Data error is data-cancer. Hard to detect and spreads fast Detect data errors early and autonomously with AI/ML

Stop the spread. Stop the damage

Autonomous Data Monitoring for Cloud and Lake

Data Warehouses, Lakes and Clouds are notoriously error prone. The process of validating their Data Quality (DQ) is laborious manual work and is far from satisfactory. Unfortunately, >95% of your data is dark data – unmonitored, unvalidated and unreliable. Every step it moves downstream, errors get exponentially compounded. It takes 10x cost to fix it, if you can detect it all. What you don’t detect is unmitigated business risk.

When DataBuck?

Current data monitoring and validation tools and processes fare very poorly under these conditions. DataBuck is perfect for these:

Cloud/Lake use
Dynamic data, example, Operational and transactional data
High volumeof data
New sourcesor changing structures of data
New uses for data (unvalidated data for analytics, customer insights, etc.)

What Is DataBuck?

DataBuck is an autonomous data monitoring software. It catches unreliable data early and automatically without any coding or rules writing, by sensing intricate changes in the “DNA Fingerprint” of data with AI/ML. It automates over 80% of the laborious work for data monitoring and validation.

DataBuck autonomously sets 1,000s of validation checks w/o manual intervention. It is >10x faster than any other tool or your own custom scripts. AI/ML enables the tool to be set up and validate entire databases or schemas in just a few clicks.

Customer’s report benefits of:

(i) Higher trust in reports, analytics & models
(ii) Lower data maintenance work & cost
(iii) 10x efficiency in scaling Data Quality ops

People productivity
boost >80%

Reduction in unexpected errors: 70%

Cost reduction >50%

Time reduction to onboard data set ~90%

Increase in processing speed >10x

Cloud native

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

IDC Innovator Award in
Data Quality

AI/ML-powered Data Quality validation tool, DataBuck, was recognized by IDC for innovative solution in the DQ space. DataBuck’s AI/ML-powered business rule discovery automates a tedious, labor intensive and time consuming process for validating Data Quality from 6-9 months to just a few days.

Gartner Cool Vendor Award for Data Validation & Data Quality

An innovative tool that Gartner has recognized has the potential to change the big Data landscape. Big Data Quality Validation and Data Matching is done autonomously and effortlessly by Machine Learning algorithms that learn the expected behavior of data without manual intervention. Without coding, Data Quality deviations are picked out.

Machine Learning-Guided Cloud and Lake Data Monitoring and Validation Tool

DataBuck can validate your Big Data autonomously and at 10x speed of any other data validation tool or your own custom scripts. Clean data in 3 clicks and no-coding.