Autonomous Data Quality Validation with DataBuck
Eliminate unexpected data issues
Why Monitor Data Quality?
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).
Challenges
Traditional data validation solutions:
- Were built for Data Stewards, not for Data Engineers, or Data Governance, or Data Management teams.
- They were built for writing rules for every table individually one by one, not built for automation or scaling.
- Are costly to scale and difficult to maintain.
Our approach to Data Quality
Autonomous Data Quality - powered by ML
DataBuck is an automated data validation and monitoring tool, powered by Machine Learning.
- Auto recommends the essential, baseline set of rules to validate any dataset.
- Write additional custom rules to top off the essential recommendations.
- Extremely secure- Moves the rules to where the data is and does not move the data.
- The business users and Data Stewards can edit data validation checks in a few clicks from the UI.
Benefits:
DataBuck is purpose built for the Cloud
Accomplish 2 man-years worth of validation in 8 hours
DataBuck has delivered these results for its customers:
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
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 organizations 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
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
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
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
A multinational bank operating in 10 different countries reduced its regulatory compliance risk by configuring DataBuck to improve the completeness and accuracy of KYC data.
Customer:
A multinational bank reduced its regulatory compliance risk by configuring DataBuck to improve the completeness and accuracy of KYC data.
Technology:
AWS, Teradata, Hive
Challenges:
- Mismatched views of the multi-country banking data flowing into the data lake daily.
- The KYC initiative was stalling as invalid or nonstandard contact information made the customers unreachable.
Outcomes:
- DataBuck auto-discovered relationships between different columns to alert against potentially mismatched data.
- Invalid data in KYC and contact-ability attributes of customer data were identified by applying advanced pattern matching rules
A life insurance and Annuities Company was able to save computation time by more than 67% while ensuring referential integrity, completeness of the data movement, and the logic of financial numbers with the help of DataBuck.
Customer:
A life insurance and Annuities Company was able to save computation time by more than 67% while ensuring referential integrity, completeness of the data movement, and the logic of financial numbers with the help of DataBuck
Technology:
AWS S3, Hive
Challenges:
- Detect data issues prior to posting the data in the financial data lake.
- Reduce the projected time to implement essential quality checks for 10% of the critical data assets (over 500).
Outcomes:
- DataBuck autonomously profiled and created 9-Point data quality checks for all the data that landed from the legacy systems to the AWS bucket.
- Created data matching rules to reconcile the data between AWS S3 bucket and Hive database.
- In less than 8 weeks, DataBuck was able to establish checks for over 500 data assets with the help of one resource.
A pharma company was receiving over 70 different sources of data from several healthcare providers. DataBuck’s out-of-the-box, pharma-specific capabilities solved their top-10 needs for pharmaceutical data validation.
Customer:
A pharma company was receiving over 70 different sources of data from several healthcare providers. DataBuck’s out-of-the-box, pharma-specific capabilities solved their top-10 needs for pharmaceutical data validation.
Technology:
AWS S3, Hive
Challenges:
- Traditional Data Quality validation solution was labor intensive.
- Not having sufficient resources to do any advanced quality checks
Outcomes:
- Two-Step data quality validation was implemented with DataBuck.
- DataBuck’s out-of-the-box, pharma-specific capabilities solved their top-10 needs for pharmaceutical data validation.
- IT team needs fewer resources as DQ was automated and streamlined.
Do your business partners question your credibility in providing error free data?
See how DataBuck Leverages AI/ML for Superior Data Validation
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.