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

Trusted data, without coding

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.

What Is DataBuck?

Data errors due to “Systems Risks” are the biggest contributors to untrustworthy data. As ETL jobs move data around, errors creep in and steadily multiply like cancer across an enterprise. For every step the error spreads, it takes 10x more cost and effort to fix it.

DataBuck is an autonomous data quality validation s/w. It automatically detects 100% of all Systems Risks with minimal human intervention using AI/ML. It automatically sets 1,000s of validation checks and their thresholds. 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

When is DataBuck powerful?

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

Cloud/Lake use
Dynamic data, for example, operational and transactional data
The high volume of data
New sources or changing structures of data
Data is being used for purposes it was not intended for when it was collected. (New uses for data)

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

Learn More About DataBuck Modules

Module 1

Observability

DataBuck’s unified Observability platform prevents critical data issues before it breaks the data pipeline or reaches the data users.

Powered by Machine Learning, DataBuck is an advanced Observability tool. It autonomously observes critical errors at all points in the data pipeline and alerts data engineers to control data flow.

Learn More

Module 2

Trustability

Beyond Observability:
ML-powered Data Trustability

Measure the trustworthiness and usability of data on the cloud, without IT intervention.

Monitor Data Trustability across the entire data pipeline with the help of DataBuck's Trustability Module.

Learn More

Module 3

Data Quality

Autonomous Data Quality Validation

DataBuck is an automated data monitoring and validation software.

Autonomously validate 1,000’s of data sets in a few clicks
10x in scaling Data Quality operations
Lower data maintenance work & cost

Learn More

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 Entrepreneur Magazine features FirstEigen

Published in Entrepreneur.com

Strategies for Achieving Data Quality in the Cloud

Adopting a strategy for achieving data quality in the cloud is essential for any business that relies on data. By considering the factors that contribute to data quality issues and putting processes and tools in place, you can ensure the highest-quality data, and your cloud data projects will have a greater chance of success.

Simplify Data Quality Monitoring and Data Quality Validation with DataBuck

Schedule a Free Demo

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.

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.

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

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

Get a Free Demo Today