How do you ensure data relevance in your data pipeline?

2 Validate data sources

The next step is to validate the data sources that you will use in your pipeline. Data sources can be internal or external, structured or unstructured, static or streaming, and so on. You need to verify that the data sources are reliable, accessible, secure, and compliant with your data relevance criteria. You can use various methods and tools to validate data sources, such as data profiling, data cataloging, data lineage, data governance, and data quality assessment. You should also monitor the data sources for any changes or issues that might affect their relevance.

3 Transform data appropriately

The third step is to transform the data according to your data relevance criteria and the needs of your data outputs. Data transformation can involve various operations, such as filtering, aggregating, joining, enriching, cleaning, standardizing, and formatting the data. You need to ensure that the data transformation preserves or enhances the relevance of the data, and does not introduce any errors or inconsistencies. You can use various frameworks and tools to perform data transformation, such as ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), or data wrangling.

4 Deliver data effectively

The final step is to deliver the data to your data outputs, such as dashboards, reports, models, or applications. You need to ensure that the data delivery is timely, efficient, secure, and compatible with your data outputs. You also need to ensure that the data delivery provides the right level of detail, context, and insight for your end users and stakeholders. You can use various methods and tools to deliver data, such as APIs, data warehouses, data lakes, data marts, or data pipelines.

By following these steps, you can ensure data relevance in your data pipeline, and improve the quality and value of your data products and services.