DataTrust’s practical DataOps approach allows organizations to enable continuous development, continuous testing, and continuous deployment and monitor for their data integrations and analytics initiatives.
In a classic DevOps methodology, the need for testing/validations ends after deploying the code to production. Whereas for data integrations and analytics solutions using DataOps – there is a need for ongoing production data quality monitoring and control processes.
In the past, the driver for the data quality control process requirement used to be a regulatory or compliance requirement where the data platforms and the key datasources need a data quality certification.
But in the present day's digital enterprises any accuracy and integrity issues of the data platform can have a major implication on the overall business, sometimes can impact the bottom-line of the company.
A risk-based approach needs to be adopted to identify the quality control scenarios for the data platforms, data pipelines, and analytics assets. A transparent proactive approach is required to identify any product data quality exceptions with automated alerts and notifications. The platform owners should have real-time visibility into data quality outages of the various data assets with an ability to trace the status of the issue and drill-down capabilities.
DataTrust’s rich data quality dimensions dashboard provides clear visibility into real-time quality metrics of the analytics assets. It's slicing and dicing capabilities empower the data platform stakeholder the insights into the risk profile of the various issues, which data domain the most issue belong to etc.