Data quality is not a problem any single role can solve in isolation. The complexity and scale of modern data ecosystems necessitate a collaborative approach, where quality testing serves as a shared infrastructure across all data and analytics roles.
Failing the SLA was the price we paid for trust. And it was worth every second.
Embracing a fundamentally different approach that prioritizes influence over enforcement, iteration over perfection, and advocacy over bureaucracy, that empowers you to take control of data quality assessments.
We introduce a new, more effective approach to data quality assessments, enabled by DataKitchen’s free open-source software
DataOps Data Quality TestGen:
Simple, Fast, Generative Data Quality Testing, Execution, and Scoring.
[Open Source, Enterprise]
DataOps Observability:
Monitor every date pipeline, from source to customer value, & find problems fast
[Open Source, Enterprise]
DataOps Automation:
Orchestrate and automate your data toolchain with few errors and a high rate of change.
[Enterprise]
DataOps Consulting, Coaching, and Transformation
Commercial Data & Analytics Platform for Pharma
Data Production Teams
Data Science/AI
Data Engineering
Data Quality
Business Analytics
Data Products
Data Mesh
Data Contracts
ModelOps / MLOps
DataGovOps
Self-Service Operations
Data Quality Assessments
Data Quality Testing
Data Observability
Data Orchestration
Monitor every Data Journey in an enterprise, from source to customer value, in development and production.
Simple, Fast Data Quality Test Generation and Execution. Your Data Journey starts with verifying that you can trust your data.
Orchestrate and automate your data toolchain to deliver insight with few errors and a high rate of change.
