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Decision making in the context of business intelligence and data quality

L. Marshall, R. de la Harpe
SA Journal of Information Management | Vol 11, No 2 | a404 | DOI: | © 2009 L. Marshall, R. de la Harpe | This work is licensed under CC Attribution 4.0
Submitted: 12 February 2009 | Published: 12 February 2009

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L. Marshall, Cape Peninsula University of Technology Cape Town, South Africa
R. de la Harpe, Cape Peninsula University of Technology Cape Town, South Africa

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Abstract Making decisions in a business intelligence (BI) environment can become extremely challenging and sometimes even impossible if the data on which the decisions are based are of poor quality. It is only possible to utilise data effectively when it is accurate, up-to-date, complete and available when needed. The BI decision makers and users are in the best position to determine the quality of the data available to them. It is important to ask the right questions of them; therefore the issues of information quality in the BI environment were established through a literature study. Information-related problems may cause supplier relationships to deteriorate, reduce internal productivity and the business' confidence in IT. Ultimately it can have implications for an organisation's ability to perform and remain competitive. The purpose of this article is aimed at identifying the underlying factors that prevent information from being easily and effectively utilised and understanding how these factors can influence the decision-making process, particularly within a BI environment. An exploratory investigation was conducted at a large retail organisation in South Africa to collect empirical data from BI users through unstructured interviews. Some of the main findings indicate specific causes that impact the decisions of BI users, including accuracy, inconsistency, understandability and availability of information. Key performance measures that are directly impacted by the quality of data on decision-making include waste, availability, sales and supplier fulfilment. The time spent on investigating and resolving data quality issues has a major impact on productivity. The importance of documentation was highlighted as an important issue that requires further investigation. The initial results indicate the value of


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