Understanding your Data
To get the most value from Petavue, it helps to understand what data is available and how it is organized. Think of this as learning the “map” of your data before you start asking complex questions.
To get the most value from Petavue, it helps to understand what data is available and how it is organized. Think of this as learning the “map” of your data before you start asking complex questions.
1. Data Dictionary
The Data Dictionary is your main reference point. Go to app.petavue.com → Data Hub → Dictionary to see:
- All sources you’ve connected (CRM, marketing automation, product analytics, etc.)
The tables and columns synced from each source
- Which tables/columns are enabled or disabled for analysis, as shown below
- Editable descriptions for tables and columns to clarify business meaning.
- You can edit the following in the Data Dictionary:
- Tables → update the description to clarify purpose.
- Columns → edit the name, description, or format, etc.
Tip: Keep descriptions written in plain business language. For example, instead of type, label it as Account Type. Clear column names not only make results easier to read but also help Petavue run analysis more efficiently and avoid conflicts.
2. Data Exploration
Once you know what data is available, the next step is to explore it with Data Assessments. These are quick checks that highlight your data’s structure, availability, and quality — including column-level fill percentages.
Examples:
Performing a data assessment on your account data
Detecting duplicate records in customer tables
Checking column usage and fill percentage across tables
3. Sample Analyses
Exploration doesn’t stop at quality checks. Use Petavue to run light analyses that help you learn the shape of your data, such as:
Distribution of opportunities by stage
Average deal cycle by product line
Customer segments with the highest retention
Support tickets by severity over time
For more ideas, browse the Prompt Library. It includes ready-to-use prompts for common exploration tasks, tailored to RevOps, CX Ops, and Marketing use cases.
Wrap-Up
By understanding your data first, you set yourself up for more accurate, trusted analyses. A clear data “map” helps you ask sharper questions, validate results faster, and avoid surprises later in the analysis flow.