ISO-IEC 88242:2016, also known as the Systems and software engineering—Evaluation of software product quality—Data quality model, is an international standard developed by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC). It provides guidelines and criteria for evaluating the quality of data in software products.
The Importance of Data Quality
Data quality plays a crucial role in modern software systems. Poor data quality can lead to inaccurate results, unreliable decision-making, and compromised system performance. ISO-IEC 88242:2016 helps organizations assess and improve their data quality management processes to ensure reliable and trustworthy information.
Main Characteristics of ISO-IEC 88242:2016
ISO-IEC 88242:2016 defines three main characteristics for assessing data quality:
Accuracy: The extent to which data represents the true values or facts it is supposed to reflect. Accuracy ensures that data is reliable and free from errors.
Completeness: The extent to which all required data elements are present and populated correctly. Complete data provides a comprehensive and meaningful view of the subject.
Consistency: The extent to which data is harmonized and aligned with other related data sources. Consistent data allows for accurate comparisons and analyses.
Evaluating Data Quality Using ISO-IEC 88242:2016
To evaluate data quality using ISO-IEC 88242:2016, organizations need to establish a systematic approach that includes the following steps:
Identify relevant data quality characteristics based on the specific needs and requirements of the software system.
Define measurement criteria and indicators for each data quality characteristic.
Collect data samples and measure their compliance with the defined criteria and indicators.
Analyze the results and identify areas for improvement.
Implement corrective actions to enhance data quality.
By following these steps, organizations can use ISO-IEC 88242:2016 as a framework to continuously monitor and improve the quality of data in their software systems, leading to more accurate and reliable outcomes.
Nina She