{"id":6882,"date":"2026-07-28T00:53:16","date_gmt":"2026-07-28T04:53:16","guid":{"rendered":"https:\/\/workai.tv\/news\/2026\/07\/ai-data\/8-proactive-steps-to-build-trusted-data-for-analytics-and-ai\/"},"modified":"2026-07-28T00:53:16","modified_gmt":"2026-07-28T04:53:16","slug":"8-proactive-steps-to-build-trusted-data-for-analytics-and-ai","status":"publish","type":"post","link":"https:\/\/workai.tv\/news\/2026\/07\/ai-data\/8-proactive-steps-to-build-trusted-data-for-analytics-and-ai\/","title":{"rendered":"8 Proactive Steps to Build Trusted Data for Analytics and AI"},"content":{"rendered":"<h2>Share with your CDO<\/h2>\n<p>AI amplifies bad data rather than correcting it, which makes <a href=\"https:\/\/www.techtarget.com\/searchdatamanagement\/feature\/Proactive-practices-for-data-quality-improvement\" target=\"_blank\" rel=\"noopener nofollow\">proactive data quality management<\/a> a prerequisite for any serious AI investment. Anne Marie Smith outlines eight practices for building data that both humans and AI agents can trust, covering ownership structures, root cause analysis, metadata standards, continuous monitoring, and cultural accountability. The argument is less about new technology and more about the organizational plumbing that determines whether AI outputs are reliable enough to act on.<\/p>\n<h2>What this means for your business<\/h2>\n<p>The CDOs who will struggle most with this aren&#8217;t the ones ignoring data quality; they&#8217;re the ones still treating it as a periodic cleansing exercise. AI changes the math. A model trained on stale or inconsistent data doesn&#8217;t fail loudly, it fails quietly, producing confident-sounding recommendations that are subtly wrong. If your organization is deploying AI agents with any degree of autonomy, the question isn&#8217;t whether your data is perfect but whether you have the monitoring infrastructure to catch deterioration before it propagates into decisions.<\/p>\n<p>Smith&#8217;s framing of data stewards as &#8220;operational guardians&#8221; rather than cleanup crew is the most practically useful reframe in the piece. The recurring failure mode in large enterprises looks like this: technical teams own the pipelines, business teams own the outcomes, and nobody owns the gap between them. Stewardship that bridges business intent and technical implementation closes that gap. Her point that stewards now need to collaborate directly with AI governance teams is particularly well-timed, since most organizations have built those two functions in parallel silos that have never formally intersected.<\/p>\n<p>Smith consults in information management, which gives her a practitioner&#8217;s credibility on process but also means the framework tilts toward the ideal state rather than the messy politics of getting there. The hardest part of this agenda isn&#8217;t defining quality standards or deploying monitoring dashboards; it&#8217;s getting a CFO to fund stewardship headcount that doesn&#8217;t ship product. If your data quality program stalls, the bottleneck is almost certainly there, not in the eight steps themselves. I&#8217;d revise this assessment if a major AI deployment failure were traced publicly to governance gaps that adequate stewardship would have caught, because that&#8217;s the forcing function that shifts budget priorities.<\/p>\n<p><em>Based on reporting from <a href=\"https:\/\/www.techtarget.com\/searchdatamanagement\/feature\/Proactive-practices-for-data-quality-improvement\" target=\"_blank\" rel=\"noopener nofollow\">8 Proactive Steps to Build Trusted Data for Analytics and AI<\/a>, originally published 2026-07-23 03:00:00.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Share with your CDO AI amplifies bad data rather than correcting it, which makes proactive data quality management a prerequisite for any serious AI investment. Anne Marie Smith outlines eight practices for building data that both humans and AI agents can trust, covering ownership structures, root cause analysis, metadata standards, continuous monitoring, and cultural accountability. [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":6883,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[146],"tags":[237],"tmauthors":[],"class_list":["post-6882","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai-data","tag-cdo"],"_links":{"self":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts\/6882","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/comments?post=6882"}],"version-history":[{"count":0,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts\/6882\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/media\/6883"}],"wp:attachment":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/media?parent=6882"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/categories?post=6882"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/tags?post=6882"},{"taxonomy":"tmauthors","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/tmauthors?post=6882"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}