Data Quality & Cleansing

Catch errors before they catch up with you

Having a bad hair day? It's no different than running into a bad dataset once a while. Bad data can range from missing key fields, wrong format, out of range data, and etc. You need a tool that can alert you on the discrepancies, capture the errors, and fix/redirect the mistakes.

CloverETL offers a comprehensive Data Quality package of tools that help you cope with data errors. With features such as data policies on Data Readers, Data Profiler, Validator, AddressDoctor and general data validation functions in CTL (Clover Transformation Language) you are able to test data consistency and filter or fix erroneous data.

Also, with the ProfilerProbe, a Data Profiler extension, you can gather and analyze data-related statistics. This powerful component allows you to pre-qualify data prior to execution as well as to provide a statistical overview of data processed.

   
Data Reader policy—All our data readers work with selectable data policy which affects how errors in the input data are treated. Different policies allow different reactions to data errors from strict reject over to controlled logging or completely lenient behavior.
Validator—Validator allows you to specify a set of rules using a powerful drag&drop visual designer and check the validity of incoming data based on these rules. Validation rules can check various criteria like date format, numeric value, interval match, phone number validity and format, etc.
Data Profiler—Let's you discover potential issues in your data. Calculates statistics such as ranges, averages or histograms for data profiling overview. Also offers trend analysis for TDQM setups.
Address Validation & Cleansing—Using Address Doctor to provide parsing of address details, verification and enrichment including geo coding.
Email Address Validation—Reads from input and parses e-mail addresses from specified fields.

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