{"id":49772,"date":"2008-08-26T07:45:23","date_gmt":"2008-08-26T04:45:23","guid":{"rendered":"https:\/\/www.altoros.com\/blog\/?p=49772"},"modified":"2021-08-03T21:21:43","modified_gmt":"2021-08-03T18:21:43","slug":"5-things-to-watch-out-for-in-data-warehousing","status":"publish","type":"post","link":"https:\/\/www.altoros.com\/blog\/5-things-to-watch-out-for-in-data-warehousing\/","title":{"rendered":"5 Things to Watch Out for in Data Warehousing"},"content":{"rendered":"<p><center><small>Featured image: Oracle Warehouse Builder (<a href=\"https:\/\/blogs.oracle.com\/dataintegration\/post\/advanced-aggregation\" rel=\"noopener noreferrer\" target=\"_blank\">credit<\/a>)<\/small><\/center><\/p>\n<p>&nbsp;<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.altoros.com\/blog\/5-things-to-watch-out-for-in-data-warehousing\/#Why_create_a_data_warehouse\" >Why create a data warehouse?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.altoros.com\/blog\/5-things-to-watch-out-for-in-data-warehousing\/#Things_to_pay_attention_to\" >Things to pay attention to<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.altoros.com\/blog\/5-things-to-watch-out-for-in-data-warehousing\/#Success_metrics_in_data_warehousing\" >Success metrics in data warehousing<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.altoros.com\/blog\/5-things-to-watch-out-for-in-data-warehousing\/#Further_reading\" >Further reading<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"Why_create_a_data_warehouse\"><\/span>Why create a data warehouse?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>In a recent article, we discussed how <a href=\"https:\/\/en.wikipedia.org\/wiki\/Business_intelligence\" rel=\"noopener noreferrer\" target=\"_blank\">business intelligence<\/a> coupled with <a href=\"https:\/\/en.wikipedia.org\/wiki\/Data_warehouse\" rel=\"noopener noreferrer\" target=\"_blank\">data warehousing<\/a> helps to improve decision-making and creates a unified view of an organization, in case it is done properly. Today, let&#8217;s focus deeper on the role of a data warehouse in this process.<\/p>\n<p>The <a href=\"https:\/\/web.archive.org\/web\/20081120021253\/http:\/\/www.beyeblogs.com\/rajanguptaBIBlog\/archive\/2008\/07\/maximizing_data_warehouse_roi.php\" target=\"_blank\" rel=\"noopener noreferrer\">post<\/a> by Rajan Gupta in BeyeBlogs gets to the core of data warehousing and explores what you need to enhance your ROI. According to Rajan, there are several reasons for using a data warehouse as a single reference information.<\/p>\n<blockquote>\n<p>1. <strong>Maintain consistency<\/strong>.<br \/>\n2. If your production data needs an offline fix (like standardizing customer and product IDs), it&#8217;s better to do that <strong>data-fix in one place<\/strong>. If you have separate enterprise reporting and analysis platforms, you will need to do that data transformation at two places, instead of one.<br \/>\n3. <strong>Data auditability<\/strong>. A single information reference point having detailed data will provide a good audit-trail of your summary transactions\/analysis.<br \/>\n4. <strong>ETL synergy<\/strong>. If you have diverse systems, and you want to have some level of information integration, its better to do it at one place. Doing ETL for summary data warehouse and a detailed reporting database, will almost double your efforts.<br \/>\n5. <strong>Overall platform ease<\/strong>. You maintain only one information infrastructure (administration, scheduling, publishing, performance tuning\u2026).<br \/>\n6. <strong>Ease of change management<\/strong>. Any change in your information requirements, or changes in your source systems will be managed and done at one place.<\/p>\n<\/blockquote>\n<p>So, then, with all these benefits, why is there so much fuss about granular data in a data warehouse? &#8220;Having most granular (or detailed) transaction-level data is core to broad-basing the data warehouse applications,&#8221; Rajan says. However, he enlists some of the releated challenges and demands:<\/p>\n<blockquote>\n<p>1. <b>Brings forth the real issues with transactional data<\/b>. In summary data warehouses, you can ignore some of the transaction-level data issues and do some patch-work to ensure that aggregated data has a level of acceptable quality. Bringing in granular data, will need more incisive surgery on your data issues. This will extend the time of implementation.<br \/>\n2. <b>ETL efforts go up<\/b>. This is related to the first point. Your key plumbing task in DW will become larger and more complex.<br \/>\n3. <b>Existing robust and stable reporting and querying platforms<\/b>. Why fix what ain\u2019t broken? etc.<\/p>\n<\/blockquote>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Things_to_pay_attention_to\"><\/span>Things to pay attention to<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>So, yes, there have been talks of the concept of data warehousing being misleading, failing to deliver efficient solutions at the enterprise level and frequently causing problems upon implementation. That&#8217;s why I\u2019ll try to sum up a few things you should definitely try to watch out for when tackling your data warehouses.<\/p>\n<p>1) First and foremost: <strong>data quality<\/strong>. When your data is dirty, outdated and\/or inconsistent upon entering the warehouse, the results you are gonna get won\u2019t be any better, really. Data warehousing is not supposed to deal with your erroneous data, it\u2019s not supposed to perform data cleansing. These processes need to take place BEFORE your data gets even close to the warehouse, that is, your data integration strategy needs to address low-quality data problem.<\/p>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2008\/03\/oracle-complex-data-warehousing-concept.gif\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2008\/03\/oracle-complex-data-warehousing-concept.gif\" width=\"650\" height=\"285\" class=\"aligncenter size-full wp-image-61087\" \/><\/a><small>A sample architecture for a complex data warehouse (Image credit: <a href=\"https:\/\/docs.oracle.com\/cd\/A87860_01\/doc\/server.817\/a76994\/concept.htm\" rel=\"noopener noreferrer\" target=\"_blank\">Oracle<\/a>)<\/small><\/center><\/p>\n<p>2) Come to think of it, <strong>data integration<\/strong> is the second thing to watch out for.  Do your integration tools live up to your requirements? Can your software handle the data volumes you have? Will it comply with the newly added to your warehouse source systems and subject areas? How high is the level of automation of your integration system? Can you avoid manual intervention? You gotta ask yourself all of these questions before you complain that your warehouse isn\u2019t providing you with the quality of information you expected.<\/p>\n<p>3) Next, <strong>dreaming too big<\/strong>. When you build sand castles, you gotta realize they\u2019ll disappear in a matter of days, or even hours. Your can\u2019t have it all and, at the same time, you can\u2019t have your pie and eat it, too. Breaking the project into small segments, giving them enough time to deliver and having patience is the key to having a pleasant experience with your data warehousing solution. What? Did you think you can fix all the mess in your data in a matter of days? =)<\/p>\n<p>4) Then, don\u2019t go <strong>rushing into solutions<\/strong>. Don\u2019t panic. Yes, warehouse projects require time and effort on your part. Yes, it\u2019s gonna be complicated at first. But that\u2019s not the reason to stop with one project and rush into another. Stick with your first choice, fix it, work on it. Multiple projects will waste your resources and end up as another silo aimlessly taking up your corporate resources.<\/p>\n<p>5) Finally, make sure you have a <strong>scalable architecture<\/strong> that you can redesign according to your increasing needs. Your business grows, sometimes grows quicker than you think (the number of customers increases, they have more information, more data to be processed) and you want your solution to continue to perform on the same level and live up to your expectations.<\/p>\n<p>The list goes on actually, as there are more things to watch out for\u2026but these are the first that come to mind.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Success_metrics_in_data_warehousing\"><\/span>Success metrics in data warehousing<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Recently, <a href=\"https:\/\/www.techtarget.com\/searchdatamanagement\/definition\/data-management?track=sy560\">a question<\/a> was posed to a SearchDataManagement.com expert as to what metrics should be used for a data warehousing project.<\/p>\n<p><a href=\"https:\/\/www.linkedin.com\/in\/wmcknight\/\" rel=\"noopener noreferrer\" target=\"_blank\">William McKnight<\/a> from Lucidity Consulting recommended the following three as most valuable:<\/p>\n<div id=\"attachment_62218\" style=\"width: 160px\" class=\"wp-caption alignright\"><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2008\/09\/William-McKnight.jpg\"><img decoding=\"async\" aria-describedby=\"caption-attachment-62218\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2008\/09\/William-McKnight.jpg\" width=\"150\" class=\"size-thumbnail wp-image-62218\" \/><\/a><p id=\"caption-attachment-62218\" class=\"wp-caption-text\"><small>William McKnight<\/small><\/p><\/div>\n<ol>\n<li><strong>Business return on investment (ROI)<\/strong>. Are you getting the bottom line success with your project?<\/li>\n<li><strong>Data usage<\/strong>. Is your data used as intended by the users?<\/li>\n<li><strong> Data gathering and availability<\/strong>. Is your data available to the extent it should be?<\/li>\n<\/ol>\n<p>He also mentioned up time, cycle end times, successful loads and clean data levels as secondary technical metrics to pay attention to.<\/p>\n<blockquote><p>&#8220;In short, you want to eliminate intolerable defects\u2014as defined by the data stewards. These defects come in 10 different categories: referential integrity, uniqueness\/deduplication, cardinality, subtype\/supertype constructs, value domains\/bounds, formatting errors, contingency conditions, calculations, correctness and conformance to &#8216;clean&#8217; set of values.&#8221; \u2014William McKnight<\/p><\/blockquote>\n<p>With all these recomendations, your data warehouse has chances to serve your enterprise needs.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Further_reading\"><\/span>Further reading<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/integrating-new-systems-acquired-with-the-merger-in-a-data-warehouse\/\">Acquisitions and Mergers: Adding New Systems into a Data Warehouse<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/why-data-warehousing-why-business-intelligence\/\">Why Business Intelligence for Data Warehousing?<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<hr\/>\n<p><center><small>The post is written by <a href=\"https:\/\/www.altoros.com\/blog\/author\/alena-semeshko\/\">Alena Semeshko<\/a>, with contributions from <a href=\"https:\/\/www.altoros.com\/blog\/author\/alex\/\">Alex Khizhniak<\/a>.<\/small><\/center><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Featured image: Oracle Warehouse Builder (credit)<\/p>\n<p>&nbsp;<\/p>\n<p>Why create a data warehouse?<\/p>\n<p>In a recent article, we discussed how business intelligence coupled with data warehousing helps to improve decision-making and creates a unified view of an organization, in case it is done properly. Today, let&#8217;s focus deeper on the role of a data [&#8230;]<\/p>\n","protected":false},"author":178,"featured_media":62225,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[7],"tags":[960,895],"class_list":["post-49772","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-and-opinion","tag-data-integration","tag-research-and-development"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>5 Things to Watch Out for in Data Warehousing | Altoros<\/title>\n<meta name=\"description\" content=\"Problems don\u2019t come out of nowhere\u2014usually, there are some reasons behind.\" \/>\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.altoros.com\/blog\/5-things-to-watch-out-for-in-data-warehousing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"5 Things to Watch Out for in Data Warehousing | Altoros\" \/>\n<meta property=\"og:description\" content=\"Featured image: Oracle Warehouse Builder (credit) &nbsp; Why create a data warehouse? In a recent article, we discussed how business intelligence coupled with data warehousing helps to improve decision-making and creates a unified view of an organization, in case it is done properly. 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