What are the best ways to ensure data accuracy in your pipelines?

1 Data validation

Data validation is the process of verifying that the data meets the expected standards, rules, and formats before it enters or moves through the pipeline. This process can help prevent data corruption, duplication, inconsistency, and incompleteness. Common techniques for data validation include schema validation, which checks that the data conforms to the predefined structure and data types of the target system or database; business rules validation, which checks that the data satisfies the specific logic and constraints of the business domain or use case; and data profiling, which involves analyzing the data to uncover its characteristics like distribution, frequency, outliers, and anomalies.

2 Data quality checks

Data quality checks are the process of measuring and monitoring the data against the defined quality dimensions, such as accuracy, completeness, consistency, timeliness, and relevance. This process can help identify and resolve data quality issues such as missing values, incorrect values, inconsistent values, outdated values, and irrelevant values. Common data quality checks include data cleansing to correct or remove data that does not meet the standards or requirements, data deduplication to eliminate redundant data in the pipeline or target system/database, and data enrichment to enhance or supplement the data with additional information or attributes from other sources.

3 Metadata management

Metadata management is the process of creating, storing, updating, and accessing the metadata of the data pipeline. Metadata is the data about the data, such as its origin, format, structure, meaning, lineage, and quality. This process can help improve data accuracy by providing visibility, traceability, and context to the data pipeline. Common techniques include creating and maintaining a centralized repository of metadata for the data pipeline, tracking and documenting the data flow and changes throughout the data pipeline from sources to outputs, and adding and updating the metadata with relevant information or comments to explain the data or the data pipeline.

4 Error handling

Error handling is an essential process for detecting, logging, and resolving errors that occur in the data pipeline. These errors can be caused by system failures, network issues, data corruption, data validation failures, or data quality issues. Error handling helps to prevent data accuracy degradation by making the data pipeline resilient, reliable, and recoverable. Common error handling techniques include error detection (implementing mechanisms to monitor and alert on the data pipeline performance and status), error logging (recording and storing information about the errors that occur), and error resolution (applying strategies to fix or mitigate the errors). Examples of error resolution include retries, fallbacks, rollbacks, or manual interventions.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?