data quality

Introduction

Data quality is a critical aspect of data management that significantly impacts the effectiveness and reliability of data-driven decision-making processes. High-quality data is essential for accurate analysis, reliable insights, and effective use of information systems. This synthesis explores various dimensions and frameworks of data quality, the importance of maintaining high data quality, and the implications of poor data quality on different applications.

Key Insights

Dimensions of Data Quality:
- Data quality encompasses multiple dimensions such as accuracy, completeness, consistency, and timeliness, which are crucial for ensuring the reliability and effectiveness of data .
- Intrinsic, contextual, representational, and accessibility dimensions are essential for understanding and meeting data consumers' needs.
Impact on Machine Learning and Information Systems:
- High-quality data directly improves the accuracy, robustness, and generalizability of machine learning models.
- Poor data quality can significantly hinder the performance of information systems and the effectiveness of data-driven decisions .
Data Quality Management and Assessment:
- Effective data quality management involves both a priori (before data collection) and a posteriori (after data collection) estimation of data quality components.
- Various frameworks and methodologies, such as rule-based measurement and the 3As Data Quality-in-Use model, are used to assess and manage data quality in different contexts, including Big Data environments .
Practical Approaches and Tools:
- Practical approaches and tools for improving data quality include data preprocessing methods, database constraints, and data quality filters .
- The use of data quality vectors and aggregation methods helps in developing comprehensive data quality indicators for better data management.
Organizational Awareness and Action:
- There is a significant gap between the perceived and actual levels of data quality within organizations, with many data records containing critical errors.
- Increased awareness and proactive measures are necessary for organizations to improve their data quality standards and reduce the tolerance for bad data.

Conclusion

Data quality is a multifaceted concept that includes various dimensions such as accuracy, completeness, consistency, and timeliness. High-quality data is essential for the effective performance of machine learning models and information systems. Effective data quality management requires both pre- and post-data collection assessments, utilizing various frameworks and methodologies. Despite the growing awareness of data quality issues, many organizations still struggle with poor data quality, highlighting the need for continuous improvement and proactive management practices.

Summary

Introduction

Key Insights

Conclusion

Sources and full results

Beyond Accuracy: What Data Quality Means to Data Consumers

High-quality data should be intrinsically good, contextually appropriate, clearly represented, and accessible to the data consumer, beyond just accuracy.

Data quality management in GIS systems

This paper presents mathematical-statistical methods for a priori and a posteriori estimation of data quality in GIS systems, with examples and numerical values.

Data quality of platforms and panels for online behavioral research

Prolific provides high data quality for online behavioral research, while MTurk shows low data quality, with frequency and purpose of usage predicting data quality.

Data quality in information systems

Data quality is crucial for effective use of information systems, and this paper presents new concepts, tools, and techniques related to data quality, emphasizing the importance of database constraints.

Assessing data quality: A managerial call to action

Organizations need to improve data quality, as on average, 47% of recently created data records contain critical errors, with only 3% of data quality scores rated acceptable.

Data Quality: Concepts, Methodologies and Techniques

This book provides a comprehensive introduction to data quality issues, highlighting the importance of accuracy, completeness, and consistency in various data types and their impact on efficiency and effectiveness.

Measuring data quality in information systems research

The proposed framework effectively measures data quality in information systems research, handling both execution and description issues, and addressing uncertainty in predicates.

A Data Quality in Use model for Big Data

The 3As Data Quality-in-Use model, based on ISO/IEC 25012, 25024, effectively assesses Data Quality-in-Use levels in Big Data projects, indicating trustworthiness and soundness of the results of Big Data analysis.

Evaluating the Impact of Data Quality on Machine Learning Model Performance

High-quality data significantly improves machine learning model performance, enhancing accuracy, robustness, and generalizability.

Data Quality

This methodology improves data quality in LCA by aggregating multiple data quality attributes, increasing precision in input data uncertainty assessments.

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