The Integrous Analytics system provides two distinct mechanisms for managing attribution data quality: Data Correction and Data Mapping. These features work together but have some key differences. Understanding how and when to use each will ensure data integrity and clean reporting in your system.
Comparing Data Correction and Data Mapping Modules
Feature | Data Correction | Data Mapping |
|---|---|---|
Purpose | Fix errors in “Raw” fields by directly modifying values | Transform data from “RAW” fields into standardized values without altering underlying values |
Fields Updated | Raw fields (e.g., "RAW FC UTM MED") | Reporting fields only (e.g., "Reporting Medium") |
When it Runs | Only once, when manually executed | On Engagement creation/update, or when configuration is changed |
Destructive | Yes - Original values are permanently modified | No - Original values remain unchanged |
Ideal Use Case | Correcting errors in source data (e.g., list upload or URL mistakes) | Standardizing display values and creating hierarchies for cleaner and bucketed reporting |
Configuration | Self-service through Admin Console | Managed by Integrous Analytics team |
💡Reminder: The values in the Raw fields inform the values in the Reporting Fields
For a refresher, see the article, Processing, RAW, and Reporting Fields Explained.
Examples of when to use Data Correction:
Use Data Correction when the underlying RAW data itself is wrong and needs to be permanently fixed.
Marketing list upload mistakes: You uploaded a tradeshow list but accidentally set RAW FC UTM MED to "Trade Show" when your UTM Taxonomy requires "Tradeshow", or used "RSA 2024" instead of "RSA Conference 2024" for the campaign name.
URL parameter mistakes: Your paid search campaigns have been running with UTM Parameters containing typos like utm_medium=ppc instead of utm_medium=paid-search, creating inconsistent Attribution data.
Legacy values that don't match current taxonomy: Old Engagement records contain values like "cpc" and "adwords" that should be updated to match your current standardized UTM Taxonomy of "paid-search" and "google-ads".
Rule of Thumb: If the RAW data is incorrect, use Data Correction.
For more information, see the Data Correction Module article.
Examples of when to use Data Mapping:
Use Data Mapping when the RAW data is correct, but you want to transform it for cleaner, hierarchical reporting.
Standardizing display values: Your RAW data correctly contains "paid-search", "ppc", and "cpc" from different sources, but you want all of these to display as "Paid Search" in your Reporting Medium field for consistent reporting.
Improving readability: Transform technical values like "webinar-reg" into readable labels like "Webinar Registration" without losing the original data.
Creating reporting hierarchies for bucketed reporting:
You want to group multiple medium types together, such as mapping "Paid Search", "Paid Social", and "Paid Display" all into a "Paid Media" category, then rolling that up into "Marketing" in reporting.
You want to group multiple Engagement Types together, for example, grouping all Engagement Types that start with “Whitepaper” or “Webinar” into an Engagement Type Category of “Whitepaper” or “Webinar” respectively, allowing reporting on kinds of Engagement Types and drilling into individual ones.
Rule of Thumb: If the RAW data is correct but needs to be transformed for cleaner reporting, use Data Mapping.
For more information, see the Data Mapping Module article.