EigenRules: #1 Software for Data Quality rules auto discovery directly from your data

Get the essential rules you MUST have in place given to you in plain English, DataBuck will accelerate SME’s work and reduce time to market for onboarding new data sources or apps.

Just Point to the Data and Out Come the Data Quality Rules

Point EigenRules to the data, wait a few minutes and out comes the auto discovered DQ rules in plain English! 

  • Run EigenRules from your Windows laptop as a utility
  • Give these auto discovered rules to your SME’s and they’ll be amazed!
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Amazing Software: Try it now!

For a limited time only, as a part of our new product introduction, try  EigenRules for free for 1month. It’s light weight with a small foot print. 99.9% of the users have fallen in love with it right away. If you don’t love it just delete it after your trial.

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Ask what ETL can do for you, and what EigenRules can do for you

  • Streamlines Data Quality rule discovery process
  • The SME’s can piggy back on the auto discovered DQ rules to accelerate their rule discovery
  • If you already use an ETL tool and you are writing rules, find gaps in your rule set
  • Augment what you already have with a thorough set of rules
  • Very quick “Time to Market”: for every data source you can cut 3-4 weeks of work to just 15 mins with only 1 resource to discover DQ rules including multi-columns relationships

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Want to Try This Amazing Software Now?

Auto discovered Data Quality rules will give you peace of mind that the essential rules are in place and it frees up the SME’s to focus their scarce time on the much more advanced rules very unique and specific to your company .

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Examples of types of Data Quality rules auto discovered

Every data set will have few 100’s of essential Data Quality rules that must be checked to validate data thoroughly. EigenRules will discover rules in all 6 Data Quality dimensions. Below are examples of the actual rules discovered for a loan data set and printed out by the software in plain English.  User gave ZERO inputs as to the meanings and relevance of the columns, EigenRules auto discovers reltionships and rules that govern every microsegment of data.

  • Uniqueness, Loan Number, Cannot be duplicate
  • Completeness, Loan Closing Date, Cannot be Null
  • Conformity, Loan Closing Date, Valid Format, yyyyMMdd
  • Validity, Inter Column Relationships , IF `Property State`=GA AND `Loan Source`=4 AND `Product Type`=1 THEN `Investor Type`=3
  • Drift, `Income Documentation`, Acceptable Values 1, 3, 4, 6
  • Timeliness, First_Payment_Date must be within 90 days of the Loan_Closing_Date
  • Consistency, Differences between Original_Credit_Score – Current-Credit_Score must have  Lower_Limit: -221.2, Upper_Limit:207.2
  • Accuracy, IF `Property State`=GA AND `Loan Source`=4 AND `Product Type`=1 AND `Investor Type`=3 Then `Unpaid Principal Balance` will have the following range:  Lower_Limit:0 Upper_Limit:260,853
  • Accuracy, IF `Property State`=CT AND `Loan Source`=2 AND `Product Type`=6 AND `Investor Type`=7 Then `Unpaid Principal Balance` will have the following range:  Lower_Limit:0 Upper_Limit:1,929,964
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