March 25, 2024

Announcing Bigeye Dependency Driven Monitoring

Optimized monitoring for every column that matters, and none that don't.

Kendall Lovett

We’re thrilled to announce Bigeye Dependency Driven Monitoring and Bigeye Lineage Plus. Together, these powerful new capabilities allow business users and analysts to—for the first time—harness the power of data observability and ensure their data products and analytics dashboards are reliable by default.

Bigeye Dependency Driven Monitoring 

Optimized monitoring for everything that matters, and nothing that doesn't.

Bigeye Dependency Driven Monitoring is a revolutionary new approach that brings the power of data observability directly to enterprise business users.

Simply start by connecting an analytics dashboard or data product and Bigeye will now automatically identify every upstream dependency—even across modern and legacy data sources—and deploy targeted monitoring on only the columns that are actually in use.

☝️ Schedule a custom demo to see Bigeye Dependency Driven Monitoring in action☝️

No more having to choose between deploying expensive, noisy site-wide monitoring or risking a data outage by trying to guess which columns are important. Bigeye Dependency Driven Monitoring lets you focus on the exact data your business relies on.

The benefits of Bigeye Dependency Driven Monitoring for data engineering teams include:

  • Lower cost of ownership by avoiding unnecessary monitoring on unneeded columns
  • Reduced alert noise with AI-driven anomaly detection only on the data in use
  • Increased business value by empowering business users to monitor what matters to them
  • Faster issue resolution with lineage-powered root cause and impact analysis

Bigeye is uniquely able to deliver Dependency Driven Monitoring for the enterprise. That's because we use Bigeye Lineage Plus—our unmatched lineage technology—to connect with over 50 modern and legacy data sources and automatically map each business-critical dependency at column-level precision through data sources, ETL stages, and even across the boundary from cloud to on-prem.

Ensuring every business user can trust their analytics

For those of us responsible for delivering analytics to business users, the story is all too familiar. No matter how many hours you spend perfecting a dashboard, a single data issue can send trust plummeting—especially if the dashboard user finds it before you do. According to Bigeye’s 2023 State of Data Quality Report, 70% of business leaders said they lack trust in analytics dashboards due to regular data quality incidents. 

While most enterprise data teams invest heavily in a variety of tools and processes to monitor data quality and data pipeline health, they still struggle to answer the data consumers’ age-old question, “Is the data in my dashboard reliable?”

That's because enterprise dashboards have a long list of dependencies that are simply too difficult to map and when a problem is discovered, it isn’t immediately obvious which table is at fault.

With Bigeye Dependency Driven Monitoring, you never have to let a "that data doesn't look right," comment ruin your day again.

Explore a complete lineage map of every column your dashboard depends on, see where and why data issues occur, and get a rolled-up data health summary right in your analytics dashboard.

The BI tool browser extension gives your dashboard users confidence in their analytics and provides a warning if something needs your attention.

Deploying monitoring on upstream dependencies

Once you've connected your dashboard or data product, Bigeye will create a complete dependency lineage map.

You can then use Bigeye Autometrics to deploy recommended data pipeline and data quality monitoring on every upstream column powering your data product. For BI tools like Tableau or Microsoft Power BI, you can choose to monitor an entire workbook or fine-tune coverage by deploying monitoring on a particular dashboard or story.

Autometrics includes out-of-the-box data quality monitoring for six data dimensions and will only deploy metrics relevant to the data in each column. If you have specific monitoring needs, the user-friendly wizard makes it simple to customize and configure which monitoring is deployed or create new custom metrics to meet your business needs.

Performing end-to-end root cause and impact analysis

If you're alerted to a data issue, Bigeye's lineage-powered root cause and impact analysis capabilities are available to help you quickly understand the downstream impact and identify the root cause for fast prioritization and resolution.

You can also dig into the details of each cross-source column relationship and even use automatically generated debug queries to see a preview of affected rows. 

Dashboard users will receive updates via the Bigeye BI Extension as soon as the issue is resolved, reducing the need for back-and-forth communication with the data team. 

Beyond analytics reliability

Dependency Driven Monitoring is changing the way enterprises drive value with data observability. Because Bigeye can monitor the dependencies upstream from any table or connected data source, teams can use Bigeye Dependency Driven Monitoring to ensure the health of ML models, data stores, AI projects, and more. 

☝️ Register for our webinar on April 10th to learn more!☝️

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Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
Data analyst
Business analyst
Data/product manager
Total cost
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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