Turbo Charge Data Catalog with Data Trust Score

Superior Data Governance by Integrating Data Catalog With ML-Based Data Quality (DQ)

Data Catalog Platform Integrated with Data Quality

Trust and Data Quality are critical to making the most efficient use of data and data governance platforms. It is vital to measure and communicate data quality to ensure that stakeholders are making decisions based on good information.

DataBuck enables Catalog users (Alation, Zeenea, Octopai, Informatica, IBM, Ataccama, and many others) to autonomously evaluate data quality, calculate a trust score for their data assets (“DQI”), and display the results in the data catalog.

Watch How DataBuck Turbo Charge Alation Data Catalog

Reduction in unexpected errors

Out-of-the-box AI/ML capabilities

People Productivity

Automated DQI Without Human Intervention

Increase in processing speed

Simple to Integrate With Alation

Transition from a manual model to a trust-based data-driven approach With DataBuck

First Eigen

How does The Data Quality Platform work?

Scan: DataBuck scans each data asset registered in your Data Governance Platform.

Auto Discover Metrics- DataBuck autonomously creates data health metrics specific for each data asset. The well-accepted and standardized DQ tests are customized for each data set individually, leveraging AI/ML algorithms.

Monitor- Health metrics are then translated to a data trust score. Health metrics are computed based on quality dimensions for each column in the data asset.

Alert: DataBuck continuously monitors the health check metrics and trust score and alerts users when the trust score becomes unacceptable.

Autonomous Data Trust Score for Data Catalogs

Incorporating Data Trust Score into the Data Catalog Challenging? More insights in this White Paper

The deviation of the trust score displayed in the summary of analysis results shows how the quality score changed between the last two analyses. Every violation discovered can be double-clicked for further information:

  •  Expand the dimension to see which columns are affected at the data asset level. Click a column name to see the dimension details for that column.
  • At the column level, click the dimension name for further details.

Users can then decide for each Data Quality violation to be ignored or evaluated during the analysis. Users can choose for the entire data asset or individual columns.

First Eigen

What DataBuck users say…

Introduction DQ Monitoring on AWS
FirstEigen recognized in AWS re:Invent as best-of-breed DQ tool
Autonomous Data Quality validation on Cloud
How AI/ML simplifies Data Quality and increases accuracy

Friday Open House

Our development team will be available every Friday from 12:00 - 1:00 PM PT/3:00 - 4:00 PM ET. Drop by and say "Hi" to us! Click the button below for the Zoom Link: