{"id":49695,"date":"2008-04-18T02:24:00","date_gmt":"2008-04-17T23:24:00","guid":{"rendered":"https:\/\/www.altoros.com\/blog\/?p=49695"},"modified":"2021-07-01T06:56:27","modified_gmt":"2021-07-01T03:56:27","slug":"wrong-approach-to-data-quality-right-approach-to-data-quality","status":"publish","type":"post","link":"https:\/\/www.altoros.com\/blog\/wrong-approach-to-data-quality-right-approach-to-data-quality\/","title":{"rendered":"Poor Data Quality Can Have Long-Term Effects"},"content":{"rendered":"<p><center><small>(<a href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Open_Data_Quality_Standards_Framework.png\">Featured image 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\/wrong-approach-to-data-quality-right-approach-to-data-quality\/#Ever-changing_customer_data\" >Ever-changing customer data<\/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\/wrong-approach-to-data-quality-right-approach-to-data-quality\/#How_to_address_this\" >How to address this<\/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\/wrong-approach-to-data-quality-right-approach-to-data-quality\/#Think_about_it_in_advance\" >Think about it in advance<\/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\/wrong-approach-to-data-quality-right-approach-to-data-quality\/#Further_reading\" >Further reading<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"Ever-changing_customer_data\"><\/span>Ever-changing customer data<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>As much as you hear about the importance of data quality being the determinant of your organization\u2019s success, companies all over the world still use inaccurate and outdated data in their daily work. In the bulk of information that piles up over the months, even years, you usually can\u2019t even identify what\u2019s more urgent and important.<\/p>\n<p>The traditional ETL approach, which quite a few companies have come to use, sure is helpful. But once you\u2019ve gone through its stages, it\u2019s important not to forget that your new cleansed data is still constantly being enriched and changed. So, in less than no time, a new challenge emerges as you get your cleansed data mixed with new data that isn\u2019t necessarity as consistent and reliable as it should be.<\/p>\n<p>At the same time, Gartner estimates that 50% of enterprises undertaking a CRM strategy are unaware of significant data quality problems in their systems. According to <a href=\"https:\/\/www.linkedin.com\/in\/berniegracy\/\" rel=\"noopener noreferrer\" target=\"_blank\">Bernie Gracy<\/a> of Pitney Bowes Group, these <a href=\"https:\/\/web.archive.org\/web\/20090201045434\/http:\/\/www.cxoamerica.com\/pastissue\/article.asp?art=269005&#038;issue=184\" rel=\"noopener noreferrer\" target=\"_blank\">consequences<\/a> may influence C-level executives of various roles. &#8220;The effects of poor data can have long-term, devastating consequences,&#8221; Bernie says.<\/p>\n<div id=\"attachment_56835\" style=\"width: 160px\" class=\"wp-caption alignright\"><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2008\/04\/bernie-gracy.jpg\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-56835\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2008\/04\/bernie-gracy-150x150.jpg\" width=\"150\" height=\"150\" class=\"size-thumbnail wp-image-56835\" \/><\/a><p id=\"caption-attachment-56835\" class=\"wp-caption-text\"><small>Bernie Gracy<\/small><\/p><\/div>\n<blockquote><p>&#8220;Recipients of duplicate mailings are likely to become frustrated and question the firm\u2019s overall operating efficiency. If these redundant mailings each consistently misspell the individual\u2019s name or address, the frustration level is likely to approach alienation or even a legal concern\u2014especially if the recipient had previously made a request to the mailer that they be removed from the vendor\u2019s mailing list or asked to be placed on an industry-wide, do-not-mail list.&#8221; \u2014Bernie Gracy, Pitney Bowes Group<\/p><\/blockquote>\n<p>Many of these inefficiencies may lead to the loss of credibility, significant fines, and even lawsuits.<\/p>\n<blockquote><p>&#8220;Data inefficiencies can also result in missed up-sell and cross-sell opportunities. Without a single view of the customer across the enterprise, it\u2019s impossible to aggregate information to make decisions. This makes it impossible to distinguish between single-product and multi-product buyers, or between new and existing customers.&#8221; \u2014Bernie Gracy, Pitney Bowes Group<\/p><\/blockquote>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_to_address_this\"><\/span>How to address this<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>What should you do? Analyzing many expert research studies and customer involvements, we see that implementing a unified and repeated data quality monitoring approach is important. Here&#8217;s a great schema found on Wikipedia, confirming the need for an integrated approach.<\/p>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2008\/04\/Open_Data_Quality_Cleansing_and_Monitoring_Pattern.png\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2008\/04\/Open_Data_Quality_Cleansing_and_Monitoring_Pattern.png\" width=\"640\" class=\"aligncenter size-full wp-image-56843\" \/><\/a><small>Data quality monitoring workflow (<a href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Open_Data_Quality_Cleansing_and_Monitoring_Pattern.png\" rel=\"noopener noreferrer\" target=\"_blank\">Image credit<\/a>)<\/small><\/center><\/p>\n<p>The steps you could follow while at that include:<\/p>\n<ul>\n<li>Create a clear standard that your incoming data should match<\/li>\n<li>Identify the main issues with incoming data by checking it against the created standard<\/li>\n<li>Look for the ways to solve the identified problems (as a possibility you cold create a notification system to send out alerts whenever unvalid or inconsistent data is detected)<\/li>\n<\/ul>\n<p>At the first glance, this looks like it could solve your problems. But that\u2019s just your incoming data. Another part of the problem lies in the clean data already stored in your warehouse. It\u2019s validity isn\u2019t everlasting, is it? Thus, a few more things in your to-do list:<\/p>\n<ul>\n<li>Identify the most appropriate time span for your data to be re-verified<\/li>\n<li>Schedule your data verification system to conduct repeated checks according to the identified data validity time span<\/li>\n<\/ul>\n<p>All in all, just keep in mind that data within any organization is a dynamic and constanly changing asset, and data quality checking should become a repeated procedure, rather than a one-time practice.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Think_about_it_in_advance\"><\/span>Think about it in advance<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<pSo, data cleansing and data quality should be both a one-time procedure and a repeated practice. But here\u2019s a catch, when do you usually think of and\/or worry about the quality of your data? When migrating and integrating it? When using it in marketing? When building your strategy based on it?<\/p>\n<p>Has it ever crossed your mind that the quality of data needs to be thought through before it\u2019s actually gathered? That\u2019s new and you don\u2019t see too many companies thinking of it\u2026yet. We don\u2019t quite realize that even at the early stages of preparing for data gathering and while obtaining it, data quality aleady plays an important role in the future of your company. It is the early stages that make a difference in what your data become in the end. If approached properly from the very beginning, your data will surely pay off when you get to sharing and maintaining it, and especially when applying it.<\/p>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2008\/04\/Open_Data_Quality_Cleansing_Framework.png\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2008\/04\/Open_Data_Quality_Cleansing_Framework.png\" width=\"640\" class=\"aligncenter size-full wp-image-56845\" \/><\/a><small>A sample data cleansing framework (<a href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Open_Data_Quality_Cleansing_Framework.png\" rel=\"noopener noreferrer\" target=\"_blank\">Image credit<\/a>)<\/small><\/center><\/p>\n<p>Cost-wise, this approach is rather efficient, too. Corporate data is not the thing to save up on\u2014investing in data quality from the very beginning would save up a lot when it comes to verification, cleansing, and use. Cleansing, as a matter of fact, may very well become redundant. Sweet?<\/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\/neglecting-the-quality-of-data-leads-to-failed-crm-projects\/\">Neglecting the Quality of Data Leads to Failed CRM Projects<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/data-quality-upstream-or-downstream\/\">Data Quality: Upstream or Downstream?<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/5-things-to-watch-out-for-in-data-warehousing\/\">5 Things to Watch Out for in 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>, edited by <a href=\"https:\/\/www.altoros.com\/blog\/author\/alex\/\">Alex Khizhniak<\/a>.<\/small><\/center><\/p>\n","protected":false},"excerpt":{"rendered":"<p>(Featured image credit)<\/p>\n<p>&nbsp;<\/p>\n<p>Ever-changing customer data<\/p>\n<p>As much as you hear about the importance of data quality being the determinant of your organization\u2019s success, companies all over the world still use inaccurate and outdated data in their daily work. In the bulk of information that piles up over the months, even years, [&#8230;]<\/p>\n","protected":false},"author":178,"featured_media":56847,"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-49695","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>Poor Data Quality Can Have Long-Term Effects | Altoros<\/title>\n<meta name=\"description\" content=\"Following a thorough data quality strategy may take a while, but you won\u2019t regret it.\" \/>\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\/wrong-approach-to-data-quality-right-approach-to-data-quality\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Poor Data Quality Can Have Long-Term Effects | Altoros\" \/>\n<meta property=\"og:description\" content=\"(Featured image credit) &nbsp; Ever-changing customer data As much as you hear about the importance of data quality being the determinant of your organization\u2019s success, companies all over the world still use inaccurate and outdated data in their daily work. 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On her blog, she covers ETL, EAI, BI, open-source, and all the aspects of data integration. Alena is focused on creating awareness, fostering a better understanding, and keeping her readers well-informed on the emerging problems and data integration practices of the day. 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