{"id":1157,"date":"2019-07-17T13:24:36","date_gmt":"2019-07-17T20:24:36","guid":{"rendered":"https:\/\/www.lightsondata.com\/?p=1157"},"modified":"2019-10-16T14:59:57","modified_gmt":"2019-10-16T21:59:57","slug":"3-key-data-integrity-testing-strategies-for-dw-bi-systems","status":"publish","type":"post","link":"https:\/\/www.lightsondata.com\/3-key-data-integrity-testing-strategies-for-dw-bi-systems\/","title":{"rendered":"3 key data integrity testing strategies for DW\/ BI systems"},"content":{"rendered":"<p>Data warehousing and business intelligence users assume, and need, trustworthy data.In the <a href=\"https:\/\/www.gartner.com\/it-glossary\/\" target=\"_blank\" rel=\"noopener noreferrer\">Gartner Group\u2019s Online IT Glossary<\/a>, data integrity and data integrity testing are defined as follows:<\/p>\n<p style=\"padding-left: 40px;\"><strong>Data Integrity:<\/strong> the quality of the data residing in data repositories and database objects. The measurement which users consider when analyzing the value and reliability of the data.<\/p>\n<p style=\"padding-left: 40px;\"><strong>Data Integrity Testing:<\/strong> verification that moved, copied, derived, and converted data is accurate and functions correctly within a single subsystem or application.<\/p>\n<p>Data integrity processes should not only help you understand a project\u2019s data integrity, but also help you gain and maintain the accuracy and consistency of data over its lifecycle. This includes data management best practices such as preventing data from being altered each time it is copied or moved. Processes should be established to maintain DW\/ BI data integrity at all times. Data, in its final state, is the driving force behind industry decision making. Errors with data integrity commonly arise from human error, noncompliant operating procedures, errors in data transfers, software defects, compromised hardware, and physical compromise to devices.This article provides a focus on DW\/ BI \u201cdata integrity testing\u201d &#8212; testing processes that support:<\/p>\n<ul>\n<li>All data warehouse sources and target schemas<\/li>\n<li>Extract, Transform, Load (ETL) processes<\/li>\n<li>Business intelligence components andfront-end applications<\/li>\n<\/ul>\n<p>We will cover how key data integrity testing strategies are addressed in each of the above categories.Other categories of DW\/ BI and ETL testing, even though important, are not a focus in this article (e.g., functional, performance, security, scalability, system and integration testing, end-to-end, etc.).<\/p>\n<h2>Classifications of Data Integrity for DW\/ BI Systems<\/h2>\n<p>To build upon Gartner\u2019s definition that you read above, data Integrity is<\/p>\n<blockquote><p>an umbrella term that refers to the consistency, accuracy, and correctness of data stored in a database.<\/p><\/blockquote>\n<p>There are 3 primary types of data integrity: entity, domain, and referential.<\/p>\n<ol>\n<li><strong>Entity Integrity<\/strong> ensures that each row in a table (for example) is uniquely identified and without duplication. Entity integrity is often enforced by placing primary key and foreign key constraints on specific columns. Testing may be achieved by defining duplicate or the null values in test data.<\/li>\n<li><strong>Domain Integrity<\/strong> requires that each set of data values\/columns falls within a specific permissible defined range. Examples of domain integrity are correct data type, format, and data length; values must fall within the range defined for the system; null status; and permitted size values. Testing may be accomplished, in part, using null, default and invalid values.<\/li>\n<li><strong>Referential Integrity<\/strong> is concerned with keeping the relationships between tables synchronized. Referential integrity is often enforced with primary key and foreign key relationships. It may be tested, for example, by deleting parent rows or the child rows in tables.<\/li>\n<\/ol>\n<h2>Verifying Data Integrity in Schemas, ETL Processes, and BI Reports<\/h2>\n<p>Before we dive into the 3 key data integrity strategies, let\u2019s quickly outline a commonframework (Figure 1) that illustrates the major DW\/ BI components that are generally verified in each phase of DH\/ BI testing.<\/p>\n<figure id=\"attachment_1159\" aria-describedby=\"caption-attachment-1159\" style=\"width: 876px\" class=\"wp-caption alignnone\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" style=\"font-family: inherit; font-style: inherit; font-weight: inherit;\" src=\"https:\/\/i0.wp.com\/www.lightsondata.com\/wp-content\/uploads\/2019\/07\/dw-bi-testing-framework.png?resize=876%2C491&#038;ssl=1\" alt=\"dw bi testing framework\" width=\"876\" height=\"491\" \/><figcaption id=\"caption-attachment-1159\" class=\"wp-caption-text\"><strong>Figure 1:<\/strong> General Framework for DW\/ BI Testing During the software development lifecycle (SDLC)<\/figcaption><\/figure>\n<h3 style=\"text-align: center;\"><em>Learn how to build a <a href=\"https:\/\/www.lightsondata.com\/the-ultimate-guide-to-a-data-quality-issues-log\/\" target=\"_blank\" rel=\"noopener noreferrer\">Data Quality issues log (free template included)<\/a><\/em><\/h3>\n<p>It\u2019s important to be on the same page with this as the following 3 key DW\/ BI components are presented in this testing framework:<\/p>\n<h2>1. Verifications of Source and Target Data Requirements and Technical Schema Implementations<\/h2>\n<p>Requirements and schema-level tests confirm to what extent the design of each data component matches the targeted business requirements. This process should include the ability to verify:<\/p>\n<ul>\n<li>Business and technical requirements for all source and target data<\/li>\n<li>Data integrity specifications technically implemented (database management systems, file systems, text files, etc.)<\/li>\n<li>Data models for each implemented data schema<\/li>\n<li>Source to target data mappings vs. data loaded into DW targets. Examples of sources and associated targets include source data that are loaded to staging targets as well as staging data that are loaded to data warehouse or data mart targets.<\/li>\n<\/ul>\n<p>Schema quality represents the ability of a schema to adequately and efficiently project &#8216;information\/data&#8217;. Schema in this definition refers to the schema of the data warehouse regardless if it is a conceptual, logical or physical schema, star, constellation, or normalized schema. However, this definition is extended here to include the schemas of all data storages used in the whole data warehouse system including the data sourcing, staging, the operational data store, and the data marts. It is beneficial to assess the schema quality in the design phase of the data warehouse.<\/p>\n<p>Detecting, analyzing and correcting schema deficiencies will boost the quality of the DW\/ BI system. Schema quality could be viewed from various dimensions, namely:<\/p>\n<ul>\n<li>Schema correctness<\/li>\n<li>Schema completeness<\/li>\n<li>Schema conformity<\/li>\n<li>Schema integrity<\/li>\n<li>Interpretability<\/li>\n<li>Tractability<\/li>\n<li>Understandability<\/li>\n<li>Concise representation<\/li>\n<\/ul>\n<h2>2. ETL source and target data integrity tests<\/h2>\n<p>Most DW integrity testing and evaluation focus on this process. Various functional and non-functional testing methods are applied to test the ETL process logic for data. The goal is to<\/p>\n<ul>\n<li>Verify that valid and invalid conditions are correctly processed for all source and target data<\/li>\n<li>Ensure primary and foreign key integrity<\/li>\n<li>Verify test correctness of data transformations,<\/li>\n<li>Ensure data cleansing<\/li>\n<li>Guarantee application of business rules, etc.<\/li>\n<\/ul>\n<p>A properly-designed ETL system extracts data from source systems, enforces data quality and consistency standards, conforms data so that separate sources can be used together, and finally delivers data in a format that enables application developers to build applications and enables end users to make decisions.<\/p>\n<h2>3. BI reporting verifications<\/h2>\n<p>BI applications provide an interface that helps users interact with the back-end. The design of these reports is critical for understanding and planning the data integrity tests.Insights such as what content uses which information maps, what ranges are leveraged in which indicators, and where interactions exist between indicators is required to build a full suite of test cases. If any measures are defined in the report itself, these should be verified as accurate. However, all other data elements that are pulled straight from the tables map should already have been validated from one of the above two sections.<\/p>\n<h2>A sample DW\/ BI verification framework and sample verifications<\/h2>\n<p>DW\/ BI data integrity verification is categorized here as follows. Figure 2 shows a verification classification framework for the techniques applicable to sources and targets in data warehouse, ETL process, and BI report applications.<\/p>\n<figure id=\"attachment_1161\" aria-describedby=\"caption-attachment-1161\" style=\"width: 450px\" class=\"wp-caption aligncenter\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/www.lightsondata.com\/wp-content\/uploads\/2019\/07\/dw-bi-integration-testing.png?resize=450%2C224&#038;ssl=1\" alt=\"dw bi integration testing\" width=\"450\" height=\"224\" \/><figcaption id=\"caption-attachment-1161\" class=\"wp-caption-text\"><strong>Figure 2:<\/strong> Framework for DWH\/BI Data Integrity Verifications<\/figcaption><\/figure>\n<p>The \u201cwhat\u201d, \u201cwhen\u201d and \u201cwhere\u201d of DW\/ BI data integration testing is represented in the following table.<\/p>\n<ul>\n<li>Column headings represent when and where data related testing will take place<\/li>\n<li>Rows represent \u201cwhat\u201d data-related items should be considered for testing<\/li>\n<\/ul>\n<p>A Sampling of Verifications in the Three Categories of Data Integrity Testing: Schemas, ETL Processes, and BI Reports:<\/p>\n<table data-rows=\"2\" data-cols=\"3\">\n<thead>\n<tr>\n<th>\n<p style=\"text-align: center;\"><strong>Verifications of Source &amp; Target Data Requirements and Technical Schema Implementations<\/strong><\/p>\n<\/th>\n<th>\n<p style=\"text-align: center;\"><strong>Source &amp; Target Data Integrity Tests After ETL\u2019s<\/strong><\/p>\n<\/th>\n<th>\n<p style=\"text-align: center;\"><strong>BI Reporting Verifications<\/strong><\/p>\n<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\n<p style=\"text-align: left;\">\u2022Data aggregation rules\u2022Data boundaries correct\u2022Data filtering, source to target correct\u2022Data formats correct\u2022Data lengths correct\u2022Data transformation rules correct and understandable\u2022Data types correct\u2022Date\/time formats correct\u2022Default values defined\u2022Domain ranges defined \u2022Field data boundaries defined\u2022Field data constraints correct\u2022Field names correct\u2022Indices defined\u2022Null fields correct\u2022Numeric field precisions correct\u2022Primary &amp; foreign keys assigned\u2022Surrogate keys identified<\/p>\n<\/td>\n<td>\n<p style=\"text-align: left;\">\u2022\u201cLookups\u201d work as expected\u2022All fields loaded as expected\u2022Concatenated data from multiple fields correct\u2022Correct handling of \u201cchange data capture\u201d (CDC\u2019s)\u2022Correct handling of \u201cslowly changing dimensions\u201d (SCD\u2019s)\u2022Data Inserts, updates, deletes as expected\u2022Data profiling on source and target data \u2013 no anomalies\u2022Data sorted as defined\u2022Data transformations, cleaning, enrichment as expected\u2022Data\/time format and values correct\u2022Default values correct\u2022Domain integrity maintained\u2022Duplicate data checks as expected\u2022ETL errors\/anomalies logged\u2022Field data aggregations correct\u2022Field data constraints applied\u2022No field data truncations\u2022No negative values where positive expected\u2022No null field data when defined not null\u2022Numeric field precisions correct\u2022Parent to child relationships checked\u2022Referential integrity as expected\u2022Rejected records handled as expected\u2022Source to target field data copied with no changes\u2022Source to target record counts as expected\u2022Sources to targets data filtered correctly \u2022Trim functions are correct<\/p>\n<\/td>\n<td>\n<p style=\"text-align: left;\">\u2022Data aggregation rules applied\u2022Data boundaries correct\u2022Data filtering correct\u2022Data formats correct\u2022Data lengths correct\u2022Data transformation rules applied\u2022Data types correct\u2022Data value sorting correct\u2022Date\/time formats correct\u2022Default values correct\u2022Derived data correct\u2022Domain ranges as expected\u2022Drill up and downs display correct data\u2022Exported data correct\u2022Field data boundaries defined\u2022Field data constraints correct\u2022Field data traceable back to DW\u2022Field names correct\u2022Field totals correct\u2022Field values for aggregates correct\u2022Filtered data fields correct (ranges, ID\u2019s, etc.)\u2022Graphed data correct\u2022Min, max, avg values correct\u2022Null fields correct\u2022Numeric field precisions correct\u2022Numeric precision for all fields\u2022Report data match DW\/ data mart\u2022Report field default values correct\u2022Report formats comply with requirements\u2022Summary fields correct\u2022Validate the access to data \u2013 security<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Key Takeaways<\/h2>\n<ul>\n<li>Data in its final state is the driving force behind organizational decision making.<\/li>\n<li>Raw data is often changed and processed to reach a usable format for BI reports. Data integrity practices ensure that this DW\/ BI information is attributable and accurate.<\/li>\n<li>Data can easily become compromised if proper measures are not taken to verify it as it moves from each environment to become available to DW\/ BI projects. Errors with data integrity commonly arise through human errors, noncompliant operating procedures, data transfers, software defects, and compromised hardware.<\/li>\n<li>By applying the 3 key data integrity testing strategies introduced in this article, you should be able to improve quality and reduce time and costs when developing and maintaining a DW\/ BI project.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Data warehousing and business intelligence users assume, and need, trustworthy data.In the Gartner Group\u2019s Online IT Glossary, data integrity and data integrity testing are defined as follows: Data Integrity: the quality of the data residing in data repositories and database objects. The measurement which users consider when analyzing the value and reliability of the data. [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":1189,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"3 key data integrity testing strategies for DW\/ BI systems #lightsondata #data #BI #DW #dataquality","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[3,4],"tags":[54,41,52,53],"class_list":["post-1157","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-intelligence","category-data-quality","tag-bi","tag-data-integration","tag-data-warehouse","tag-dw","post-wrapper","thrv_wrapper"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>3 key data integrity testing strategies for DW\/ BI systems | LightsOnData<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.lightsondata.com\/3-key-data-integrity-testing-strategies-for-dw-bi-systems\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"3 key data integrity testing strategies for DW\/ BI systems | LightsOnData\" \/>\n<meta property=\"og:description\" content=\"Data warehousing and business intelligence users assume, and need, trustworthy data.In the Gartner Group\u2019s Online IT Glossary, data integrity and data integrity testing are defined as follows: Data Integrity: the quality of the data residing in data repositories and database objects. The measurement which users consider when analyzing the value and reliability of the data. [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.lightsondata.com\/3-key-data-integrity-testing-strategies-for-dw-bi-systems\/\" \/>\n<meta property=\"og:site_name\" content=\"LightsOnData\" \/>\n<meta property=\"article:published_time\" content=\"2019-07-17T20:24:36+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2019-10-16T21:59:57+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/i2.wp.com\/www.lightsondata.com\/wp-content\/uploads\/2019\/07\/3-key-data-integrity-testing-strategies.png?fit=800%2C450&ssl=1\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"450\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Wayne Yaddow\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@georgefirican\" \/>\n<meta name=\"twitter:site\" content=\"@georgefirican\" \/>\n<meta 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Morgan Chase, Credit Suisse, Standard and Poor\u2019s, AIG, Oppenheimer Funds, and IBM. He taught IIST (International Institute of Software Testing) courses on data warehouse and ETL testing and wrote DW\/BI articles for Better Software, The Data Warehouse Institute (TDWI), Tricentis, and others. Wayne continues to lead numerous ETL testing and coaching projects on a consulting basis. 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