254. Enhancing Data Quality and Insights in Complex Systems
Invited abstract in session TA-33: Decision Analysis and Artificial Intelligence (AI), stream Decision Analysis.
Tuesday, 8:30-10:00Room: Maurice Keyworth 1.31
Authors (first author is the speaker)
| 1. | Leif Meier
|
| Wirtschaftsinformatik, insb. Supply Chain Management, Westfälische Hochschule |
Abstract
The efficacy of modern information and application systems is deeply rooted in the quality and comprehensiveness of their underlying data.
This work introduces and evaluates a set of methodologies aimed at providing clarity over complex datasets and examines supportive techniques to assess and enhance data quality for reliable subsequent analysis.
Our approach investigates the spectrum of interdepedent data fields, scrutinizes dependencies and confidence bands, evaluates logical consistencies, and simulates probability scenarios.
Such methodologies are relevant in any field dealing with intricate data structures: Through a blend of statistical analysis, machine learning, and simulation techniques, we propose a comprehensive framework that not only ensures the accuracy of ingoing information but also enhances its analytical value.
By applying these principles, industries facing data complexity can improve operational efficiency and decision-making processes. Our research underscores the importance of a robust data foundation and seeks to offer practical insights into achieving sustainable data quality management practices.
Keywords
- Analytics and Data Science
- Management Information Systems
- Risk Analysis and Management
Status: accepted
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