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Ever felt like you’re flying blind when it comes to your data pipelines? You’re not alone.
As enterprises grow, the complexity of their data systems skyrockets—and so does the risk of failure. Imagine being able to see every piece of your data puzzle, catch problems before they spiral out of control, and trust that your data is as reliable enough to inform smart business decisions.
That’s where data observability comes in.
We’ve developed a comprehensive whitepaper, Your Ultimate Guide to Data Observability, to help you understand the fundamentals and best practices for managing data health across your organization.
What’s Inside the Guide:
- Understanding Data Observability: Get a clear definition and learn how it differs from data monitoring, testing, and reliability.
- The Importance of Data Observability: Learn why observability is crucial for building trust in your data and driving better business outcomes.
- Key Benefits: Discover how data observability decreases downtime, reduces firefighting, and increases investment in your data strategy.
- Who Uses It: Explore how different roles within your organization—data engineers, analysts, and business users—benefit from data observability.
- Common Use Cases: See real-world examples of how observability supports accurate analytics, robust machine learning models, and smooth data migrations.
- Best Practices: Learn how to start implementing data observability in your own pipelines and develop clear ownership over pipeline segments.
This whitepaper is your all-in-one resource to start mastering data observability and ensuring that your data stays reliable and trusted across the organization.
Ready to Take Control of Your Data?
Download Your Complete Guide to Data Observability and start building a more reliable data infrastructure today.
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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
Kyle Kirwan
Get the Best of Data Leadership
Subscribe to the Data Leaders Digest for exclusive content on data reliability, observability, and leadership from top industry experts.
Want the practical playbook?
Join us on April 16 for The AI Trust Summit, a one-day virtual summit focused on the production blockers that keep enterprise AI from scaling: reliability, permissions, auditability, data readiness, and governance.
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