DataBuck: Empowering Data Quality
DataBuck is the ultimate solution for overcoming data challenges where traditional tools fall short:
- Cloud/Lake usage
- Dynamic data (e.g., operational and transactional)
- High data volumes
- New data sources or changing structures
- Unintended data use (data being used for new purposes)
With DataBuck's AI-powered data quality validation, all Systems Risks are automatically detected, setting up numerous checks and thresholds.
Experience its unmatched speed, surpassing other tools by over 10 times.
Customers love the benefits:
- Enhanced trust in reports, analytics, and models
- Reduced data maintenance workload and costs
- 10 times more efficiency in scaling Data Quality operations
Reduction in unexpected errors: 70%
Cost reduction >50%
Time reduction to onboard data set ~90%
Increase in processing speed >10x
Unlock the power of DataBuck and revolutionize your data management today!
4 Classes of Checks for Comprehensive Data Validation
DataBuck’s unified Observability platform:
Powered by Machine Learning, DataBuck is an advanced Observability tool that prevents critical data issues before it breaks the data pipeline or reaches the data users.
ML-powered Data Trustability
Measure the trustworthiness and usability of data on the cloud, and monitor Data Trustability across the entire data pipeline with the help of DataBuck's Trustability Module.
Autonomous Data Quality Validation:
DataBuck is an automated data monitoring and validation software that autonomously validates 1,000’s of data sets in a few clicks, with lower data maintenance work & costs.
Cross Platform Data Matching powered by ML:
Reconcile complex data across multiple platforms automatically using no-code Machine Learning to detect errors before they infect multiple systems downstream.
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
In The News Popular publications features FirstEigen
DataBuck customer, Roland Doummar, SVP, Head of Data Gov. First Abu Dhabi Bank, and his team were awarded the Best Use Case of Data Management for 2023 at the Middle East banking AI & Analytics summit #MEBANKINGAI.
Published in Entrepreneur.com
Strategies for Achieving Data Quality in the Cloud
With more and more applications moving to the cloud, the quality of data is becoming a growing concern. Erroneous data can cause all sorts of problems for businesses.
Published in Dataversity
How to Leverage Machine Learning to Identify Data Errors in a Data Lake
A data lake becomes a data swamp in the absence of comprehensive data quality validation and does not offer a clear link to value creation.