Data must arrive intact and fit for purpose as it travels between your business data source systems, enterprise data warehouse/data lake, and data marts/methods to your data analytics reports, business dashboards, and AI/ML initiatives. Data errors must be caught early before they become costly issues to your business.
Some questions addressed by data testing are:
- Did the problem originate in the source data?
- Were there issues with the transformation logic?
- Is the report or process pulling from the right data mart?
Because different types of errors are introduced as your data makes its way through the data journey, various types of tests are needed.
Pre-screening checks: During ingestion of data from business data source files, test metadata and format to check for no empty values, field type, minimum/maximum value, maximum value, etc. for a clean start on its journey.
Field-Level Tests: As data flows from the data warehouse to data marts, test business logic for aggregation, value range, and transformation tests.
Vital Checks: Based on system constraints, test for uniqueness, completeness, nullness, and referential integrity to catch more errors.
Reconciliation Tests: ETL validation for detailed source to target comparison tests ensure each row of data arrives to its destination.
Report and App Testing: UI and API tests of the analytics tools, business applications, and integration points for final display of data.
Traditional data testing methods are not enough to catch data errors. Manual “stare and compare” data checks are painfully slow and data sampling leaves dangerous gaps, especially when migrating data to new infrastructure or consolidating systems. It is difficult, costly, and time-consuming to detect errors in millions of rows of data from multiple systems. Automated spot checks are insufficient to prevent data errors, as they do not provide comprehensive coverage.
To address this issue, we employ an automated end-to-end data testing approach that is customized for each step of the data journey. These tests are conducted continuously within your CI/CD pipeline to ensure consistent data validation. Building automated data tests requires a collaborative effort among a data engineer who is proficient in data systems, a data scientist who understands data quality and context, particularly in machine learning capabilities, and a test engineer with expertise in automation and test design.
DataOps emphasizes the data pipeline, with clear communication among all stakeholders. Gartner defines DataOps as “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.” Additionally, “automated testing-removing bottlenecks arising from traditional, human-intensive testing approaches-enables higher quality amid more frequent delivery of new capabilities.”