Product
-
April 10, 2024
Ensuring Reliable Analytics with Bigeye Dependency Driven Monitoring | Webinar Replay
Catch the replay to learn how Bigeye Dependency Driven Monitoring helps data teams!
.png)
Despite the ever-growing need to be data-driven, enterprise decision makers are still constantly disrupted by broken analytics dashboards caused by unseen upstream data issues. In this webinar, we demonstrate how Dependency Driven Monitoring uniquely solves this challenge by allowing data teams to map every single column powering an analytics dashboard—even across modern and legacy sources—deploy AI-driven monitoring on them, and use high-fidelity lineage to find and solve data issues anywhere in the analytics pipeline.
Watch to learn how Bigeye Dependency Driven Monitoring helps data teams:
- Reduce data observability spend and overhead by monitoring every column that matters, and none that don’t
- Improve business user confidence in analytics with Bigeye data reliability insights delivered right in BI dashboards
- Identify and solve data issues faster with end-to-end, cross-column lineage across modern and legacy sources
- Give data analysts a complete map of every column powering their dashboard and their owners
share this episode
Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
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
Monitoring
Schema change detection
Lineage monitoring
Monitoring
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
.png)