MAINTAINING FINANCIAL DATA QUALITY FOR BUSINESS INTELLIGENCE

Naveen Kunnathuvalappil Hariharan

No w7n26, OSF Preprints from Center for Open Science

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.

Date: 2019-12-22
New Economics Papers: this item is included in nep-cmp and nep-isf
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:w7n26

DOI: 10.31219/osf.io/w7n26

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