[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"node-by-slug-\u002Fen\u002Finsights\u002Fai-data-quality-for-ai-success-en":3,"related-content-view-26230-en":406},["Reactive",4],{"entity":5,"errors":-1},{"__typename":6,"status":7,"metatag":8,"id":203,"path":204,"hideNavigation":205,"title":206,"translations":207,"sharedHeaderRefMedia":215,"sharedTeaserTitle":206,"sharedTeaserText":241,"sharedTeaserRefMedia":243,"sharedParagraphs":253,"sharedTaxrefIndustries":374,"sharedTaxrefExpertises":378,"sharedIntroText":382,"sharedCtaLink":205,"sharedDate":384,"sharedAuthor":386},"NodeInsight",true,[9,14,18,22,26,31,35,39,43,47,51,55,59,63,67,70,74,77,80,82,85,88,92,96,100,104,108,112,116,120,124,128,132,136,140,143,146,149,152,155,158,161,164,167,171,175,178,182,186,190,193,197,201],{"tag":10,"attributes":11},"meta",{"name":12,"content":13},"title","AI data quality: strong data foundations for AI - Zühlke",{"tag":10,"attributes":15},{"name":16,"content":17},"description","AI data quality is critical for trustworthiness. Learn how governance and validation prevent failures and compliance risks while enabling safe AI scaling.",{"tag":10,"attributes":19},{"name":20,"content":21},"abstract","AI systems are only as good as the data that feeds them. Apply these learnings from our client work to ensure the AI data quality you need for successful AI initiatives.",{"tag":10,"attributes":23},{"name":24,"content":25},"icbm","51.525772, -0.082716",{"tag":27,"attributes":28},"link",{"rel":29,"href":30},"canonical","https:\u002F\u002Fcms.zuehlke.com\u002Fen\u002Finsights\u002Fai-data-quality-for-ai-success",{"tag":10,"attributes":32},{"name":33,"content":34},"robots","index, 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Zühlke",{"tag":10,"attributes":68},{"property":69,"content":17},"og:description",{"tag":10,"attributes":71},{"property":72,"content":73},"og:image","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Fmax_1300x1300\u002Fpublic\u002Fimages\u002FDataWaves.jpeg?itok=Vj_cZeJy",{"tag":10,"attributes":75},{"property":72,"content":76},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fzuhlke_innovation_social.jpg",{"tag":10,"attributes":78},{"property":79,"content":73},"og:image:url",{"tag":10,"attributes":81},{"property":79,"content":76},{"tag":10,"attributes":83},{"property":84,"content":73},"og:image:secure_url",{"tag":10,"attributes":86},{"property":84,"content":87},"https:\u002F\u002Fwww.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fzuhlke_innovation_social.jpg",{"tag":10,"attributes":89},{"property":90,"content":91},"og:image:alt","image",{"tag":10,"attributes":93},{"property":94,"content":95},"og:updated_time","2026-05-21",{"tag":10,"attributes":97},{"property":98,"content":99},"place:location:longitude","7.438014",{"tag":10,"attributes":101},{"property":102,"content":103},"place:location:latitude","46.947944",{"tag":10,"attributes":105},{"property":106,"content":107},"og:street_address","80 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data quality: how strong data foundations stop AI projects failing in production",[208,211],{"path":204,"langcode":209},{"id":210},"en",{"path":212,"langcode":213},"\u002Fde\u002Finsights\u002Fdatenqualitaet-die-datengrundlage-fuer-den-erfolg-von-ki",{"id":214},"de",{"id":216,"sharedMediaLegend":205,"mediaImage":217,"name":240},"173210",{"url":218,"width":219,"height":220,"alt":221,"title":205,"size":222,"mime":223,"variations":205,"headerMinimalVariations":224},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fai_foundations_pillar_3_2.png",1280,720,"Close-up of a person wearing glasses with blue data visualisations reflected in the lens, representing AI foundations and digital technology.",1253396,"image\u002Fpng",[225,230,231,236,239],{"name":226,"url":227,"width":228,"height":229},"FOCAL_POINT32_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002Fai_foundations_pillar_3_2.png.webp?itok=hTeiDB9Z",800,533,{"name":226,"url":227,"width":228,"height":229},{"name":232,"url":233,"width":234,"height":235},"FOCAL_POINT169_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_medium\u002Fpublic\u002Fimages\u002Fai_foundations_pillar_3_2.png.webp?itok=l_1hDnv2",1200,675,{"name":237,"url":238,"width":219,"height":220},"FOCAL_POINT169_LARGE","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_large\u002Fpublic\u002Fimages\u002Fai_foundations_pillar_3_2.png.webp?itok=bIIEIAIf",{"name":237,"url":238,"width":219,"height":220},"ai_foundations_pillar_3_2.png",{"processed":242},"\u003Cp>\u003Cspan>AI data quality is critical for trustworthiness. Learn how governance and validation prevent failures and compliance risks while enabling safe AI scaling.\u003C\u002Fspan>\u003C\u002Fp>",{"id":244,"sharedMediaLegend":205,"mediaImage":245,"name":252},"170985",{"url":246,"width":247,"height":248,"alt":249,"title":205,"size":250,"mime":251,"variations":205},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FDataWaves.jpeg",2400,1800,"Abstract layers of light and waves illustrating data flow",501943,"image\u002Fjpeg","DataWaves.jpeg",[254,261,296,301,312],{"__typename":255,"id":256,"ctaLink":205,"pgSharedText":257,"pgSharedTitle":205,"pgSharedCtaLink":205},"ParagraphPgText","27742",{"value":258,"processed":259,"format":260},"\u003Cp>AI proofs of concept often look convincing until they meet live data. Then come the inconsistencies, permission gaps, silent errors and loss of confidence that stall deployment.\u003C\u002Fp>\u003Cp>If your AI initiatives are slowing down as they \u003Ca href=\"\u002Fnode\u002F27000\" target=\"_blank\" title=\"Discover what it really takes to scale AI from proof-of-concept to production\" data-entity-type=\"node\" data-entity-uuid=\"71c4a910-7cd2-44f5-b2b7-e5afbede6efe\" data-entity-substitution=\"canonical\">move towards production\u003C\u002Fa>, the first explanation is often the wrong one. People tend to blame the model, the vendor, or the lack of internal adoption; but in reality, one of the most common causes is that the data foundation is not strong enough for real-world use.\u003C\u002Fp>\u003Cp>That is why data quality has become essential to prevent AI failure and scale safely.\u003C\u002Fp>\u003Cp>When data quality is weak, the effects show up quickly: incorrect outputs, biased decisions, security exposure, compliance surprises, and a loss of trust that can stall an otherwise promising AI initiative.\u003C\u002Fp>\u003Cp>This article focuses on that question.\u003C\u002Fp>\u003Cp>\u003Cem>This article is part of our 'Value from AI now' series on the three key challenges organisations face when scaling AI initiatives. \u003C\u002Fem>\u003Ca href=\"\u002Fnode\u002F26955\" title=\"Generate measurable value from AI now: how to turn AI into business transformation\" data-entity-type=\"node\" data-entity-uuid=\"4f28673c-5d7b-4d13-8034-1343b0427eea\" data-entity-substitution=\"canonical\" target=\"_blank\">\u003Cem>Explore the full framework here\u003C\u002Fem>\u003C\u002Fa>\u003Cem>.\u003C\u002Fem>\u003C\u002Fp>\u003Ch2>How data quality impacts your AI initiatives\u003C\u002Fh2>\u003Cp>Data quality refers to the accuracy, completeness, consistency, reliability, and relevance of your data and it has a direct bearing on AI decision-making and outputs.\u003C\u002Fp>\u003Cp>Low-quality data compromises decision making, which can impact everything from strategic decision-making to customer interactions. It’s also one of the main causes of GenAI project failure.\u003C\u002Fp>\u003Cp>The classic adage ‘garbage in, garbage out’ rings very true when it comes to building AI systems. Even when businesses can deliver early wins from the likes of GenAI RAG applications, they soon realise that data access and quality are critical for turning functional prototypes into scalable, product-grade solutions.\u003C\u002Fp>\u003Cp>When data doesn’t have high quality, it’s easy for an organisation to start seeing some familiar risks:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e7d0f3a15adad334d77f43f5c8282bb68\">confidently wrong outputs based on incomplete or outdated information  \u003C\u002Fli>\u003Cli data-list-item-id=\"e58dd70eeb3da84ef0f23b72bf6530685\">biased outcomes caused by non-representative data  \u003C\u002Fli>\u003Cli data-list-item-id=\"e9d7dcabdf7500353780e0d7c3b1a0791\">operational disruption when automated decisions rely on inconsistent sources  \u003C\u002Fli>\u003Cli data-list-item-id=\"e3f020ebc24e19f9a79e91619ea6b1c37\">regulatory or compliance exposure when permitted use, provenance, or accountability are unclear\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>What 'good' AI data quality actually means\u003C\u002Fh2>\u003Cp>If you train a model on poor or irrelevant data, the results will likely be disappointing. But labelling data as either 'good' or 'bad' in this way might be too simplistic. To truly understand data quality, more nuance is required.\u003C\u002Fp>\u003Cp>It comes down to the specific problem you’re trying to solve. A data source might be an excellent fit for one AI model and worthless for another. That’s why choosing the best data for each task is so important.\u003C\u002Fp>\u003Cp>To mitigate risks and truly become an AI-empowered business, companies must focus on collecting data that’s not only accurate and timely but also directly relevant to the challenge being addressed – whether you’re an insurance firm looking to streamline underwriting and claims processing, or a retailer looking to enhance real-time strategic decision making.\u003C\u002Fp>\u003Cp>In a nutshell then, ‘good’ AI data quality is data that’s:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e2fc91fd4c5ccaa87fac472feb8d00897\">relevant to the use case  \u003C\u002Fli>\u003Cli data-list-item-id=\"e2763c4fcc42c463f44cc13b386cc6a58\">accurate and reliable  \u003C\u002Fli>\u003Cli data-list-item-id=\"e891c7ead0e7b7a6396b4b0539a721030\">complete and consistent enough for the level of risk involved  \u003C\u002Fli>\u003Cli data-list-item-id=\"e7a42497254b0b7520fb425e4b7ca57d9\">timely enough to support real operations  \u003C\u002Fli>\u003Cli data-list-item-id=\"e0ed24fcae43a7ad534dabb5279475377\">secure and accessible under the right controls  \u003C\u002Fli>\u003Cli data-list-item-id=\"e2f3d9e8a04e5332a853d01f557c07d35\">compliant with the organisation’s obligations  \u003C\u002Fli>\u003Cli data-list-item-id=\"e6b41139ff4816ffabb2588b63807f147\">understandable across teams\u003C\u002Fli>\u003C\u002Ful>\u003Cblockquote>\u003Cp>AI tools can learn to identify data anomalies and predict potential future quality issues, minimising errors before they escalate.\u003C\u002Fp>\u003C\u002Fblockquote>\u003Ch2>A framework to ensure data quality for AI initiatives\u003C\u002Fh2>\u003Cp>We’re witnessing an increased focus on data quality across our clients and the many industries we work  with. More organisations are recognising that data quality is critical for the success of AI initiatives.\u003C\u002Fp>\u003Cp>To make data truly AI-ready, organisations should approach it methodically, focusing on alignment, validation, and governance.\u003C\u002Fp>","\u003Cp>AI proofs of concept often look convincing until they meet live data. Then come the inconsistencies, permission gaps, silent errors and loss of confidence that stall deployment.\u003C\u002Fp>\u003Cp>If your AI initiatives are slowing down as they \u003Ca href=\"\u002Fen\u002Finsights\u002Fscaling-ai-from-proof-of-concept-to-production\" target=\"_blank\" title=\"Discover what it really takes to scale AI from proof-of-concept to production\" data-entity-type=\"node\" data-entity-uuid=\"71c4a910-7cd2-44f5-b2b7-e5afbede6efe\" data-entity-substitution=\"canonical\">move towards production\u003C\u002Fa>, the first explanation is often the wrong one. People tend to blame the model, the vendor, or the lack of internal adoption; but in reality, one of the most common causes is that the data foundation is not strong enough for real-world use.\u003C\u002Fp>\u003Cp>That is why data quality has become essential to prevent AI failure and scale safely.\u003C\u002Fp>\u003Cp>When data quality is weak, the effects show up quickly: incorrect outputs, biased decisions, security exposure, compliance surprises, and a loss of trust that can stall an otherwise promising AI initiative.\u003C\u002Fp>\u003Cp>This article focuses on that question.\u003C\u002Fp>\u003Cp>\u003Cem>This article is part of our 'Value from AI now' series on the three key challenges organisations face when scaling AI initiatives. \u003C\u002Fem>\u003Ca href=\"\u002Fen\u002Fexpertise\u002Fai-implementation\u002Fai-value-now-turn-ai-into-business-transformation\" title=\"Generate measurable value from AI now: how to turn AI into business transformation\" data-entity-type=\"node\" data-entity-uuid=\"4f28673c-5d7b-4d13-8034-1343b0427eea\" data-entity-substitution=\"canonical\" target=\"_blank\">\u003Cem>Explore the full framework here\u003C\u002Fem>\u003C\u002Fa>\u003Cem>.\u003C\u002Fem>\u003C\u002Fp>\u003Ch2>How data quality impacts your AI initiatives\u003C\u002Fh2>\u003Cp>Data quality refers to the accuracy, completeness, consistency, reliability, and relevance of your data and it has a direct bearing on AI decision-making and outputs.\u003C\u002Fp>\u003Cp>Low-quality data compromises decision making, which can impact everything from strategic decision-making to customer interactions. It’s also one of the main causes of GenAI project failure.\u003C\u002Fp>\u003Cp>The classic adage ‘garbage in, garbage out’ rings very true when it comes to building AI systems. Even when businesses can deliver early wins from the likes of GenAI RAG applications, they soon realise that data access and quality are critical for turning functional prototypes into scalable, product-grade solutions.\u003C\u002Fp>\u003Cp>When data doesn’t have high quality, it’s easy for an organisation to start seeing some familiar risks:\u003C\u002Fp>\u003Cul>\u003Cli>confidently wrong outputs based on incomplete or outdated information  \u003C\u002Fli>\u003Cli>biased outcomes caused by non-representative data  \u003C\u002Fli>\u003Cli>operational disruption when automated decisions rely on inconsistent sources  \u003C\u002Fli>\u003Cli>regulatory or compliance exposure when permitted use, provenance, or accountability are unclear\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>What 'good' AI data quality actually means\u003C\u002Fh2>\u003Cp>If you train a model on poor or irrelevant data, the results will likely be disappointing. But labelling data as either 'good' or 'bad' in this way might be too simplistic. To truly understand data quality, more nuance is required.\u003C\u002Fp>\u003Cp>It comes down to the specific problem you’re trying to solve. A data source might be an excellent fit for one AI model and worthless for another. That’s why choosing the best data for each task is so important.\u003C\u002Fp>\u003Cp>To mitigate risks and truly become an AI-empowered business, companies must focus on collecting data that’s not only accurate and timely but also directly relevant to the challenge being addressed – whether you’re an insurance firm looking to streamline underwriting and claims processing, or a retailer looking to enhance real-time strategic decision making.\u003C\u002Fp>\u003Cp>In a nutshell then, ‘good’ AI data quality is data that’s:\u003C\u002Fp>\u003Cul>\u003Cli>relevant to the use case  \u003C\u002Fli>\u003Cli>accurate and reliable  \u003C\u002Fli>\u003Cli>complete and consistent enough for the level of risk involved  \u003C\u002Fli>\u003Cli>timely enough to support real operations  \u003C\u002Fli>\u003Cli>secure and accessible under the right controls  \u003C\u002Fli>\u003Cli>compliant with the organisation’s obligations  \u003C\u002Fli>\u003Cli>understandable across teams\u003C\u002Fli>\u003C\u002Ful>\u003Cblockquote>\u003Cp>AI tools can learn to identify data anomalies and predict potential future quality issues, minimising errors before they escalate.\u003C\u002Fp>\u003C\u002Fblockquote>\u003Ch2>A framework to ensure data quality for AI initiatives\u003C\u002Fh2>\u003Cp>We’re witnessing an increased focus on data quality across our clients and the many industries we work  with. More organisations are recognising that data quality is critical for the success of AI initiatives.\u003C\u002Fp>\u003Cp>To make data truly AI-ready, organisations should approach it methodically, focusing on alignment, validation, and governance.\u003C\u002Fp>","full_html",{"__typename":262,"id":263,"caption":264,"captionSecondary":205,"pgSharedTitle":205,"pgMediaRefMedia":265,"secondaryImage":205,"edgeToEdge":295,"fullWidth":295},"ParagraphPgMedia","27749","Alignment, governance, and validation are critical components for getting your data ‘AI-ready’",{"id":266,"sharedMediaLegend":267,"mediaImage":268,"name":294},"170989","Alignment, governance, and validation are critical components for getting your data ‘AI ready’",{"url":269,"width":270,"height":271,"alt":272,"title":205,"size":273,"mime":223,"variations":205,"mediaVariations":274,"infographVariations":288},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FAI%20data%20quality%20pillars.png",1033,656,"The three pillars of AI data quality: governance, alignment, and validation",41520,[275,280,281,284,287],{"name":276,"url":277,"width":278,"height":279},"FIXED_WIDTH_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_small\u002Fpublic\u002Fimages\u002FAI%20data%20quality%20pillars.png.webp?itok=Ju62l5ce",900,572,{"name":276,"url":277,"width":278,"height":279},{"name":282,"url":283,"width":270,"height":271},"FIXED_WIDTH_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_medium\u002Fpublic\u002Fimages\u002FAI%20data%20quality%20pillars.png.webp?itok=BjcFu7jy",{"name":285,"url":286,"width":270,"height":271},"FIXED_WIDTH_LARGE","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_large\u002Fpublic\u002Fimages\u002FAI%20data%20quality%20pillars.png.webp?itok=_P6wjkOB",{"name":285,"url":286,"width":270,"height":271},[289,290,291,292,293],{"name":276,"url":277,"width":278,"height":279},{"name":276,"url":277,"width":278,"height":279},{"name":276,"url":277,"width":278,"height":279},{"name":282,"url":283,"width":270,"height":271},{"name":282,"url":283,"width":270,"height":271},"AI data quality pillars.png",false,{"__typename":255,"id":297,"ctaLink":205,"pgSharedText":298,"pgSharedTitle":205,"pgSharedCtaLink":205},"27750",{"value":299,"processed":300,"format":260},"\u003Ch3>1. Align: make sure the data fits the use case \u003C\u002Fh3>\u003Cp>Before a system goes anywhere near production, leaders should \u003Cstrong>be clear about what data it depends on and why\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>That means asking:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e094bb2e80f6b3ae403bd41bef942819b\">Which sources is the system actually using?  \u003C\u002Fli>\u003Cli data-list-item-id=\"e236216ab4dce268db8a413c062779b0b\">Are we allowed to use this data for this purpose and in this jurisdiction?  \u003C\u002Fli>\u003Cli data-list-item-id=\"e503e63994c12a6e5de829dd79ce92725\">Are the \u003Ca href=\"\u002Fnode\u002F26122\" data-entity-type=\"node\" data-entity-uuid=\"12600952-09de-488d-b5f4-37578c3f148a\" data-entity-substitution=\"canonical\" title=\"Explore how data contracts and lineage can unlock AI innovation\" target=\"_blank\">definitions and semantics\u003C\u002Fa> consistent across teams?  \u003C\u002Fli>\u003Cli data-list-item-id=\"e47078b3d00f769dcbd16307146fe36e5\">Is the data representative of what the system will face in real operation?  \u003C\u002Fli>\u003Cli data-list-item-id=\"e621203403fbcaff5682912319ff26c48\">Does the data support the actual business objective, or just the demo?  \u003C\u002Fli>\u003C\u002Ful>\u003Cp>This is where many projects already carry hidden risk. A model may appear effective in a narrow environment, but \u003Cstrong>once the real data landscape shows up — fragmented systems, conflicting definitions, unclear permissions — the reliability of the whole initiative starts to weaken\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>Alignment is about stopping that earlier.\u003C\u002Fp>\u003Ch3>2. Validate: test reliability before the user does\u003C\u002Fh3>\u003Cp>That means more than a one-off quality check before launch. It means understanding what “good enough” looks like for the workflow in question, then checking consistently for missing fields, outdated values, duplication, anomalies and drift.\u003C\u002Fp>\u003Cp>Validation should answer questions such as:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e47d93ce507e658e087eba5305678d4a4\">Is the data consistent across systems?  \u003C\u002Fli>\u003Cli data-list-item-id=\"e7abae899f148c395e3b5983e929d7255\">Are key fields complete enough for the level of risk involved?  \u003C\u002Fli>\u003Cli data-list-item-id=\"eded913d55a524218bd9aeb68191e9c5b\">Are there anomalies that suggest quality is degrading?  \u003C\u002Fli>\u003Cli data-list-item-id=\"e39126582f534908e11171e4972464d82\">Do we have clear thresholds for when the data is not good enough to rely on?  \u003C\u002Fli>\u003Cli data-list-item-id=\"e39085582651aafd60ff2878ed458ae20\">Will somebody act when those thresholds are breached?  \u003C\u002Fli>\u003C\u002Ful>\u003Cp>\u003Cstrong>Automation can help\u003C\u002Fstrong> a great deal here. It can surface anomalies earlier, detect patterns humans might miss, and make monitoring more efficient. \u003Cstrong>But automation does not remove the need for judgement.\u003C\u002Fstrong> Validation only works when the organisation has agreed what it is validating against, and who is accountable for the response.\u003C\u002Fp>\u003Ch3>3. Govern: keep the data trustworthy over time \u003C\u002Fh3>\u003Cp>Good data does not stay good by accident.\u003C\u002Fp>\u003Cp>Sources change. Definitions shift. Access rights evolve. New use cases introduce new pressures. Teams come and go. Models are updated. Without governance, the data layer quietly degrades until performance drops, confidence disappears, or a visible incident forces the issue.\u003C\u002Fp>\u003Cp>\u003Cstrong>That is why governance matters.\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>In practice, it should cover:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"efcedc7727ee70c4a003a7ab5da6deafc\">\u003Cstrong>clear ownership\u003C\u002Fstrong> and stewardship  \u003C\u002Fli>\u003Cli data-list-item-id=\"e6fc9e43416dcd10e02f566a9983fc1ed\">\u003Cstrong>traceability\u003C\u002Fstrong> of where data came from and how it changed  \u003C\u002Fli>\u003Cli data-list-item-id=\"e0794f92bca95f40adecd1f2fcc3d6d92\">\u003Cstrong>version control\u003C\u002Fstrong> over important data assets  \u003C\u002Fli>\u003Cli data-list-item-id=\"ed71fc64719efc73433fc2a6bba5c1677\">\u003Cstrong>compliance\u003C\u002Fstrong> with internal policy and external regulation  \u003C\u002Fli>\u003Cli data-list-item-id=\"e0a6db2ff24fcf9d3cbe9680174dc5160\">\u003Cstrong>review processes\u003C\u002Fstrong> when usage, structure or business meaning changes  \u003C\u002Fli>\u003C\u002Ful>\u003Cp>This is also where the link to \u003Ca href=\"\u002Fnode\u002F26522\" data-entity-type=\"node\" data-entity-uuid=\"4760ac5e-0880-4704-a0de-be9e5e7a57fd\" data-entity-substitution=\"canonical\" target=\"_blank\" title=\"Discover how to turn governance into a driver of long-term success\">AI trust\u003C\u002Fa> becomes obvious. No governance framework can compensate for data nobody can explain. If leaders want AI that is safe, auditable and ready to scale, the data underneath it must be fit, permitted and traceable.\u003C\u002Fp>\u003Ch2>The questions boards should ask before approving scale\u003C\u002Fh2>\u003Cp>Before approving or expanding an AI initiative, senior leaders should be able to get clear answers to five questions.\u003C\u002Fp>\u003Col>\u003Cli data-list-item-id=\"e4362d518b44af1b2ff02b5cc94dc8061\">Do we know exactly which data sources this system depends on?  \u003C\u002Fli>\u003Cli data-list-item-id=\"e4bb1272c724a13fbd5a0ddc694113fec\">Are we allowed to use this data for this purpose, in this market, and under these conditions?\u003C\u002Fli>\u003Cli data-list-item-id=\"e8b71051223a418292e2985d00c7e377d\">How representative is the data of the people, scenarios and edge cases the system will face in production?  \u003C\u002Fli>\u003Cli data-list-item-id=\"e5b3dfcd9680d1242ce609bf8c57eedf6\">Who owns ongoing data quality, monitoring and change?  \u003C\u002Fli>\u003Cli data-list-item-id=\"ec81d8e00e1f035d99f745d5fb1056324\">How quickly can we trace a problem back to a source, transformation, permission rule or change event?\u003C\u002Fli>\u003C\u002Fol>\u003Cp>These questions help ensure that AI initiatives begin with a defensible and sustainable data foundation.\u003C\u002Fp>\u003Ch2>Lay the data foundations for reliable, production-ready AI\u003C\u002Fh2>\u003Cp>High-quality data is the foundation of successful AI initiatives  foundation of successful AI initiatives, ensuring that prototypes develop evolve into scalable, reliable, and impactful solutions.\u003C\u002Fp>\u003Cp>However,  many businesses rush this step, either by underestimating its importance or only recognising its value when problems arise.\u003C\u002Fp>\u003Cp>To truly unlock the potential of AI:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"ed1f5ab476d6a76bc78e045c2293fc333\">\u003Cstrong>Focus on quality from the start.\u003C\u002Fstrong> Accurate, complete, and consistent data sets are essential.\u003C\u002Fli>\u003Cli data-list-item-id=\"e124e8247c87e74935e400eb858603410\">\u003Cstrong>Invest in \u003C\u002Fstrong>\u003Ca href=\"\u002Fnode\u002F26510\" title=\"Discover why change management should be a priority\" data-entity-type=\"node\" data-entity-uuid=\"f82de6c0-8948-4526-bf8c-a0f9ebe031fb\" data-entity-substitution=\"canonical\" target=\"_blank\">\u003Cstrong>governance and culture\u003C\u002Fstrong>\u003C\u002Fa>\u003Cstrong>.\u003C\u002Fstrong> A robust framework ensures that quality becomes second nature across your organisation.\u003C\u002Fli>\u003Cli data-list-item-id=\"e8f27e20380e5c2c242ebf1cc343795f7\">\u003Cstrong>Leverage the right tools and approaches.\u003C\u002Fstrong> Automating processes with AI tools can enhance efficiency and reliability, but it needs to be paired with accountability.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>If you want to understand how robust your organisation’s data foundation for AI really is, Zühlke can help assess your current readiness and identify where improvements in AI data governance, validation, and risk management will have the greatest impact.     \u003C\u002Fp>","\u003Ch3>1. Align: make sure the data fits the use case \u003C\u002Fh3>\u003Cp>Before a system goes anywhere near production, leaders should \u003Cstrong>be clear about what data it depends on and why\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>That means asking:\u003C\u002Fp>\u003Cul>\u003Cli>Which sources is the system actually using?  \u003C\u002Fli>\u003Cli>Are we allowed to use this data for this purpose and in this jurisdiction?  \u003C\u002Fli>\u003Cli>Are the \u003Ca href=\"\u002Fen\u002Finsights\u002Fdata-contracts-and-lineage\" data-entity-type=\"node\" data-entity-uuid=\"12600952-09de-488d-b5f4-37578c3f148a\" data-entity-substitution=\"canonical\" title=\"Explore how data contracts and lineage can unlock AI innovation\" target=\"_blank\">definitions and semantics\u003C\u002Fa> consistent across teams?  \u003C\u002Fli>\u003Cli>Is the data representative of what the system will face in real operation?  \u003C\u002Fli>\u003Cli>Does the data support the actual business objective, or just the demo?  \u003C\u002Fli>\u003C\u002Ful>\u003Cp>This is where many projects already carry hidden risk. A model may appear effective in a narrow environment, but \u003Cstrong>once the real data landscape shows up — fragmented systems, conflicting definitions, unclear permissions — the reliability of the whole initiative starts to weaken\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>Alignment is about stopping that earlier.\u003C\u002Fp>\u003Ch3>2. Validate: test reliability before the user does\u003C\u002Fh3>\u003Cp>That means more than a one-off quality check before launch. It means understanding what “good enough” looks like for the workflow in question, then checking consistently for missing fields, outdated values, duplication, anomalies and drift.\u003C\u002Fp>\u003Cp>Validation should answer questions such as:\u003C\u002Fp>\u003Cul>\u003Cli>Is the data consistent across systems?  \u003C\u002Fli>\u003Cli>Are key fields complete enough for the level of risk involved?  \u003C\u002Fli>\u003Cli>Are there anomalies that suggest quality is degrading?  \u003C\u002Fli>\u003Cli>Do we have clear thresholds for when the data is not good enough to rely on?  \u003C\u002Fli>\u003Cli>Will somebody act when those thresholds are breached?  \u003C\u002Fli>\u003C\u002Ful>\u003Cp>\u003Cstrong>Automation can help\u003C\u002Fstrong> a great deal here. It can surface anomalies earlier, detect patterns humans might miss, and make monitoring more efficient. \u003Cstrong>But automation does not remove the need for judgement.\u003C\u002Fstrong> Validation only works when the organisation has agreed what it is validating against, and who is accountable for the response.\u003C\u002Fp>\u003Ch3>3. Govern: keep the data trustworthy over time \u003C\u002Fh3>\u003Cp>Good data does not stay good by accident.\u003C\u002Fp>\u003Cp>Sources change. Definitions shift. Access rights evolve. New use cases introduce new pressures. Teams come and go. Models are updated. Without governance, the data layer quietly degrades until performance drops, confidence disappears, or a visible incident forces the issue.\u003C\u002Fp>\u003Cp>\u003Cstrong>That is why governance matters.\u003C\u002Fstrong>\u003C\u002Fp>\u003Cp>In practice, it should cover:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>clear ownership\u003C\u002Fstrong> and stewardship  \u003C\u002Fli>\u003Cli>\u003Cstrong>traceability\u003C\u002Fstrong> of where data came from and how it changed  \u003C\u002Fli>\u003Cli>\u003Cstrong>version control\u003C\u002Fstrong> over important data assets  \u003C\u002Fli>\u003Cli>\u003Cstrong>compliance\u003C\u002Fstrong> with internal policy and external regulation  \u003C\u002Fli>\u003Cli>\u003Cstrong>review processes\u003C\u002Fstrong> when usage, structure or business meaning changes  \u003C\u002Fli>\u003C\u002Ful>\u003Cp>This is also where the link to \u003Ca href=\"\u002Fen\u002Finsights\u002Fai-risk-management-turning-governance-into-a-driver-of-long-term-success\" data-entity-type=\"node\" data-entity-uuid=\"4760ac5e-0880-4704-a0de-be9e5e7a57fd\" data-entity-substitution=\"canonical\" target=\"_blank\" title=\"Discover how to turn governance into a driver of long-term success\">AI trust\u003C\u002Fa> becomes obvious. No governance framework can compensate for data nobody can explain. If leaders want AI that is safe, auditable and ready to scale, the data underneath it must be fit, permitted and traceable.\u003C\u002Fp>\u003Ch2>The questions boards should ask before approving scale\u003C\u002Fh2>\u003Cp>Before approving or expanding an AI initiative, senior leaders should be able to get clear answers to five questions.\u003C\u002Fp>\u003Col>\u003Cli>Do we know exactly which data sources this system depends on?  \u003C\u002Fli>\u003Cli>Are we allowed to use this data for this purpose, in this market, and under these conditions?\u003C\u002Fli>\u003Cli>How representative is the data of the people, scenarios and edge cases the system will face in production?  \u003C\u002Fli>\u003Cli>Who owns ongoing data quality, monitoring and change?  \u003C\u002Fli>\u003Cli>How quickly can we trace a problem back to a source, transformation, permission rule or change event?\u003C\u002Fli>\u003C\u002Fol>\u003Cp>These questions help ensure that AI initiatives begin with a defensible and sustainable data foundation.\u003C\u002Fp>\u003Ch2>Lay the data foundations for reliable, production-ready AI\u003C\u002Fh2>\u003Cp>High-quality data is the foundation of successful AI initiatives  foundation of successful AI initiatives, ensuring that prototypes develop evolve into scalable, reliable, and impactful solutions.\u003C\u002Fp>\u003Cp>However,  many businesses rush this step, either by underestimating its importance or only recognising its value when problems arise.\u003C\u002Fp>\u003Cp>To truly unlock the potential of AI:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Focus on quality from the start.\u003C\u002Fstrong> Accurate, complete, and consistent data sets are essential.\u003C\u002Fli>\u003Cli>\u003Cstrong>Invest in \u003C\u002Fstrong>\u003Ca href=\"\u002Fen\u002Finsights\u002Fai-in-the-workplace-why-change-management-should-be-a-priority\" title=\"Discover why change management should be a priority\" data-entity-type=\"node\" data-entity-uuid=\"f82de6c0-8948-4526-bf8c-a0f9ebe031fb\" data-entity-substitution=\"canonical\" target=\"_blank\">\u003Cstrong>governance and culture\u003C\u002Fstrong>\u003C\u002Fa>\u003Cstrong>.\u003C\u002Fstrong> A robust framework ensures that quality becomes second nature across your organisation.\u003C\u002Fli>\u003Cli>\u003Cstrong>Leverage the right tools and approaches.\u003C\u002Fstrong> Automating processes with AI tools can enhance efficiency and reliability, but it needs to be paired with accountability.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>If you want to understand how robust your organisation’s data foundation for AI really is, Zühlke can help assess your current readiness and identify where improvements in AI data governance, validation, and risk management will have the greatest impact.     \u003C\u002Fp>",{"__typename":302,"id":303,"pgTitle":304,"text":305,"link":306,"coloredBackground":7,"compactMode":7,"actionVariant":27,"buttonText":205,"pgEmbededCode":309},"ParagraphPgCtaDefault","35117","Explore how to turn strong foundations into scalable transformation","In our work with Zurich Airport, see how data, platforms and operational readiness created the conditions for long-term impact.",{"title":307,"url":308,"internal":7},"Read the case study","\u002Fen\u002Fcase-studies\u002Fzurich-airport-transforms-operations-for-a-data-driven-future",{"value":310,"processed":310,"format":311},"","code",{"__typename":313,"id":314,"columns":315,"listItems":316,"text":205,"title":372,"variant":373},"ParagraphPgContentList","35120","2col",[317,349],{"id":318,"icon":205,"link":319,"logo":205,"media":322,"text":345,"title":348},"35118",{"title":320,"url":321,"internal":7},"EXPLORE THIS TOPIC","\u002Fen\u002Finsights\u002Fwhy-ai-time-savings-dont-create-business-value",{"id":323,"sharedMediaLegend":205,"mediaImage":324,"name":344},"173153",{"url":325,"width":219,"height":220,"alt":326,"title":205,"size":327,"mime":223,"variations":205,"listItemVariations":328},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fpillar_1_pillar_page.png","Three colleagues collaborating around a table in an office; a woman with curly hair smiles while listening to a seated colleague who is gesturing, while a third colleague looks on, with a laptop and drinks visible, conveying a positive team interaction.",1038100,[329,334,335,339,343],{"name":330,"url":331,"width":332,"height":333},"FOCAL_POINT31_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_1_small\u002Fpublic\u002Fimages\u002Fpillar_1_pillar_page.png.webp?itok=dx1XFriH",600,200,{"name":330,"url":331,"width":332,"height":333},{"name":336,"url":337,"width":234,"height":338},"FOCAL_POINT52_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_5_2_medium\u002Fpublic\u002Fimages\u002Fpillar_1_pillar_page.png.webp?itok=0YP8G_9D",480,{"name":340,"url":341,"width":219,"height":342},"FOCAL_POINT52_LARGE","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_5_2_large\u002Fpublic\u002Fimages\u002Fpillar_1_pillar_page.png.webp?itok=_9fPfaRY",512,{"name":340,"url":341,"width":219,"height":342},"pillar_1_pillar_page.png",{"value":346,"processed":346,"format":347},"\u003Cp>If your organisation is still asking where the commercial impact is, explore how AI creates measurable value across the business.\u003C\u002Fp>","content_list","Strong foundations are only part of the story",{"id":350,"icon":205,"link":351,"logo":205,"media":353,"text":369,"title":371},"35119",{"title":320,"url":352,"internal":7},"\u002Fen\u002Finsights\u002Fscaling-ai-initiatives-why-trust-determines-enterprise-success",{"id":354,"sharedMediaLegend":205,"mediaImage":355,"name":368},"173108",{"url":356,"width":219,"height":220,"alt":357,"title":205,"size":358,"mime":223,"variations":205,"listItemVariations":359},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fai_trust_pillar_2_1.png","Profile silhouette of a person facing a glowing digital brain, representing human interaction with artificial intelligence.",1122721,[360,362,363,365,367],{"name":330,"url":361,"width":332,"height":333},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_1_small\u002Fpublic\u002Fimages\u002Fai_trust_pillar_2_1.png.webp?itok=1IxQuq9g",{"name":330,"url":361,"width":332,"height":333},{"name":336,"url":364,"width":234,"height":338},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_5_2_medium\u002Fpublic\u002Fimages\u002Fai_trust_pillar_2_1.png.webp?itok=pwht3IhI",{"name":340,"url":366,"width":219,"height":342},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_5_2_large\u002Fpublic\u002Fimages\u002Fai_trust_pillar_2_1.png.webp?itok=MPGqLJl8",{"name":340,"url":366,"width":219,"height":342},"ai_trust_pillar_2_1.png",{"value":370,"processed":370,"format":347},"\u003Cp>If the bigger challenge is building confidence, control and defensibility around AI, explore how governance supports long-term success.\u003C\u002Fp>","Readiness alone will not earn trust","If a different challenge is your real constraint","media",[375],{"id":376,"name":377},"855","All industries",[379],{"id":380,"name":381},"110","AI",{"processed":383},"\u003Cp>AI data quality is critical for trustworthiness. Learn how governance and validation prevent failures and compliance risks while enabling safe AI scaling.\u003C\u002Fp>\n",{"timestamp":385},1778068800,[387],{"status":7,"contactName":388,"contactProfession":389,"contactLinkedinUrl":205,"sharedEmail":205,"sharedRefMedia":390},"Raffaele De Piano","Principal Data Architect at Zühlke",{"id":391,"sharedMediaLegend":267,"mediaImage":392,"name":405},"170990",{"url":393,"width":394,"height":228,"alt":395,"title":205,"size":396,"mime":251,"variations":205,"authorVariations":397},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FRaffa_1.jpeg",736,"A headshot of Raffaele who wears a blue shirt and stands in front of a warm brown 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