Autonomous Data Validation For Cloud and Lake
2018 IDC Innovator Award in Data Quality
2017 Gartner Cool Vendor Award
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.
Our Products & Services
DataBuck
Data inconsistencies and errors slowly creep in over time or as it moves between IT systems. FirstEigen’s DataBuck simplifies Data Quality error identification process from months to just 20mins. Powered by AI/ML algorithms it automatically learns data’s expected behavior and applies thousands of data validation checks without needing any coding. It also complements existing checks users may have in place, as it identifies additional unanticipated data quality errors from multiple data sets in just a few Clicks. It’s specialized in-memory, parallel processing algorithms deliver >10x speed over any other tool.
DataBuck is compatible with all major platforms (SQL/no-SQL/Hadoop/Cloud) and can elegantly handle both regular and Big Data.
Data Quality for Cloud
Data in the Cloud is notoriously error prone. DataBuck Cloud Edition is sepcially designed for Amazon AWS and Azure Cloud. It is a Cognitive Data Quality solution that self-learns data validation rules continuously and validates data for its business context and historical consistency w/o any programming or set up. It can also reconcile Cloud data with other sources. It can help you enforce constraints by flagging the bad data and sending out alerts to the right people. Cloud data validation can be operationalized in just a few clicks.
Predictive Analytics
Get actionable insights to your business from our team of former management consultants, corporate executives and data scientists. We make it hassle-free and you can use the predictive models long after we are gone.
Our Partners
Biggest cause of analytics failure…
Analytics is wonderful when it works, it “… returned an average of $13.01 for each dollar spent this year, that’s up from 2011, when analytics yielded returns averaging $10.66”
– ANALYTICS MARKET SURVEY, September 2014
If you can’t trust your data, you can’t trust your analytics or the decisions based on it. How can you win your customers hearts if you can’t know them because your data is incorrect? The hidden rot of inaccurate data will erode your brand promise and your customers will leave in droves.
Every business context is unique and the accuracy of your data has to be validated in your specific historical context. Accurate and reliable data validation checks have to use your specific historical trends and patterns to validate your data. In two decades of Data Quality consulting experience, we are yet to see a single organization that has been able to execute it reliably.
AI/ML-powered DataBuck self discovers Data Quality rules and patterns in your data and turns them into validation checks. Your analytics will be a lot more reliable and powerful within 6 weeks of using DataBuck to validate data.
Why Choose FirstEigen
Cognitive Data Quality for Cloud & Lake- Picked by Gartner and IDC as the best-of-breed in DQ solutions. Lessens work load by 80% on data stewards & SME’s for creating DQ rules
Get trusted data in a few clicks, w/o writing rules or coding
DQ Accelerators- reduce DQ effort from 9 months to just a few days
Scale DQ rule writing to 1000’s of data sets effortlessly using AI/ML
Testimonials
Great team to work with. Their can-do attitude is very infectious. The quality of work delivered is outstanding.
This is a group that you should definitely consider for your analytics needs. They were instrumental in making big contributions with work we did for Big Box US retailers.
I was mighty impressed with the expertise around management consulting and abilities in envisioning a complete solution that they brought to the table. With their help, we could deliver an excellent and implementable solution in a relatively time crunched environment. It was a great partnering experience for us.
How Machine Learning can minimize risk of erroneous data
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