Our data observability platform
Kensu monitors the end-to-end quality of data usage in real-time so your team can easily prevent data incidents.
Understand your data usage
It is more important to understand what you do with your data than the data itself.
Holistic
data mapping
Analyse data quality and lineage through a single comprehensive view.
Observability
dashboard
Get real-time insights about data usage across all your systems, projects and applications.
End-to-end
data monitoring
Monitor data flows instead of the ever-increasing number of repositories.
Ecosystem
enrichment
Share lineages, schemas and quality info with catalogs, glossaries and incident management systems.
Troubleshoot issues
At a glance, find the root causes of complex data issues to prevent any "datastrophes" from propagating.
Contextual
notifications
Generate notifications about specific data events and their context.
Traced data
manipulation
Understand how data has been collected, copied and modified by any application.
Detection of
AI-based anomalies
Detect anomalies based on historical data information.
Root cause
analyses
Leverage lineage and historical data information to find the initial cause.
"Datastrophe" prevention
Make sure the quality of your data remains consistent at all times across your projects and applications.
Customizable
rules engine
Define rules and thresholds to anticipate data issues.
Data quality
objectives
Set data quality objectives for your applications and projects.
Propagation
prevention
Mitigate the impact of dependencies between teams.
Run-time
integration
Enable data quality monitoring within your application code.
We love your stack ...

... and we've got your back
Security
Because security matters to us and to our clients, we are SOC 2 compliant.
On-premise
We provide an on-premise solution for corporations with specific needs.
Cloud
You can benefit from all our features with a highly secured cloud solution.
Discover our data observability method
The “Data Observability Driven Development” method is a paradigm shift that allows data teams and data usage to scale efficiently.
Data observability is done from within the applications instead of outside to enable your data projects with continuous validation, contextual observability, avoidance of work duplication, and iterative implementation.
Our use cases
Less documentation.
Stronger governance
& productivity.
Protect sensitive and private data
Accelerate your time to market
Create a protective shell around your data applications
Trust what you deliver.
Do you have any questions regarding
data quality management or data observability?