More about what we store in AWS
Don’t Fret
All data is encrypted at rest and in transit. Most data is pseudonymized for further protection. See below for actual examples of what is stored. If you have any further questions, reach out via our support portal.
Data Retention
We retain the data associated with your Jira instance until you’ve no longer had a supported subscription with us for 60 days. This way if you uninstall and reinstall, or there’s a short gap before renewal, you won’t lose your data. If you want your instance’s data removed earlier, you can reach out in writing to us via our support portal.
Jump to a specific data scheme for detailed information on what we store:
Forecast Configuration Data
Used to define how forecasts are calculated.
Includes:
Forecast names and identifiers
Jira data sources (board, project, JQL)
Throughput and simulation settings
Hierarchical relationships between forecasts
Viewer/admin permission sets
Audit metadata (creator, timestamps, app version)
Purpose: Required to reproduce forecasts consistently and manage access control.
Forecast Results
Stores the output of Monte Carlo simulations.
Includes:
Probability distributions
Completion date ranges
Historical forecast snapshots
Processing status metadata
Purpose: Allows users to review historical forecasts, compare changes over time, and share results.
Portfolio Data
Used to group multiple forecasts for program- or portfolio-level analysis.
Includes:
Portfolio definitions
Associated forecast identifiers
Display configuration (selected Jira fields)
Permissions and audit metadata
Purpose: Enables consolidated reporting across multiple teams or initiatives.
Scenario Data
Optional "what-if" configurations that modify forecast assumptions.
Includes:
Alternative parameters (e.g., scope or capacity changes)
Scenario-specific overrides
Purpose: Allows experimentation without altering the primary forecast configuration.
User Preference Data
Includes:
Favorite forecasts
Favorite portfolios
Identified by Atlassian account ID and Jira Cloud ID.
Purpose: Improves usability and personalization.
Scheduling Metadata
Used for automated forecast generation.
Includes:
Scheduling frequency and rules
Next execution timestamps
Purpose: Keeps forecasts up to date without manual interaction.
Dependency Configuration
Defines how Jira issue link types represent dependencies.
Includes:
Jira link type identifiers
Directionality and settings
Audit metadata
Purpose: Ensures dependency-aware forecasting when issue relationships are used.
Legacy Connect lookup
Required for handling pre-Forge data retreival
Includes:
Client identifiers
Cloud Id