Guided analysis

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End-of-life announcement
Release notes
- Release Notes Overview
- Data Workbench 6.74 Release Notes
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Installation
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Dataset configuration
- Understanding Dataset Construction
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Report Server
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Page Tagging
- What Kind of Data Can I Acquire?
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- Acquiring Baseline Measurements
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- Tracking Visitor Activity Within Flash Rich Media Content
- P3P Considerations for Third-Party Page Tagging
- Page Tag Variable Mapping Worksheet
Metrics, dimensions, and filters
- About Data Workbench Metrics, Dimensions, and Filters
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- Dimension Types
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- Baseline Profiles
Geography
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- Installing Data Workbench Geography
- Imagery Layers
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Controlled experiments
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- What Are the Requirements?
- Controlled Experimentation
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- Experiment Design Spreadsheet
Server Administration and Installation
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Sensor
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- Installing
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- Sensor Txlogd.conf File
- Event Data Record Fields
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Installing the Monitoring Profile
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- Workspaces
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Dashboard
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Implement Data Workbench
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- Data Workbench Architecture
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- Implementation of Features
Query API
Data Workbench Integration with Adobe Target
Data Workbench Glossary

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Guided analysis

Last update: 2022-10-04

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Data Workbench
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IMPORTANT

Read more about Data Workbench’s End-of-life announcement.

A guided analysis visualization provides cues for further analysis based on the selections that you make in a workspace.

This visualization helps you identify which dimensions can provide you with more information, that is, those that are most strongly correlated with your selections. The guided analysis visualization in your Adobe application displays the dimensions relevant to your dataset, as in the following Site guided analysis visualization.

NOTE

If a dimension name displays in red, it is not defined in your dataset.

When you make a selection within a workspace, the guided analysis visualization displays check marks to the left and dots to the right of the dimensions to show which ones provides the most relevant information:

Check marks identify the dimensions whose values differ from the benchmark in a statistically significant way (that is, the difference between the selection and the benchmark is not due to random chance).
Dots indicate the degree by which the selection differs from the benchmark. The first dot represents the U statistic, and the second dot represents the V statistic. See Understanding the Statistical Measures. The brighter and larger the dots, the greater the difference, and the more relevant the information for the dimension based on your selection (that is, brighter, larger dots represent more useful information).