that takes into account disruptive technologies. It identifies four stages in the development of new technologies. In each of these stages there are factors that should be overcome. Stage 1 emphasises technical issues. In stage 2 environmental factors such as law and adoption should be considered. Developers begin to incorporate traditional business model factors in stage 3. Stage 4 focuses on factors that will sustain the business. The paper also distinguishes between supply and demand business models. The development of the literature on peer to peer networks illustrates these stages.

67 citations

01 Jan 2009
TL;DR: This chapter shows how process mining techniques such as process discovery and conformance checking can be used to support process modeling and process redesign and illustrates how processes can be improved and optimized over time using analytics for explanation, prediction, optimization and what-if-analysis.
Abstract: Business Process Intelligence (BPI,) is an emerging area that is getting increasingly popularfor enterprises The need to improve business process efficiency, to react quickly to changes and to meet regulatory compliance is among the main drivers for BPI BPI refers to the application of Business Intelligence techniques to businessprocesses andcomprises a large range ofapplication areas spanning from process monitoring and analysis to process discovery, conformance checking, prediction and optimization This chapter provides an introductory overview of BPI and its application areas and delivers an understanding of how to apply BPI in one's own setting In particular it shows how process mining techniques such as process discovery and conformance checking can be used to support process modeling and process redesign In addition, it illustrates how processes can be improved and optimized over time using analytics for explanation, prediction, optimization and what-if-analysis Throughout the chapter a strong emphasis is given to describe tools that use these techniques to support BPI Finally, major challenges for applying BPI in practice and future trends are discussed

66 citations

Journal ArticleDOI
TL;DR: Based on 170 samples from firm-level survey, the nomological linkage from IT competence to CRM performance is analyzed and the results show data management capability fully mediates between IT competence and BA use, while customer response capability partially mediating between BA use andCRM performance.

66 citations

Journal ArticleDOI
TL;DR: A model for process OLAP (P-OLAP) is presented and OLAP specific abstractions in process context such as process cubes, dimensions, and cells are defined and a MapReduce-based graph processing engine is presented, to support big data analytics over process graphs.
Abstract: In today's knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of data from a variety of sources. In order to understand businesses one may need to perform considerable analytics over large hybrid collections of heterogeneous and partially unstructured data that is captured related to the process execution. This data, usually modeled as graphs, increasingly come to show all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics. We use the term big process graph to refer to such large hybrid collections of heterogeneous and partially unstructured process related execution data. Online analytical processing (OLAP) of big process graph is challenging as the extension of existing OLAP techniques to analysis of graphs is not straightforward. Moreover, process data analysis methods should be capable of processing and querying large amount of data effectively and efficiently, and therefore have to be able to scale well with the infrastructure's scale. While traditional analytics solutions (relational DBs, data warehouses and OLAP), do a great job in collecting data and providing answers on known questions, key business insights remain hidden in the interactions among objects: it will be hard to discover concept hierarchies for entities based on both data objects and their interactions in process graphs. In this paper, we introduce a framework and a set of methods to support scalable graph-based OLAP analytics over process execution data. The goal is to facilitate the analytics over big process graph through summarizing the process graph and providing multiple views at different granularity. To achieve this goal, we present a model for process OLAP (P-OLAP) and define OLAP specific abstractions in process context such as process cubes, dimensions, and cells. We present a MapReduce-based graph processing engine, to support big data analytics over process graphs. We have implemented the P-OLAP framework and integrated it into our existing process data analytics platform, ProcessAtlas, which introduces a scalable architecture for querying, exploration and analysis of large process data. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.

66 citations

Proceedings ArticleDOI
24 Mar 2014
TL;DR: A conceptual framework for organizing emerging analytic activities involving educational data that can fall under broad and often loosely defined categories, including Academic/Institutional Analytics, Learning Analytics/Educational Data Mining, Learner Analytics/Personalization, and Systemic Instructional Improvement is developed.
Abstract: In this paper, we develop a conceptual framework for organizing emerging analytic activities involving educational data that can fall under broad and often loosely defined categories, including Academic/Institutional Analytics, Learning Analytics/Educational Data Mining, Learner Analytics/Personalization, and Systemic Instructional Improvement. While our approach is substantially informed by both higher education and K-12 settings, this framework is developed to apply across all educational contexts where digital data are used to inform learners and the management of learning. Although we can identify movements that are relatively independent of each other today, we believe they will in all cases expand from their current margins to encompass larger domains and increasingly overlap. The growth in these analytic activities leads to the need to find ways to synthesize understandings, find common language, and develop frames of reference to help these movements develop into a field.

66 citations


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