What are the most effective data quality checks during extraction?

I usually do these data validity check: Format Checks: Verifying that the data is in the correct format. For example, a date field should be in a specific date format. Length Checks: Checking that the data is within the expected length. For example, a zip code field should not exceed a certain number of characters. Enumeration Checks: Ensuring that the data is one of a predefined set of values. For example, a gender field should only contain ‘Male’, ‘Female’, or ‘Other’. Cross-field Validation: Checking the data consistency across different fields. For example, a person’s age should be consistent with their birth date.

Data consistency is most important factor while extracting data. Data consistency leads to accuracy, completeness, and correctness of data stored in a database. You can also relate consistency to the format or type of data. For example, dates should always be stored in the same format (I.e., ‘YYYY-MM-DD’), and numerical values should always be stored as the same type of number.

Ensuring data consistency is crucial. A minor yet impactful issue revolves around special characters in strings. When data is transferred between sources, the handling of strings varies. In SQL, the distinction between nvarchar and varchar is notable – nvarchar, utilizing Unicode, treats special characters differently than varchar, which restricts data to an 8-bit code page. This discrepancy can result in the removal or altered perception of special characters in different locations, introducing data inconsistencies. Nvarchar is specifically designed for storing any type of Unicode, while Varchar confines data to an 8-bit code page.

Data quality monitoring also involves establishing thresholds for acceptable performance. Implementing proactive measures, such as automated alerts triggered by deviations from predefined benchmarks, ensures prompt attention to potential issues. Regularly reviewing data quality dashboards and logs allows for real-time visualization and documentation of trends, empowering data stewards to take corrective actions. Organizations can enhance overall data quality management during extraction by incorporating feedback loops and adjustments based on monitoring insights, fostering more reliable and valuable data-driven decision-making.

Additional checks worth considering include: - Uniqueness Check: This ensures the absence of duplicate records within the dataset. - Conformity Check: This involves standardizing data formats and units to maintain consistency. It also ensures adherence to naming conventions for tables, columns, and other data elements. - Data Profiling/Pattern Discovery: This check involves the identification of patterns or anomalies within the dataset.

La calidad de los datos es el alma de la toma de decisiones inteligente. Para garantizar información fiable, hay que considerar: Comprobación del esquema: Verifica que la estructura coincida con las expectativas, garantizando coherencia y claridad. Comprobación de la integridad: Evalúa la consistencia entre conjuntos de datos, asegurando que la información sea fiable y esté íntegra. Comprobación de la validez: Verifica si los datos siguen patrones y criterios esperados, manteniendo la cohesión y precisión. Coherencia: Asegura la lógica y congruencia entre los datos, permitiendo análisis acertados. Supervisión continua de la calidad: para mantener la integridad, identificando y corrigiendo desviaciones para mantener la fiabilidad.