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
Out-of-the-box AI/ML capabilities
Automated DQI Without Human Intervention
Simple to Integrate With Alation
Transition from a manual model to a trust-based data-driven approach With DataBuck
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.
What DataBuck users say…
“What took my team of 10 Engineers 2 years to do, DataBuck could complete it in <8 hrs”
- VP Technology, Enterprise Data Office, Major US bank
“DataBuck’s Data Quality automation does 80% of the heavy lifting for us with just 5% of the effort.”
- CIO of US Financial Services firm
“Streamlining the DQ monitoring and validation process w/DataBuck has reduced our time-to-market. With fewer resource we auto discover DQ rules, which also self-heals as the data evolves.”
- Head of Enterprise Data Quality Monitoring, Major US bank
“DataBuck can really add a lot of headcount efficiency for us. This tool makes it easy for us to not only profile and discover the rules, but also to operationalize them and auto-heal as the data evolves over time.”
- VP, Enterprise Information Management, Information Governance Leader, Insurance Company
“AML is on the rise. We have data from 10 countries in different formats and standards that need to be validated. We could not keep up doing it manually. DataBuck has automated and streamlined our data pipeline.”
- Sr. Exec. Technology Office, Top-3 African bank
“In the last 3 years we’ve had a 100x increase of API’s and microservices on the Cloud. This proliferation is beyond what Data Stewards can manage. As Cloud-native tool designed for Data Engineers, DataBuck autonomously validates data upstream and tremendously eases the burden on Stewards.”
- Sr. VP Data Mgmt and Analytics, US Investment Bank
“Monitoring and validating files and data at ingestion directly impacts our revenues. DataBuck gives us the reliability, intelligence and speed we need to eliminate revenue-leakage.”
- VP Technology, Enterprise Data Office, Telehealth provider
“Aggregating weekly sales data from many dozens of sources and validating them is laborious and error prone. With DataBuck’s AI/ML-driven DQ automation we got more accurate data with less than 10% effort.”
- Director, Commercial Data Operations, US pharmaceutical
“With the traditional Data Quality tools, we could not thoroughly audit the financial data for the Street w/in our audit window. DataBuck’s performance has reduced data validation times from 11 hrs to 2 hrs, and w/higher accuracy.”
- Director, IT – Data Strategy, Financial Planning, Fortune-50 Hi Tech manufacturer
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: