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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.


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Proceedings ArticleDOI
05 Nov 2007
TL;DR: An approach to re-engineering the business process modeling in conformity with the multidimensional data model is presented and how the resulting multiddimensional views of surgical workflows enable various perspectives on the data and build a basis for supporting a wide range of analytical queries of virtually arbitrary complexity is demonstrated.
Abstract: The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re-engineering the business process modeling in conformity with the multidimensional data model. Since the business process and the multidimensional model are driven by rather different objectives and assumptions, there is no straightforward solution to converging these models.Our case study is concerned with Surgical Process Modeling which is a new and promising subdomain of business process modeling. We formulate the requirements of an adequate multidimensional presentation of process data, introduce the necessary model extensions and propose the structure of the data cubes resulting from applying vertical decomposition into flow objects, such as events and activities, and from the dimensional decomposition according to the factual perspectives, such as function, organization, and operation. The feasibility of the presented approach is exemplified by demonstrating how the resulting multidimensional views of surgical workflows enable various perspectives on the data and build a basis for supporting a wide range of analytical queries of virtually arbitrary complexity.

29 citations

Book ChapterDOI
27 Aug 2009
TL;DR: An approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools, process optimizations or a combination of such solutions into already existing BPM environments.
Abstract: What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools, process optimizations or a combination of such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.

29 citations

Journal ArticleDOI
TL;DR: Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment.
Abstract: Purpose Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data While studies suggest that BA positively affects organizational performance, there is a lack of academic research The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA Design/methodology/approach Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies Findings Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment Practical implications Extensive use of BA and data-driven decisions will lead to superior firm performance Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities Originality/value This study provides useful management insights into the effective use of BA for improving organizational performance

28 citations

Proceedings ArticleDOI
13 Jun 2016
TL;DR: A novel graph-based Complex Event Processing system GraphCEP is proposed and its performance is evaluated in the setting of two case studies from the DEBS Grand Challenge 2016.
Abstract: In recent years, the proliferation of highly dynamic graph-structured data streams fueled the demand for real-time data analytics. For instance, detecting recent trends in social networks enables new applications in areas such as disaster detection, business analytics or health-care. Parallel Complex Event Processing has evolved as the paradigm of choice to analyze data streams in a timely manner, where the incoming data streams are split and processed independently by parallel operator instances. However, the degree of parallelism is limited by the feasibility of splitting the data streams into independent parts such that correctness of event processing is still ensured. In this paper, we overcome this limitation for graph-structured data by further parallelizing individual operator instances using modern graph processing systems. These systems partition the graph data and execute graph algorithms in a highly parallel fashion, for instance using cloud resources. To this end, we propose a novel graph-based Complex Event Processing system GraphCEP and evaluate its performance in the setting of two case studies from the DEBS Grand Challenge 2016.

28 citations


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