When do I need a data observability platform?
If the consequences of data issues are high
If you are running performance marketing budgets of $millions, a data issue can result in a loss of hundreds of thousands of dollars. In these cases, the ability to detect and resolve issues fast is business-critical. It typically involves multiple teams and the ability to measure, track, and report on data quality.
If data is scaling faster than the data team
The scale and complexity of modern data environments make it impossible for teams to manually manage quality without expanding the team. A data observability platform enables automation and collaboration, ensuring data quality is maintained as data continues to grow, without impacting team efficiency.
Common use cases
If your data is being used in one of the following use cases, you should consider adding a data observability platform:
- Self-service analytics
- Data activation
- Powering AI & ML products
- Embedded analytics
- Performance marketing
- Regulatory reporting
- A/B testing and experiments
Why isn’t the open-source package enough?
The open-source package was designed for engineers that want to monitor their dbt project. The Cloud Platform was designed to support the complex, multifaceted requirements of larger teams and organizations, providing a holistic observability solution.