Collaborative Business Intelligence

Rizzi, Stefano

doi:10.1007/978-3-642-27358-2_9

Cited by 20 publications

(12 citation statements)

References 36 publications

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“…Here lists two of these challenges. First, the collaborative [10]. Second, management on the quality of shared information also becomes more challenging [11], because the quality of BI analysis result is bounded by the quality of input data, and because external control power is weaker in general to ensure data quality.…”

Section: Requirements For Business Intelligence In Risk Managementmentioning

confidence: 99%

Business Intelligence for Improving Supply Chain Risk Management

Liu

Daniels

Oosterhout

et al. 2013

International Journal of Advanced Logistics

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The risk management over a supply chain has to be founded on the risk management in each of partner companies in the chain. The business relationship and operations dependence inevitably bind the management control efforts of partner companies together. This proposes challenges for supply chain risk management and at the same time for the BI application. In this paper we analyzed the management control situations where business intelligence technology can be applied and describe the concepts of systematic risk analysis to improve the management controls, based on causal analysis of business exceptions. A "risk template" is provide to assist analysts to fully comprehend the risk scenario in the practical business setting for evaluate and re-design the existing controls for management improvement.

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Section: Requirements For Business Intelligence In Risk Managementmentioning

confidence: 99%

Business Intelligence for Improving Supply Chain Risk Management

Liu

Daniels

Oosterhout

et al. 2013

International Journal of Advanced Logistics

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“…This will empower next generation of BI systems to support advanced business performance analytics on any business domain and also, extending "the decision-making process beyond the company boundaries thanks to cooperation and data sharing with other companies and organizations" (Rizzi, 2012).…”

Section: Technological Solutionmentioning

confidence: 99%

Leveraging big-data for business process analytics

Vera-Baquero

Colomo‐Palacios

Stantchev

et al. 2015

The Learning Organization

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Purpose – This paper aims to present a solution that enables organizations to monitor and analyse the performance of their business processes by means of Big Data technology. Business process improvement can drastically influence in the profit of corporations and helps them to remain viable. However, the use of traditional Business Intelligence systems is not sufficient to meet today ' s business needs. They normally are business domain-specific and have not been sufficiently process-aware to support the needs of process improvement-type activities, especially on large and complex supply chains, where it entails integrating, monitoring and analysing a vast amount of dispersed event logs, with no structure, and produced on a variety of heterogeneous environments. This paper tackles this variability by devising different Big-Data-based approaches that aim to gain visibility into process performance. Design/methodology/approach – Authors present a cloud-based solution that leverages (BD) technology to provide essential insights into business process improvement. The proposed solution is aimed at measuring and improving overall business performance, especially in very large and complex cross-organisational business processes, where this type of visibility is hard to achieve across heterogeneous systems. Findings – Three different (BD) approaches have been undertaken based on Hadoop and HBase. We introduced first, a map-reduce approach that it is suitable for batch processing and presents a very high scalability. Secondly, we have described an alternative solution by integrating the proposed system with Impala. This approach has significant improvements in respect with map reduce as it is focused on performing real-time queries over HBase. Finally, the use of secondary indexes has been also proposed with the aim of enabling immediate access to event instances for correlation in detriment of high duplication storage and synchronization issues. This approach has produced remarkable results in two real functional environments presented in the paper. Originality/value – The value of the contribution relies on the comparison and integration of software packages towards an integrated solution that is aimed to be adopted by industry. Apart from that, in this paper, authors illustrate the deployment of the architecture in two different settings.

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“…If trust between different companies is high enough [40], a combination of data sources becomes an opportunity. In difference to adding external data to the companies' own databases, [44,58]. The strongest extension of the data provision process occurs if completely external and independent sources are brought in.…”

Section: Dimensions Of the Frameworkmentioning

confidence: 99%

Structuring Collaborative Business Intelligence: A Literature Review

Kaufmann

Chamoni

2014

2014 47th Hawaii International Conference on System Sciences

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Cooperative work in the processes of analysis and decision support currently gains strong attention in the business world. This is motivated by spreading corporate structures and technical developments like social media and network-oriented data storage that encourage the users' comprehension and demand for easy communication about data. This article reflects the state of research in the domain of business intelligence regarding the opening of processes for new data sources and analysts. Existing approaches are often labeled collaborative business intelligence (CBI), but differ heavily in definitions and focus. Therefore, a framework is presented and three main fields for the research of CBI are identified, which encompass internal communication, data storage with (external) partners and data analysis with partners. This article compares the findings with developments on the software market and describes open topics in the research domain.

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Collaborative Business Intelligence

Cited by 20 publications

References 36 publications

Business Intelligence for Improving Supply Chain Risk Management

Business Intelligence for Improving Supply Chain Risk Management

Leveraging big-data for business process analytics

Structuring Collaborative Business Intelligence: A Literature Review

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