What types of data does my organization contribute?
We’re updating how we use customer data so we can improve our apps and AI experiences for all customers. Learn more about this change
Your Atlassian organization contributes metadata and in-app data based on your data contribution settings.
Read this page to understand:
what metadata and in-app data include
how we safeguard your data contributions. More about how we protect your data
how your data contributions improve app and services for all customers
retention of contributed data
Metadata
Metadata is composed of content attributes and common patterns. Refer to this table to understand the composition of metadata.
Content attributes | Common patterns |
|---|---|
Content attributes are statistical characteristics, numeric fields, and derivatives of your in-app data. Content attributes used to improve apps and experiences for all customers:
Some examples of content attributes include:
| Common patterns are derived only from the following types of in-app data:
To create common patterns that we use to improve apps and experiences for all customers, we extract data that is common across customers and omit data that is low in frequency and may be unique to your organization. Some examples of common patterns include:
|
Metadata helps us build and refine Atlassian app features based on how customers typically use them. With metadata, we can:
make tools like Jira and Confluence more intuitive and efficient
train our search model to improve the relevance of search results
provide accurate and context-rich root cause analysis recommendations so you can resolve incidents quicker and proactively prevent future issues
surface intelligent suggestions that accelerate tasks
In-app data
In-app data refers to content created by users within Atlassian apps. This can include things like:
titles and content in Confluence pages
titles, descriptions, and comments in Jira work items
custom emoji names
custom Jira or Confluence status names
custom workflow names
Before we use in-app data to improve apps and services for all customers, we de-identify and aggregate it. We remove information that directly identifies an individual, such as name or email.
In-app data helps us create better app experiences by learning how customers typically use Atlassian apps. These patterns can help improve search relevance or identify frequent tasks.
We use this data to develop features that:
provide more tailored recommendations and next steps in workflows. For example, instead of Rovo responding with “Do you need help with anything?”, Rovo might suggest “Shall I create a work item for this?”
return fewer dead ends when acronyms mean different things. For example, if you prompt “What does ML stand for?” in Rovo, you may receive a response that is relevant to your domain (Machine Learning in a software development context or Maximum Load in a manufacturing context).
Retention of contributed data
We may extract data that appears frequently across metadata or in-app data from all customers who contribute. This recurring data, similar to common patterns, will be de-identified and aggregated at a customer level, such that it is no longer associated with individual customers.
Data that is de-identified and aggregated at a customer level such that it is no longer associated with individual customers (including common patterns), may be retained for up to seven years.
Retaining this data, which has been de-identified and aggregated at a customer level and is common across customers, enables us to make more meaningful observations over longer periods of time. By unlocking deeper insights into customer behavior, we’re able to drive continual improvements to your overall experience, including by enabling Rovo to:
surface the most relevant results in response to prompts and queries
summarize relevant content more accurately and concisely
identify the best templates for creating new documents
learn which agentic workflows and follow-up questions lead to successful completions, making multi-step tasks faster and less confusing
If you adjust your data contribution settings to opt out of metadata and/or in-app data, or delete an app or site, we will:
remove the corresponding in-app data within 30 days from our datasets used to improve apps and experiences for all customers, and corresponding content attributes within 90 days
retrain any models previously trained on that data
stop collecting new metadata and/or in-app data from the corresponding app
stop identifying recurring patterns from data you previously contributed
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