We’re excited to announce a powerful new integration between Elementary and Atlan, bridging the gap between data observability and business collaboration. This integration is designed to empower both technical and business users by delivering trusted data quality insights directly into Atlan’s data catalog.
How business users interact with data
For business users, working with data often comes with uncertainties.
- Can this data be trusted?
- Has it been validated?
- Should I run my own checks before starting my analysis?
These questions can slow down productivity and lead to hesitancy in using the available data. Without visibility into the health of data assets, business users are left to rely on assumptions—or spend additional time and resources verifying data themselves.
The gap between engineers and business users
Data engineers are often equipped with tools to monitor and maintain data pipelines, but the insights they generate rarely reach business users. Known data issues remain confined to engineering teams, creating a disconnect between those who produce the data and those who consume it.
This gap can lead to:
- Mistrust of the data by business users.
- Duplication of effort as business teams create ad hoc validation processes.
- Misaligned decision-making due to incomplete communication about data quality.
Data Health Scores: A common language
At Elementary, we’ve introduced data health scores as a simple, transparent way to communicate the quality of data assets. Data health scores are a quantitative measure of data quality that combine multiple dimensions—such as accuracy, completeness, and freshness—into a single, easy-to-understand metric. These scores provide both technical and business users with a shared understanding of data reliability, fostering trust and collaboration across teams.
Why are data health scores important?
Data health scores remove the ambiguity around data quality. Instead of vague assurances, teams can now say, “This dataset has a health score of 95%, indicating it’s reliable for analysis.” They allow organizations to:
- Communicate effectively: Bridge the gap between engineers and business users by offering a common language for discussing data quality.
- Prioritize fixes: Focus on the most critical issues affecting overall health.
- Track progress: Monitor improvements in data quality over time.
How does Elementary calculate data health scores?
Elementary evaluates data quality through six key dimensions: accuracy, validity, uniqueness, completeness, consistency, and freshness. Each test run in your dbt project contributes to these dimensions, and Elementary calculates scores based on test results. These dimension-level scores are then aggregated to produce a total health score for each dataset or domain.
To dive deeper into how these scores are calculated and used, read this post about measuring data health scores or check out our docs.
Elementary meets users where they are
At Elementary, we believe that data quality should integrate seamlessly into the tools that users already rely on.
- For engineers: Elementary integrates directly with dbt, because that’s where they work to build and manage pipelines.
- For business users: Many spend their time in data catalogs like Atlan, exploring and interacting with datasets.
To meet these users where they are, we’ve brought data health insights directly into Atlan, making it easier than ever for teams to collaborate and trust their data.
The integration: Data quality meets collaboration
This integration allows Elementary to push data quality insights into Atlan through a custom metadata API. Here’s what it enables:
- Data Health Scores in Atlan: View an asset's overall data health score directly within Atlan, providing instant visibility into its quality.
- Open Incident Tracking: Quickly identify how many active data quality incidents are associated with a dataset.
- Enhanced Context: Equip Atlan users with the critical insights they need to evaluate and trust data assets at a glance.
How It Works
- Elementary collects and monitors data quality metrics from your pipelines.
- These insights are pushed to Atlan via a custom metadata API.
- Business users in Atlan can access health scores and incident details as part of the dataset metadata.
For more details, check out our integration documentation.
Catalogs vs. Data Observability tools
While Atlan focuses on cataloging, collaboration, and discoverability, Elementary specializes in data observability—monitoring, testing, and validating the health of your pipelines. Together, they create a comprehensive solution for both technical and business users.
What’s next for this integration
This is just the beginning. Here’s what’s coming next:
- Add more relevant information such as test coverage score (are there enough tests on this data asset?)
- Make data health scores and open incidents on assets visible in the Atlan browser extension
- Leverage Atlan data contracts and column profiling to generate test recommendations in Elementary
By continuously improving the connection between technical and business workflows, we’re ensuring data observability becomes a part of every team’s toolkit—wherever they work.
Curious to see how it works?
If you’re interested in understanding how the Elementary and Atlan integration can fit into your workflow, book a demo with our team.
Contributors
We’re excited to announce a powerful new integration between Elementary and Atlan, bridging the gap between data observability and business collaboration. This integration is designed to empower both technical and business users by delivering trusted data quality insights directly into Atlan’s data catalog.
How business users interact with data
For business users, working with data often comes with uncertainties.
- Can this data be trusted?
- Has it been validated?
- Should I run my own checks before starting my analysis?
These questions can slow down productivity and lead to hesitancy in using the available data. Without visibility into the health of data assets, business users are left to rely on assumptions—or spend additional time and resources verifying data themselves.
The gap between engineers and business users
Data engineers are often equipped with tools to monitor and maintain data pipelines, but the insights they generate rarely reach business users. Known data issues remain confined to engineering teams, creating a disconnect between those who produce the data and those who consume it.
This gap can lead to:
- Mistrust of the data by business users.
- Duplication of effort as business teams create ad hoc validation processes.
- Misaligned decision-making due to incomplete communication about data quality.
Data Health Scores: A common language
At Elementary, we’ve introduced data health scores as a simple, transparent way to communicate the quality of data assets. Data health scores are a quantitative measure of data quality that combine multiple dimensions—such as accuracy, completeness, and freshness—into a single, easy-to-understand metric. These scores provide both technical and business users with a shared understanding of data reliability, fostering trust and collaboration across teams.
Why are data health scores important?
Data health scores remove the ambiguity around data quality. Instead of vague assurances, teams can now say, “This dataset has a health score of 95%, indicating it’s reliable for analysis.” They allow organizations to:
- Communicate effectively: Bridge the gap between engineers and business users by offering a common language for discussing data quality.
- Prioritize fixes: Focus on the most critical issues affecting overall health.
- Track progress: Monitor improvements in data quality over time.
How does Elementary calculate data health scores?
Elementary evaluates data quality through six key dimensions: accuracy, validity, uniqueness, completeness, consistency, and freshness. Each test run in your dbt project contributes to these dimensions, and Elementary calculates scores based on test results. These dimension-level scores are then aggregated to produce a total health score for each dataset or domain.
To dive deeper into how these scores are calculated and used, read this post about measuring data health scores or check out our docs.
Elementary meets users where they are
At Elementary, we believe that data quality should integrate seamlessly into the tools that users already rely on.
- For engineers: Elementary integrates directly with dbt, because that’s where they work to build and manage pipelines.
- For business users: Many spend their time in data catalogs like Atlan, exploring and interacting with datasets.
To meet these users where they are, we’ve brought data health insights directly into Atlan, making it easier than ever for teams to collaborate and trust their data.
The integration: Data quality meets collaboration
This integration allows Elementary to push data quality insights into Atlan through a custom metadata API. Here’s what it enables:
- Data Health Scores in Atlan: View an asset's overall data health score directly within Atlan, providing instant visibility into its quality.
- Open Incident Tracking: Quickly identify how many active data quality incidents are associated with a dataset.
- Enhanced Context: Equip Atlan users with the critical insights they need to evaluate and trust data assets at a glance.
How It Works
- Elementary collects and monitors data quality metrics from your pipelines.
- These insights are pushed to Atlan via a custom metadata API.
- Business users in Atlan can access health scores and incident details as part of the dataset metadata.
For more details, check out our integration documentation.
Catalogs vs. Data Observability tools
While Atlan focuses on cataloging, collaboration, and discoverability, Elementary specializes in data observability—monitoring, testing, and validating the health of your pipelines. Together, they create a comprehensive solution for both technical and business users.
What’s next for this integration
This is just the beginning. Here’s what’s coming next:
- Add more relevant information such as test coverage score (are there enough tests on this data asset?)
- Make data health scores and open incidents on assets visible in the Atlan browser extension
- Leverage Atlan data contracts and column profiling to generate test recommendations in Elementary
By continuously improving the connection between technical and business workflows, we’re ensuring data observability becomes a part of every team’s toolkit—wherever they work.
Curious to see how it works?
If you’re interested in understanding how the Elementary and Atlan integration can fit into your workflow, book a demo with our team.
