Raw data is information in its original, unorganized state, nothing has been filtered, aggregated, or interpreted yet. Its main strength is granularity: because every detail is preserved, you can apply different filters and visualizations to ask new questions as they come up.
The tradeoff is cost. Making sense of raw data requires time, technical expertise, and computational resources, and that burden grows as datasets get larger.
Processed data is raw data that has already been transformed and aggregated into a more digestible format. It’s ready to use immediately and far easier to interpret, since the heavy lifting has already been done.
The tradeoff here works in the opposite direction: aggregation sacrifices detail. And because the process isn’t reversible, once data has been condensed, you can’t go back and disaggregate it to explore different angles.
When raw data is necessary
You need raw data if: You’re running advanced analytics or machine learning models, conducting detailed user-level analysis for product development, need to comply with audit requirements that demand granular data, or integrate analytics data with other systems like CRMs or data warehouses.
Processed data works if: You’re primarily tracking high-level KPIs, have small analytics teams without data engineering resources, or need quick reporting without complex analysis.
Common mistake: Organizations export raw data “just in case” without having the infrastructure to actually use it. Raw data sitting unused in BigQuery still costs money in storage fees.
Key differences
State and organization: Raw data is unorganized and in its original form. Processed data is cleaned, organized, and summarized for immediate use.
Effort and resources: Raw data requires significant time and resources to process and analyze. Processed data is readily interpretable and easier to work with right away.
Detail and completeness: Raw data is complete and comprehensive, allowing for thorough analysis and the ability to ask questions you didn’t anticipate. Processed data is condensed and may lack detailed information needed for certain analyses.
Flexibility: Raw data offers flexibility for various analyses and reporting needs you haven’t yet defined. Processed data is tailored for specific interpretations determined at aggregation time.
Learn more about the use cases for raw data analytics from this post: How to use raw data in web analytics.
