ELEMENTARY CLOUD

Once you start sharing data with downstream consumers and stakeholders one of the most important things that you want to create is trust. Trust that the data that is being used is “healthy”. Imagine being a data analyst using a specific data asset but you constantly run into data quality issues. You will eventually lose trust.

This is why we created data health scores in Elementary. It is a way to share an overview of the health of your data assets.

To measure health we use an industry standard framework of Data Quality Dimensions. These dimensions help assess the reliability of data in various business contexts. Ensuring high-quality data across these dimensions is critical for accurate analysis, informed decision-making, and operational efficiency.

To learn more, watch the webinar Measuring Data Health with Elementary

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Data Health Dashboard

The Data Health Dashboard is intended for your data consumers and stakeholders, that want to get a summary of what is happening with the data in your organization.

It gives a high-level overview that doesn’t require deep technical knowledge or going into specific test results. the dashboard presents the data health in a simple way, by giving a health score, and using a color code to indicate if this score is healthy. Filters are available at the top of the page, making it easy to see the data health in different contexts.

The dashboard is based on the 6 Data Quality Dimensions:

Freshness

Ensures that data is up to date and reflects the latest information.

Completeness

Ensures all required data is available, without missing values.

Accuracy

Ensures that data represents the real-world scenario correctly.

Consistency

The degree to which data remains uniform across multiple instances.

Uniqueness

Ensures that each entity is represented only once and there are no duplicates.

Validity

Ensures that data conforms to rules or expectations, such as acceptable ranges or formats.

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How is the data health score calculated?

Each test you run in either dbt or Elementary is mapped to one of these pillars, and given a score. The scoring method is very simple:

  • If the test passes, the score is 100
  • If the test is in warn status, the score is 50
  • If the test is in fail status, the score is 0

The results are aggregated to give a health score for each pillar. The total score is a weighted average of the 6 pillars, where the weight is configurable. The thresholds for the color coding (green, yellow and red) are also configurable.

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Can I customize the quality dimension mapping of my tests?

Of course! Each test you run, whether it’s a generic or a custom test, can be mapped to one of the 6 quality dimensions. The way to do so is to add quality_dimension to the test definition in your dbt project:

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Coming soon

  • Send a daily report of the data health to your stakeholders
  • Compare the data health of different domains
  • Set up alerts for when the data health is below a certain threshold
Data Quality DimensionsData Catalog