Data transformation is the process of converting raw data into a clean, structured, and analytics-ready format. It is a core step in ETL and ELT pipelines and directly impacts data quality and usability.

Common transformations include:

• Cleaning and standardizing values

• Converting data types

• Joining datasets

• Aggregating metrics

• Applying business rules

• Enriching data with derived fields

For example, raw event logs might be transformed into daily user activity summaries, or transaction records might be enriched with customer attributes.

In modern analytics stacks, transformations often happen inside the data warehouse using SQL and tools like dbt. This approach improves scalability, transparency, and testing.

Data transformation is where business logic lives. Poorly designed transformations lead to inconsistent metrics and confusing dashboards.

Best practices include:

• Clear naming conventions

• Version-controlled transformation code

• Automated testing

• Documentation

• Separation of raw and modeled layers

From a BI perspective, transformations enable self-service analytics by making data intuitive and reliable.

Data transformation is the bridge between raw data and meaningful insights. When done well, it simplifies analytics and increases trust across the organization.