Discrepancy between BQ data from GSC and data shown in GSC UI

[ArturMalek]

Hi, 
I'm exporting GSC data into BQ using native export function (and not API) to avoid sampling .
When I compare specific numbers between GSC UI and data collected in BQ they do not match. 

In GSC UI (search result report) - I choose country + page(exact URL) + specific month filters 

With same filters BQ data shows much higher numbers, both when it comes to clicks and impressions. 

Is it GSC UI sampling/aggregation issue related to this: 
https://support.google.com/webmasters/thread/75525854/why-does-filtering-out-pages-from-performance-...
Or is there any other thing that could cause that discrepancy? 

Thanks,
Artur

[ms4446 · Accepted solution]

When comparing data from Google Search Console (GSC) UI to BigQuery (BQ), it's not uncommon to encounter discrepancies in metrics such as impressions and clicks. These differences can be attributed to several factors, including sampling, aggregation methods, filter settings, data freshness, export configurations, data integrity, and potential updates in data handling by Google.

1. Sampling: GSC UI may use sampling techniques for efficiency, especially with large datasets or complex queries, leading to estimated values. BQ, designed for large-scale analytics, typically provides a complete dataset, which can explain some discrepancies.

2. Aggregation Differences: The method of data aggregation in GSC UI and BQ might differ (e.g., weekly vs. daily aggregation), leading to variations in the metrics.

3. Filter Consistency: Ensure identical filters (date range, country, page URL, etc.) are applied in both GSC UI and BQ. Minor differences in filters can cause significant data variations.

4. Data Freshness: BQ might display a delay in data updates compared to the real-time nature of GSC UI, contributing to discrepancies.

5. Data Granularity: BQ often provides more granular data than GSC UI, which can lead to different interpretations of trends or patterns.

6. Data Export Settings: Check the export settings from GSC to BQ to ensure no data transformation or filtering occurs during this process.

7. Data Integrity: Technical issues during data export/import can affect data integrity. Stay updated on any known issues or updates from Google that might impact data accuracy.

8. Version Updates: Keep track of updates to GSC or BQ, as changes in how data is processed or reported can introduce new discrepancies.

9. Documentation: Consult Google's official documentation 

10. Cross-Verification with Other Tools: If possible, cross-verify data with other analytics tools for additional context or validation.

11. Custom Reports: Creating custom reports in both GSC and BQ with identical parameters can sometimes yield more comparable data.

By thoroughly investigating these factors, you can effectively troubleshoot discrepancies between GSC UI and BQ data, ensuring a more accurate analysis of your website's search performance.

[ArturMalek · Accepted solution]

Hi Mark, 
thanks for your answer. 
Considering all the differences would you say that BQ data is more accurate than GSC UI reports? 

/Artur

[ms4446 · Accepted solution]

When comparing data from Google Search Console (GSC) UI to BigQuery (BQ), it's not uncommon to encounter discrepancies in metrics such as impressions and clicks. These differences can be attributed to several factors, including sampling, aggregation methods, filter settings, data freshness, export configurations, data integrity, and potential updates in data handling by Google.

1. Sampling: GSC UI may use sampling techniques for efficiency, especially with large datasets or complex queries, leading to estimated values. BQ, designed for large-scale analytics, typically provides a complete dataset, which can explain some discrepancies.

2. Aggregation Differences: The method of data aggregation in GSC UI and BQ might differ (e.g., weekly vs. daily aggregation), leading to variations in the metrics.

3. Filter Consistency: Ensure identical filters (date range, country, page URL, etc.) are applied in both GSC UI and BQ. Minor differences in filters can cause significant data variations.

4. Data Freshness: BQ might display a delay in data updates compared to the real-time nature of GSC UI, contributing to discrepancies.

5. Data Granularity: BQ often provides more granular data than GSC UI, which can lead to different interpretations of trends or patterns.

6. Data Export Settings: Check the export settings from GSC to BQ to ensure no data transformation or filtering occurs during this process.

7. Data Integrity: Technical issues during data export/import can affect data integrity. Stay updated on any known issues or updates from Google that might impact data accuracy.

8. Version Updates: Keep track of updates to GSC or BQ, as changes in how data is processed or reported can introduce new discrepancies.

9. Documentation: Consult Google's official documentation 

10. Cross-Verification with Other Tools: If possible, cross-verify data with other analytics tools for additional context or validation.

11. Custom Reports: Creating custom reports in both GSC and BQ with identical parameters can sometimes yield more comparable data.

By thoroughly investigating these factors, you can effectively troubleshoot discrepancies between GSC UI and BQ data, ensuring a more accurate analysis of your website's search performance.

[ArturMalek · Accepted solution]

Hi Mark, 
thanks for your answer. 
Considering all the differences would you say that BQ data is more accurate than GSC UI reports? 

/Artur

[ms4446 · Accepted solution]

Both GSC UI and BQ have their strengths, however BQ data is generally considered more accurate, especially for in-depth analysis, due to its comprehensive nature and lower likelihood of data sampling. However, the choice between GSC UI and BQ should be based on your specific needs, such as the level of detail required, the scale of data, and the type of analysis you intend to perform.