{"id":13315,"date":"2019-11-08T15:48:00","date_gmt":"2019-11-08T20:48:00","guid":{"rendered":"http:\/\/www.iri.com\/blog\/?p=13315"},"modified":"2024-05-20T14:10:07","modified_gmt":"2024-05-20T18:10:07","slug":"data-class-validation-workbench","status":"publish","type":"post","link":"https:\/\/www.iri.com\/blog\/iri\/iri-workbench\/data-class-validation-workbench\/","title":{"rendered":"Data Class Validation in IRI Workbench"},"content":{"rendered":"

Abstract: <\/i><\/b>This is the first of two articles on data class validation in the <\/span><\/i>IRI Workbench<\/span><\/i><\/a> graphical IDE for the IRI DarkShield, FieldShield and CellShield EE <\/span><\/i>data masking tools<\/span><\/i><\/a>. It provides an overview of IRI-provided data class validation scripts and how to use them in data classification and discovery. The <\/span><\/i>second article<\/span><\/i><\/a> shows how to create your own validation script for a custom data class you need to discover or modify. <\/span><\/i><\/i><\/p>\n

Beyond their ability to find data across disparate sources that match patterns, IRI Workbench users can access a large number of computational validation scripts \u2014 and\/or create their own \u2014 for use as <\/span>Data Matchers<\/span><\/a> in <\/span>data classification, discovery and masking<\/span><\/a> jobs. These \u2018canned\u2019 scripts have been added to applicable preloaded data classes and a library of common Regex patterns.\u00a0\u00a0<\/span><\/p>\n

Data class validation programs verify that a numeric value not only matches a Java Regular Expression (Regex) pattern, but conforms to one or more computational rules that define it. For example, you may find a 16-digit number in a pattern search, but only a computational examination of that number can validate whether it represents a valid credit card PAN.\u00a0\u00a0<\/span><\/p>\n

Why does their extra validation matter? Computational verification of values ensures that the data is clean, correct, and fit for purpose (data quality assessment). First-class analytics can only happen with <\/span>quality data<\/span><\/a>. And, in data security governance and data privacy law compliance contexts, it is necessary to find values that contain personally identifiable information (<\/span>PII<\/span><\/a>) while weeding out false positive values.\u00a0<\/span><\/p>\n

For these reasons, the provided validation scripts and customization facilities for verifying data class search results are key value-adds for users of the IRI FieldShield, DarkShield, or CellShield EE tools in the <\/span>IRI Data Protector Suite<\/span><\/a>. Ultimately, these validators could be used in almost any data discovery, extraction, masking, transformation, cleansing, or reporting job supported in the <\/span>IRI Voracity<\/span><\/a> platform.<\/span><\/p>\n

What Do These Validation Scripts Check?<\/b><\/h5>\n

Before the addition of these scripts, <\/span>IRI Workbench<\/span><\/a> data classes that matched on patterns did not have the flexibility to analyze the validity of the data that was matched upon, based only on patterns.\u00a0 Patterns have sufficed in the majority of cases since the structure of the data often had no mathematical logic behind it.<\/span><\/p>\n

However, data classes such as credit card numbers and national identification numbers require additional validation to ensure the integrity of the data. Common validation checks include:<\/span><\/p>\n