商业智能系统的信息质量:基于成熟度的评估

Abdelhak Ait Touil, Siham Jabraoui
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引用次数: 0

摘要

背景:商业智能(BI)系统的主要作用是向组织内的决策者提供信息。此外,至关重要的是要承认这些信息的质量是非常重要的。一些研究广泛地讨论了信息系统(包括BI)中信息质量的重要性。然而,对“信息质量”影响因素的探讨相对较少。目的:本研究旨在通过调查影响信息质量的商业智能成熟度的决定因素来解决这一文献空白。方法:引入了一个包含三个维度的成熟度模型,即数据质量、BI基础设施和数据驱动文化。从84家公司收集数据,并使用SEM-PLS方法进行分析。结果:分析表明,成熟度对信息质量有高度的正向影响,验证了三个决定因素的相关性。结论:本研究建议并强烈支持数据质量、商业智能基础设施和数据驱动文化作为商业智能成熟度的关键维度的重要性和相关性。成熟度和信息质量之间稳健的统计关系显示了从成熟度角度接近系统的有效性。这项调查为探索影响信息质量的其他维度铺平了道路。关键词:BI基础设施,BI成熟度,数据驱动文化,数据质量,信息质量
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Information Quality of Business Intelligence Systems: A Maturity-based Assessment
Background: The primary role of a Business Intelligence (BI) system is to provide information to decision-makers within an organization. Moreover, it is crucial to acknowledge that the quality of this information is of greatest significance. Several studies have extensively discussed the importance of information quality in information systems, including BI. However, there is relatively little discussion on the factors influencing 'Information quality”. Objective: This study aimed to address this literature gap by investigating the determinants of BI maturity that impacted information quality. Methods: A maturity model comprising three dimensions was introduced, namely Data quality, BI infrastructure, and Data-driven culture. Data were collected from 84 companies and were analyzed using the SEM-PLS approach. Results: The analysis showed that maturity had a highly positive influence on Information Quality, validating the relevance of the three proposed determinant factors. Conclusion: This study suggested and strongly supported the importance and relevance of Data quality, BI infrastructure, and Data-driven culture as key dimensions of BI maturity. The robust statistical relationship between maturity and information quality showed the effectiveness of approaching the systems from a maturity perspective. This investigation paved the way for exploring additional dimensions that impact Information quality. Keywords: BI infrastructure, BI maturity, Data-driven culture, Data quality, Information quality.
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