Catch data errors before your business partners do Autonomously monitor any metric you want; ML will recommend what else you must

Eliminate your blind spots

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Why Monitor data

Error creeps into data every step along the way as it makes it way to the business and analytics team for usage and insights. Companies are seeing an increase in data volume, data complexity, number of data sources and number of platforms (Lake, Cloud, Cloud Warehouses, Hadoop).

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Challenge

Traditional data validation solutions

- Were built for Data Stewards and not for Data Engineers
- They were built for individually writing rules for every table one by one, not built for automation
- Are costly to scale and are difficult to manage

Solution

Catch bad data NOT by writing laborious rules, BUT by sensing changes in the DNA of data using AI/ML.

Benefit

Catch data errors before your business partners do.

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

DataBuck is not merely a software, adopting it sends a message – build reliability by design

DataBuck is NOT

- BI tool
- ETL tool
- Data wrangling tool

DataBuck IS AN automated data monitoring and validation software

DataBuck has delivered these results for its customers:

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Top-3 US Bank with Over $1.5 Trillion in assets, reduced operational and regulatory reporting risk by leveraging DataBuck to monitor 15,000+ data assets.

Customer: Top-3 US Bank with Over $1.5 Trillion in assets, reduces operational and regulatory reporting risk by leveraging DataBuck to monitor 15,000+ data assets.

Technology: MapR Data Lake, Teradata, Exadata

Challenges:
- Lack of scalable solution to monitor 15,000+ data assets
- Lack of SMEs to define appropriate Data Quality Rules
- Lack of Resources to code and update Data Quality Rules

Outcomes:
- Deployed auto-discovered data quality rules in less than 90 days for 3000+ data assets using 4 resources
- Detected several unexpected data issues within 30 days of production

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Top 3 global Networking Equipment provider reduced financial reporting risk by leveraging DataBuck to monitor 800+ data assets in its financial data warehouse and reconciling financial information.

Customer: Top 3 global Networking Equipment provider reduces financial reporting risk by leveraging DataBuck to monitor 800+ data assets in its financial data warehouse and reconciling financial information.

Technology: Teradata, Postgress, Snowflake

Challenges:
- Lack of scalable solution to validate and monitor 800+ data assets within the nightly processing window
- Financial organization were surfacing data errors undetected by the technical team

Outcomes:
- Deployed auto-discovered data quality rules in less than 30 days for 800 data assets using 2 resources
- Detected several unexpected data errors that would have impacted financial reports
- DataBuck reduced data validation time from 11 hrs to less than 2 hrs

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Top-3 Bank in Africa reduces financial crime risk by leveraging DataBuck to monitor client data spanning over 300 data assets in its Data Lake.

Customer: Top-3 Bank in Africa reduces financial crime risk by leveraging DataBuck to monitor client data spanning over 300 data assets in its Data Lake.

Technology: Cloudera Data Lake, S3, Teradata, MSSQL

Challenges:
- Lack of SMEs to define comprehensive and effective Data Quality Rules
- Lack of Resources to code and update Data Quality Rules

Outcomes:
- Deployed auto-discovered data quality rules in less than 30 days for 300+ data assets using 1 resource
- Detected several customer contactability issues within 7 days of production

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Top 3 Telemedicine and Healthcare company reduces data risk and transforms its data pipeline by leveraging DataBuck to monitor eligibility files received from 250+ Hospitals comprising of millions of records in real time.

Customer: Top 3 Telemedicine and Healthcare company reduces data risk and transforms its data pipeline by leveraging DataBuck to monitor eligibility files received from 250+ Hospitals comprising of millions of records in real time

Technology: AWS, S3, Redshift, mySQL

Challenges:
- Lack of SMEs to define comprehensive and effective Data Quality Rules
- Lack of Resources to code and update Data Quality Rules

Outcomes:
- Deployed auto-discovered data quality rules in less than 15 days for 500+ data assets using 2 resources
- Detected several unexpected data issues within 7 days of production

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Leading media streaming company reduces revenue risk by leveraging DataBuck to monitor customer, prospect, and account data in near real time.

Customer: Leading media streaming company reduces revenue risk by leveraging DataBuck to monitor customer, prospect, and account data in near real time

Technology: AWS, Salesforce, S3

Challenges:
- Lack of SMEs to define comprehensive and effective Data Quality Rules
- Lack of Resources to code and update Data Quality Rules

Outcomes:
- Deployed auto-discovered data quality rules in less than 7 days for 20+ data assets using 1 resource
- Detected 3% of invalid data used for billing

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What Data Sources Can it Work With:

DataBuck can accept data from all major data sources, including Hadoop, Cloudera, Hortonworks, MapR, HBase, Hive, MongoDB, Cassandra, Datastax, HP Vertica, Teradata, Oracle, MySQL, MS SQL, SAP, Amazon AWS, MS Azure, and more.

How AI/ML simplifies Data Monitoring and Data Quality Validation