Reports, explorations, the Google Analytics Data API, and BigQuery Export display data in somewhat different ways. Use the table to compare what data is available where and to understand the limitations of each method of viewing it.
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Data availability & limitations | Reports & Insights | Explorations | Google Analytics Data API | BigQuery |
Data scope | Aggregated | Event and user-level data | Aggregated (mirror standard reports) | Event and user-level data (excluding any value additions that Google Analytics makes to the data found in standard reports and explorations) |
Access methodology | Google Analytics interface | Google Analytics interface | Any-third party application that can access Google Analytics data on user's behalf | GCP Console or any reporting application that can query BigQuery data |
High cardinality1 | No (other) rows for funnel reports. Can return (other) row for predefined standard reports and Realtime report. | No | No (other) rows for funnel method. Can return (other) row for core and realtime. | No |
Sampling2 | Sampling may apply to funnel reports. No sampling for predefined standard reports and Realtime report. | Possible. When an exploration needs to process more events than the quota limit, Analytics uses a representative sample of the available data. | Sampling may apply to funnel method. No sampling for core and realtime. | No |
Thresholding when Google signals is enabled3 | Reports contain Google signals enriched data at aggregate level when Analytics applies thresholds. When Google signals is not enabled, thresholds may still be applied if the data includes demographic or search query information. | Reports contain Google signals enriched data at aggregate level when Analytics applies thresholds. When Google signals is not enabled, thresholds may still be applied if the data includes demographic or search query information. | Reports contain Google signals enriched data at aggregate level when Analytics applies thresholds. When Google signals is not enabled, thresholds may still be applied if the data includes demographic or search query information. | No Google signals related data is available. Note that this can result in the same user being counted multiple times across different devices, as Google signals measures users across devices. Therefore, data exported to BigQuery might show more users when compared with reports based on Google signals data. |
Data driven attribution4 | Yes | Yes | Yes | No |
Conversion modeling5 | Included | Included | Included | Not included |
Behavioral modeling6 | Included in the reporting module, including realtime | Partially included (only in free-form tables) | Included | Not included. BigQuery data contains cookieless pings collected by Google Analytics when consent mode is enabled and each session has a different user_pseudo_id. Modeling may lead to differences between standard reports and granular data in BigQuery, such as fewer active users in reports than BigQuery as modeling tries to predict multiple sessions from an individual user who declined cookies. |
Limitations | 150 custom reports per property | 200 individual explorations per user per property and up to 500 shared explorations per property can be created. Up to 10 segments per exploration can be imported. | Analytics APIs are subject to API quotas. Analytics 360 properties have higher limits for data collection, reporting, retention and quotas. | Standard properties have a daily export limit of 1M events/day. Analytics 360 properties have a nearly limitless export. |
Table footnotes
1 High cardinality: High-cardinality dimensions are dimensions with more than 500 unique values in one day. High-cardinality dimensions increase the number of rows in a report, making it more likely that a report hits its row limit, causing any data past the limit to be condensed into the (other) row. Learn more about high cardinality and the (other) row.
2 Sampling: Data sampling is used when the number of events returned by an exploration exceeds the limit for your property type. This allows you to still explore your data with a high level of detail by using a representative sample of your data. Learn more about sampling.
3 Thresholding when Google signals is enabled: Data thresholds are applied to prevent anyone viewing a report or exploration from inferring the identity of individual users based on demographics, interests, or other signals present in the data. Google signals are session data from sites and apps that Google associates with users who have signed in to their Google Accounts, and who have turned on Ads Personalization. Learn more about data thresholds.
4 Data driven attribution: Data-driven attribution distributes credit for the conversion based on data for each conversion event. Learn more about data driven attribution.
5 Conversion modeling: Conversion modeling allows for accurate conversion attribution without identifying users (for example, due to user privacy, technical limitations, or when users move between devices). Learn more about conversion modeling.
6 Behavioral modeling: Behavioral modeling for consent mode uses machine learning to model the behavior of users who decline analytics cookies based on the behavior of similar users who accept analytics cookies. Learn more about behavioral modeling.