{"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":"2020-01-28T09:49:47","modified_gmt":"2020-01-28T14:49:47","slug":"data-class-validation-workbench","status":"publish","type":"post","link":"https:\/\/beta.iri.com\/blog\/iri\/iri-workbench\/data-class-validation-workbench\/","title":{"rendered":"Data Class Validation in IRI Workbench"},"content":{"rendered":"<p><i><span style=\"font-weight: 400;\">This is the first of a two-part blog series detailing data class validation in <\/span><\/i><a href=\"https:\/\/www.iri.com\/products\/workbench\"><i><span style=\"font-weight: 400;\">IRI Workbench<\/span><\/i><\/a><i><span style=\"font-weight: 400;\">. This article provides an overview of our provided validation scripts and how to use them in a data discovery or classification job. The <a href=\"https:\/\/www.iri.com\/blog\/iri\/iri-workbench\/data-class-validator-workbench\/\">second article<\/a> shows how to create a validation script for a custom data class or group.\u00a0<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">Beyond their ability to find data across disparate sources that match patterns, <\/span><span style=\"font-weight: 400;\">IRI Workbench<\/span><span style=\"font-weight: 400;\"> users now have access to a large number of validation scripts &#8212; and the ability to create their own &#8212; for use in <\/span><a href=\"https:\/\/www.iri.com\/products\/workbench\/discover-data\"><span style=\"font-weight: 400;\">data classification and discovery<\/span><\/a><span style=\"font-weight: 400;\">. The \u2018canned\u2019 scripts have been added to all applicable preloaded data classes and common library of Regular Expression patterns.\u00a0\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Why does this extra validation matter? Computational verification of values ensure the data is clean, correct and fit-for-purpose. First class analytics can only happen with <\/span><a href=\"https:\/\/www.iri.com\/solutions\/data-integration\/implement\/data-quality\"><span style=\"font-weight: 400;\">quality data<\/span><\/a><span style=\"font-weight: 400;\">. And, in data security governance and privacy law compliance contexts, it is necessary for finding values that contain personally<\/span><span style=\"font-weight: 400;\"> identifiable information (<\/span><a href=\"https:\/\/www.iri.com\/solutions\/data-masking\"><span style=\"font-weight: 400;\">PII<\/span><\/a><span style=\"font-weight: 400;\">) and weeding out otherwise false positives.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For these reasons, the provided scripts and customization facilities for validating data class search results are key value-adds for users of the IRI FieldShield, DarkShield, or CellShield EE tools in the <\/span><a href=\"https:\/\/www.iri.com\/products\/iri-data-protector\"><span style=\"font-weight: 400;\">IRI Data Protector Suite<\/span><\/a><span style=\"font-weight: 400;\">. Ultimately, these validators could be used in almost any data cataloging, masking, transformation or reporting job supported in the <\/span><a href=\"https:\/\/www.iri.com\/products\/voracity\"><span style=\"font-weight: 400;\">IRI Voracity<\/span><\/a><span style=\"font-weight: 400;\"> platform.<\/span><\/p>\n<h4><b>What do these validation scripts check?<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">Prior to the addition of these scripts, <\/span><a href=\"http:\/\/www.iri.com\/products\/workbench\/voracity-gui\"><span style=\"font-weight: 400;\">IRI Workbench<\/span><\/a><span style=\"font-weight: 400;\"> preloaded data classes only matched data against a specified pattern (though also enabled enhancement through fuzzy and lookup value . matches and\/or NER models). Patterns have in fact sufficed in the majority of cases since the structure of the data often had no mathematical logic behind it.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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<ul>\n<li><b>Checksum <\/b><span style=\"font-weight: 400;\">&#8211; A <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Checksum\"><span style=\"font-weight: 400;\">checksum<\/span><\/a><span style=\"font-weight: 400;\"> is designed to detect errors during transmission and storage of the data. It is calculated by running the data through a mathematical algorithm and often appended to the end of a string of data.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\"><b>D.O.B.<\/b><span style=\"font-weight: 400;\"> &#8211;\u00a0 Data might have a person&#8217;s date of birth encoded within the number itself. A simple check to verify the date is often needed.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\"><b>Other &#8211; <span style=\"font-weight: 400;\">National Identification numbers (NID) often have regional information encoded within the number. Various tests have already been provided to ensure a NID is correct.\u00a0<\/span><\/b><\/li>\n<\/ul>\n<h4><b>Viewing Provided Data Classes and Validation Scripts<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">The <\/span><a href=\"http:\/\/www.iri.com\/products\/workbench\/voracity-gui\"><span style=\"font-weight: 400;\">IRI Workbench<\/span><\/a><span style=\"font-weight: 400;\"> validation scripts can be viewed through the IRI preferences screen. To open preferences, select the <\/span><i><span style=\"font-weight: 400;\">IRI Menu <\/span><\/i><span style=\"font-weight: 400;\"> dropdown and select <\/span><i><span style=\"font-weight: 400;\">IRI Preferences<\/span><\/i><span style=\"font-weight: 400;\">. Then select the dropdown for <\/span><i><span style=\"font-weight: 400;\">IRI\u00a0<\/span><\/i><span style=\"font-weight: 400;\">(within the preferences window) and select <\/span><i><span style=\"font-weight: 400;\">Data Classes and Groups.<\/span><\/i><\/p>\n<p><a href=\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img1.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13320 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img1-1024x584.png\" alt=\"\" width=\"649\" height=\"370\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img1-1024x584.png 1024w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img1-300x171.png 300w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img1-768x438.png 768w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img1.png 1174w\" sizes=\"(max-width: 649px) 100vw, 649px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">To reset the originally provided data classes if any changes were made, click <em>Restore Defaults<\/em>. To check if a data class has a provided script, you can select a data class and select <\/span><i><span style=\"font-weight: 400;\">Edit. <\/span><\/i><span style=\"font-weight: 400;\">This will bring up the <\/span><i><span style=\"font-weight: 400;\">Data Class or Group Edit <\/span><\/i><span style=\"font-weight: 400;\">window. I will use the\u00a0 <\/span><i><span style=\"font-weight: 400;\">CREDIT_CARD <\/span><\/i><span style=\"font-weight: 400;\">data class for this example.\u00a0<\/span><\/p>\n<p><a href=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img2.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13321 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img2.png\" alt=\"\" width=\"650\" height=\"453\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img2.png 678w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img2-300x209.png 300w\" sizes=\"(max-width: 650px) 100vw, 650px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">By selecting the associated matcher field and selecting <\/span><i><span style=\"font-weight: 400;\">Edit<\/span><\/i><span style=\"font-weight: 400;\">, a <\/span><i><span style=\"font-weight: 400;\">Data Class Matcher <\/span><\/i><span style=\"font-weight: 400;\">window will be displayed.\u00a0<\/span><\/p>\n<p><a href=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img3.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13322 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img3.png\" alt=\"\" width=\"649\" height=\"417\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img3.png 683w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img3-300x193.png 300w\" sizes=\"(max-width: 649px) 100vw, 649px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">This window details the specific pattern for a class matcher and a validator script path (if one exists). It should be noted that not all provided data classes will have a validator script. For example, the data class <\/span><i><span style=\"font-weight: 400;\">FIRST_NAME<\/span><\/i><span style=\"font-weight: 400;\"> doesn\u2019t require any validation besides the given pattern.\u00a0<\/span><\/p>\n<h4><b>How do you use these validation scripts?<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">Using these scripts is easy. They are already added to all applicable preloaded data classes, so it is just a matter of choosing the right data class for the job. We have also added over 45 new data classes supporting various national identification numbers.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, users of the IRI Workbench will be able to utilize our preloaded data classes to classify data like:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Australian Tax File Number (TFN)<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Turkish Public Identification number (T.C Kimlik No)<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">China\u2019s Identity Card Number<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Latvian Personal Code (Personas Kods)<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Unique Master Citizen Number<\/span><\/li>\n<\/ul>\n<h4><b>Example: Validation via the Dark Data Discovery Wizard<\/b><\/h4>\n<p><span style=\"font-weight: 400;\">This example uses some elements of <\/span><i><span style=\"font-weight: 400;\">Dark Data Discovery. <\/span><\/i><span style=\"font-weight: 400;\">The general idea is that, after parsing through data in unstructured sources, you can output what you\u2019re looking for in a structured text (flat) file, with its layouts automatically defined in a data definition file (.DDF).<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">While this example provides a brief introduction to dark data discovery, you may find it useful to read <\/span><a href=\"https:\/\/www.iri.com\/blog\/migration\/data-migration\/finding-dark-data-unstructured-sources\/\"><span style=\"font-weight: 400;\">this<\/span><\/a><span style=\"font-weight: 400;\"> three part blog series that explores the feature in depth.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For this tutorial, I created a JSON file filled with ten fake credit card entries. Since credit card numbers use a checksum for validation, five of these numbers will have a valid checksum and pattern while the other five will just have a valid pattern.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The goal is to use the <\/span><i><span style=\"font-weight: 400;\">Dark Data Discovery <\/span><\/i><span style=\"font-weight: 400;\">wizard to create a search job that will return only the five fully validated credit card numbers.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For reference, this is how the JSON file is formatted:\u00a0<\/span><\/p>\n<p><a href=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img4.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13323 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img4.png\" alt=\"\" width=\"649\" height=\"330\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img4.png 836w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img4-300x153.png 300w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img4-768x390.png 768w\" sizes=\"(max-width: 649px) 100vw, 649px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">To access the <\/span><i><span style=\"font-weight: 400;\">Dark Data Discovery wizard<\/span><\/i><span style=\"font-weight: 400;\">, select the DarkShield\u00a0<\/span><span style=\"font-weight: 400;\">dropdown menu from the IRI Workbench toolbar and select <\/span><i><span style=\"font-weight: 400;\">Dark Data Discovery Job<\/span><\/i><span style=\"font-weight: 400;\">. The wizard will open this way:<\/span><\/p>\n<p><a href=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img5.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13324 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img5.png\" alt=\"\" width=\"650\" height=\"468\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img5.png 679w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img5-300x216.png 300w\" sizes=\"(max-width: 650px) 100vw, 650px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">From the setup page, specify the folder and file names for the structured output file and the data definition file (DDF) metadata for that file.\u00a0<\/span><\/p>\n<p><a href=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img6.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13325 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img6.png\" alt=\"\" width=\"650\" height=\"473\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img6.png 684w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img6-300x218.png 300w\" sizes=\"(max-width: 650px) 100vw, 650px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Select the source URI that contains the JSON file and select <\/span><i><span style=\"font-weight: 400;\">Next. <\/span><\/i><span style=\"font-weight: 400;\">The next window will ask you to specify data targets for the remediation (data masking) jobs. Since no remediation is being performed, we can just skip this part by selecting <\/span><i><span style=\"font-weight: 400;\">Next <\/span><\/i><span style=\"font-weight: 400;\">again.<\/span><\/p>\n<p><a href=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img7.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13326 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img7.png\" alt=\"\" width=\"650\" height=\"476\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img7.png 679w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img7-300x220.png 300w\" sizes=\"(max-width: 650px) 100vw, 650px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">From here, you can profile several different forensic aspects &#8212; file metadata attributes &#8212; of the dark data you\u2019re discovering. The wizard can identify and display the creation, modification, and access dates of the data source, as well as its full path, owner, linkage, and hidden attributes.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For the purposes of this tutorial, you can check every box. The next window will prompt us to add or remove search matchers.<\/span><\/p>\n<p><a href=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img8.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13327 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img8.png\" alt=\"\" width=\"651\" height=\"478\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img8.png 673w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img8-300x220.png 300w\" sizes=\"(max-width: 651px) 100vw, 651px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">The next window prompts us to add or remove search matchers. Select <\/span><i><span style=\"font-weight: 400;\">Add<\/span><\/i><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><a href=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img9.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13328 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img9.png\" alt=\"\" width=\"650\" height=\"546\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img9.png 681w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img9-300x252.png 300w\" sizes=\"(max-width: 650px) 100vw, 650px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Since we already have a preloaded CREDIT_CARD data class, you can just select <\/span><i><span style=\"font-weight: 400;\">Browse<\/span><\/i><span style=\"font-weight: 400;\"> and point it to the associated class. For the <\/span><i><span style=\"font-weight: 400;\">Rule Name<\/span><\/i><span style=\"font-weight: 400;\"> field of the <\/span><i><span style=\"font-weight: 400;\">CreditCardMatcher<\/span><\/i><span style=\"font-weight: 400;\">, we can create a rule to use with our matcher.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Select <\/span><i><span style=\"font-weight: 400;\">Create <\/span><\/i><span style=\"font-weight: 400;\">and create a redaction rule using the default values. Once you have finished creating the matcher, you can click <\/span><i><span style=\"font-weight: 400;\">finish <\/span><\/i><span style=\"font-weight: 400;\">to generate a <\/span><i><span style=\"font-weight: 400;\">.search<\/span><\/i><span style=\"font-weight: 400;\"> file.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Completing the <\/span><i><span style=\"font-weight: 400;\">Dark Data Discovery Job<\/span><\/i><span style=\"font-weight: 400;\"> wizard generates a new <\/span><i><span style=\"font-weight: 400;\">.search <\/span><\/i><span style=\"font-weight: 400;\">configuration file. That file will contain the options we selected, including the source and target of our data, and the Search Matches used to tag PII for discovery, delivery, deletion, and\/or de-identification.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To begin the search, right click on the <\/span><i><span style=\"font-weight: 400;\">.search <\/span><\/i><span style=\"font-weight: 400;\">file, select <\/span><i><span style=\"font-weight: 400;\">Run As, <\/span><\/i><span style=\"font-weight: 400;\">and choose the <\/span><i><span style=\"font-weight: 400;\">IRI Search Job.<\/span><\/i><\/p>\n<p><a href=\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img10.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13329 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img10-1024x618.png\" alt=\"\" width=\"650\" height=\"392\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img10-1024x618.png 1024w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img10-300x181.png 300w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img10-768x464.png 768w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img10.png 1302w\" sizes=\"(max-width: 650px) 100vw, 650px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Selecting <\/span><i><span style=\"font-weight: 400;\">Search Job<\/span><\/i><span style=\"font-weight: 400;\"> will only conduct a search, while <\/span><i><span style=\"font-weight: 400;\">Search and Remediate Job<\/span><\/i><span style=\"font-weight: 400;\"> will also attempt to mask (or delete) any identified data. Both will generate a <\/span><i><span style=\"font-weight: 400;\">.darkdata<\/span><\/i><span style=\"font-weight: 400;\"> file identifying any data of interest.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It should be noted that, when handling actually sensitive information, users should ensure that the <\/span><i><span style=\"font-weight: 400;\">.darkdata<\/span><\/i><span style=\"font-weight: 400;\"> file is not exposed and is safely archived or deleted after the completion of the remediation to prevent PII leakage. IRI is adding a quarantine option for storing the <\/span><i><span style=\"font-weight: 400;\">.darkdata<\/span><\/i><span style=\"font-weight: 400;\"> file and corresponding search artifacts in a safe location; contact <\/span><span style=\"font-weight: 400;\">darkshield@iri.com<\/span><span style=\"font-weight: 400;\"> for details.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">From the generated <\/span><i><span style=\"font-weight: 400;\">new_search.txt<\/span><\/i><span style=\"font-weight: 400;\"> file, we can see that the search only returned the credit card numbers that fully passed validation.\u00a0<\/span><\/p>\n<p><a href=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img11.png\"><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-13330 aligncenter\" src=\"\/blog\/wp-content\/uploads\/2019\/11\/part1-img11.png\" alt=\"\" width=\"677\" height=\"131\" srcset=\"https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img11.png 791w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img11-300x58.png 300w, https:\/\/beta.iri.com\/blog\/wp-content\/uploads\/2019\/11\/part1-img11-768x149.png 768w\" sizes=\"(max-width: 677px) 100vw, 677px\" \/><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This is the first of a two-part blog series detailing data class validation in IRI Workbench. This article provides an overview of our provided validation scripts and how to use them in a data discovery or classification job. The second article shows how to create a validation script for a custom data class or group.\u00a0<\/p>\n<div><a class=\"btn-filled btn\" href=\"https:\/\/beta.iri.com\/blog\/iri\/iri-workbench\/data-class-validation-workbench\/\" title=\"Data Class Validation in IRI Workbench\">Read More<\/a><\/div>\n","protected":false},"author":122,"featured_media":13322,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"categories":[8,363,91],"tags":[610,1386,1304,280,14,823,561,1112,1388,520,850,149],"class_list":["post-13315","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-protection","category-data-quality","category-iri-workbench","tag-dark-data","tag-darkshield","tag-data-class","tag-data-discovery","tag-data-masking","tag-data-wizard","tag-ddf","tag-discover-metadata","tag-iri-darkshield","tag-iri-fieldshield","tag-iri-workbench","tag-pii"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.3 - 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