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A Survey of Data Quality Measurement and Monitoring Tools
Lisa Ehrlinger1,2, Wolfram Wöß1
1Institute for Application-Oriented Knowledge Processing (FAW), Johannes Kepler University, Linz, Austria.
Frontiers in Big Data
|April 18, 2022
Summary
This study bridges the gap between data quality research and practice by evaluating state-of-the-art tools. It reveals that many research concepts, like general data quality metrics, are underimplemented in current software solutions.
Area of Science:
- Data Science
- Software Engineering
- Information Systems
Background:
- High-quality data is crucial for reliable data analytics and data-driven decision-making.
- Existing research focuses on data quality measurement and metrics, but practical implementation in tools is less explored.
- Data quality is often confined to preprocessing, profiling, and cleansing stages.
Conclusions:
- Current data quality tools often lack the implementation of advanced or generally applicable data quality metrics discussed in research.
- There is a significant opportunity for enhancing data quality tools to better support robust data quality assessment and monitoring.
- The survey aids practitioners in tool selection and informs developers about potential functional improvements for data quality software.
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