Data validation is crucial for ensuring data quality and accuracy. It involves verifying several key dimensions: completeness, timeliness, uniqueness, consistency, validity, and integrity. This post outlines common issues, identification methods, and solutions.
Completeness: All data is present that should be. Problems include missing, null, or placeholder data, occurring at the row level-missing records-or column level-missing data points. Detection might be done via visual dashboards or automated tests. Solutions include upstream investigation or imputation of missing values.
Timeliness: Ensures that the information is current. Issues include stale data and slow performance. Detection can be made through dashboards or automated tests. Solutions include checking ingestion schedules, removing irrelevant data, and setting up alerts.
Uniqueness: This ensures that every record is unique. Problems arise due to duplicate ingestion or too many joins leading to double counting. They are found through counts on unique fields, aggregate counts, ingestion logs, or duplicate hash values. Solutions include preventing duplication, creating primary keys, or using the DISTINCT clause.
Consistency: Ensures that data is in the expected structure and format. Issues include schema or data type changes, and value changes over time-especially for categorical data. Identify via error handling, lookup tables, or regex for free text. Solutions include upstream changes, schema rebuilding, recoding, or improved upstream communication.
Validity: It verifies that the data correctly represents the business context. Problems include data not being in tune with the business rules. Detection using range testing for numeric and date data, regex for string data, or spatial checks. Solution: Treat as missing, get rid of data that cannot be used, or apply correct imputation logic.
Integrity: It ensures that data is logically consistent across data sets. Problems include illogical inconsistencies or different definitions of calculations. It can be detected by comparing key fields or by using conditional logic. Solutions involve investigation and conditional logic scripting.
In a nutshell:
- Completeness: Is all data there?
- Timeliness: Is data current?
- Uniqueness: Is each record unique?
- Consistency: Does data follow the structure?
- Validity: Does data make business sense?
- Integrity: Is data logically consistent?
By ensuring these aspects of validation, an organization ensures its data is reliable for decision-making purposes.