Data Quality Assessment in Information Systems
Summary
Data quality assessment encompasses the systematic evaluation of data to ensure its suitability for intended purposes within information systems. As organisations amass vast and heterogeneous datasets—from IoT sensors to transactional records—maintaining high-quality data has become both a technical and managerial imperative. Core dimensions such as accuracy, completeness, consistency and timeliness underpin evaluations, while emerging concerns address semantics, provenance and contextual fitness. Contemporary frameworks view quality as an evolving property shaped by sociotechnical interactions, design of data pipelines and governance policies. Practically, robust assessment informs risk management, supports regulatory compliance and enhances decision support, whether in industrial automation, healthcare analytics or financial modelling. By integrating measurement models with dynamic monitoring tools and cost-benefit analysis, practitioners can identify data defects, prioritise remediation and quantify the impact of quality interventions on operational performance.
Research from Nature Portfolio
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Research from all publishers
Recent studies have advanced both theoretical foundations and applied methods. A comprehensive review of data quality in industrial settings synthesises traditional and cutting-edge frameworks, emphasising deliberate design of quality controls, the evolving relationship between data and users in sociotechnical systems, and strategies for sustaining value as data roles change. In the context of the Industrial Internet of Things, a novel meta-model decomposes data sources into stores and providers, introducing a quality catalogue that links sensor and storage characteristics to trustworthiness assessments, with case evidence showing alignment with expert judgements. Complementarily, a cost-based analysis of completeness and representational consistency in relational databases adopts a fitness-for-use perspective, demonstrating that targeted cleaning can reduce task resolution time by up to 65% and reveal discrepancies with rule-based measures of data quality.
Data Quality Assessment in Information Systems publication trend
The graph below shows the total number of articles in data quality assessment in information systems across all publications each year (not limited to Nature Index journals).
Technical terms
Data quality dimension: Distinct attribute such as accuracy or completeness used to assess dataset quality.
Data provenance: Record of the origins and transformations of data throughout its lifecycle.
Fitness for use: Degree to which data meet the requirements of specific analytical or operational tasks.
Data trustworthiness: Extent to which data can be considered reliable and free from bias or error.
Representational consistency: Uniformity in the format and structure of data elements across records.
References
- Understanding data quality in a data-driven industry context: Insights from the fundamentals. Journal of Industrial Information Integration (2024).
- An approach for assessing industrial IoT data sources to determine their data trustworthiness. Internet of Things (2023).
- Cost-based analysis of the impact of data completeness and representational consistency. Decision Support Systems (2023).
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