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Maintaining Financial Data Quality For Business Intelligence

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  • Hariharan, Naveen Kunnathuvalappil

Abstract

Only when the input data is reliable can mathematical models and business intelligence systems for decisionmaking produce accurate and effective outputs. However, data taken from primary sources and gathered in a data mart may contain several anomalies that analysts must identify and correct. This research covers the activities involved in creating a high-quality dataset for business intelligence and data mining. Three techniques are addressed to achieve this goal: data validation, which detects and reduce anomalies and inconsistencies; data modification, which enhances the precision and robustness of learning algorithms; and data reduction, which produces a set of data with fewer characteristics and records but is just as insightful as the original dataset.

Suggested Citation

  • Hariharan, Naveen Kunnathuvalappil, 2019. "Maintaining Financial Data Quality For Business Intelligence," OSF Preprints w7n26, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:w7n26
    DOI: 10.31219/osf.io/w7n26
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    References listed on IDEAS

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