Over-aggregated metrics are one of the easiest ways dashboards become misleading without visibly breaking. A chart might show inflated revenue, duplicated conversions, or totals that are much higher than platform numbers. These problems rarely trigger error messages, but they distort reporting accuracy significantly.
The Analyzer identifies these issues by reviewing how metrics are grouped, aggregated, and combined across charts. Many teams use the Data accuracy scanner to detect over-aggregation early and prevent inflated KPIs from reaching clients or stakeholders.
Why Over-Aggregation Happens in Looker Studio
Aggregation errors occur when the grouping logic does not match the structure of the underlying data. Even a single incorrect dimension can multiply values across rows.
Common Causes Of Over-Aggregated Metrics
- Duplicate rows caused by blending
- Incorrect join keys multiplying values
- Metrics grouped by too many dimensions
- Custom fields aggregating incorrectly
- Event level data incorrectly rolled into daily totals
- Revenue or conversion counts double counted
- Dimensions with incompatible granularity
- Source schema changes affecting grouping
These issues inflate numbers quietly, making reporting unreliable.
How the Analyzer Detects Aggregation Issues
The Analyzer reviews the relationship between dimensions, metrics, and blends to find where duplication or misalignment occurs. It flags patterns that indicate inflated totals or incorrect grouping.
What the Analyzer Evaluates
- Whether a metric is duplicated across rows
- If grouping dimensions cause unintended expansions
- Whether blends create row multiplication
- Granularity mismatches between date fields
- Missing filters that normally prevent inflation
- Changes in schema that alter aggregation behavior
This identifies metrics that look correct visually but contain inaccurate totals.
Finding Duplicate Rows That Inflate Metrics
Duplicate rows are one of the biggest sources of inflated metrics. These duplicates often come from blends, joins, or schema changes.
The Analyzer Flags Duplicate Row Patterns Such As
- Metrics repeating for each unmatched row
- Blends returning more rows than expected
- Join keys create many-to-many relationships
- Event data repeated across dimensions
- Date fields duplicated across sources
These duplications often double or triple totals without users noticing.
Identifying Incorrect Grouping That Distorts Values
Grouping by unnecessary dimensions causes metrics to multiply. The Analyzer reviews charts to detect when grouping logic is too granular.
Grouping Issues the Analyzer Highlights
- Metrics grouped by campaign and keyword when only campaign is needed
- Date grouped by hour when underlying data is daily
- Dimensions included that do not affect analysis
- Fields aggregated differently than expected
- Cross source grouping causing value inflation
Correct grouping is essential for accurate totals.
Spotting Blend Issues That Inflate Metrics
Blends commonly cause over-aggregation because they combine rows from multiple sources. If join keys do not match perfectly, values multiply across datasets.
The Analyzer Uncovers Blend Problems Such As
- One source missing join keys
- Incompatible granularity between blended fields
- Blends returning unmatched rows
- Metrics duplicated during aggregation
- Dimensions missing from one source but used in grouping
These blend-based inflations mislead trend analysis and performance reporting.
Detecting Custom Field Logic That Produces Inflated Values
Custom metrics can unintentionally create inflated totals if the formula does not match the data structure.
The Analyzer Detects Custom Field Issues
- Calculations applied at the wrong aggregation level
- Formulas using fields with mismatched granularity
- Metrics summing values that should be averaged
- Derived fields adding unintended duplication
- Metrics depending on missing or null dimensions
These problems are difficult to detect manually but become obvious with Analyzer reviews.
Recognizing Schema Changes That Alter Aggregation Behavior
Platforms frequently adjust their schema, which changes how fields roll up in dashboards. The Analyzer detects when a schema update breaks aggregation logic.
Schema Shifts Detected By the Analyzer
- New fields replacing old ones
- Date fields updated to new formats
- Attribution data restructured
- Event collections split or merged
- Changed granularity causing trend inflation
Schema updates are a major source of subtle but serious aggregation errors.
Fits Naturally Into a Unified Reporting Practice
Over-aggregation issues are easier to diagnose when teams use structured reporting frameworks. Many rely on the Dataslayer insight base to maintain consistent field structures before the Analyzer evaluates aggregation behavior.
A Reliable Workflow for Preventing Over-Aggregation
- Prepare consistent data pipelines
- Run Analyzer audits across dashboards
- Identify duplicated or inflated metrics
- Correct grouping and blending logic
- Publish accurate reports with validated totals
This reduces confusion and ensures reporting accuracy.
Final Thoughts
Over-aggregated metrics can severely distort dashboards, create reporting errors, and misguide performance decisions. These issues often hide behind charts that look perfectly normal.
The Analyzer detects inflated totals by reviewing grouping logic, blends, duplicate rows, and schema behavior. As dashboards grow in complexity, the Analyzer becomes essential for ensuring KPI accuracy across all reports.
