{"id":24472,"date":"2026-06-11T13:16:19","date_gmt":"2026-06-11T11:16:19","guid":{"rendered":"https:\/\/www.cleverrepublic.com\/?post_type=blog&p=24472"},"modified":"2026-06-11T13:16:20","modified_gmt":"2026-06-11T11:16:20","slug":"why-data-quality-matters-for-successful-generative-ai-implementation","status":"publish","type":"blog","link":"https:\/\/www.cleverrepublic.com\/nl\/resources\/blog\/why-data-quality-matters-for-successful-generative-ai-implementation\/","title":{"rendered":"Why Data Quality matters for successful\u00a0(generative)\u00a0AI Implementation"},"content":{"rendered":"
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We all already know that AI is transforming the way in which organisations work.<\/span>\u00a0This could be\u00a0<\/span>as simple as\u00a0<\/span>an AI assistant or a chatbot<\/span>,<\/span>\u00a0or\u00a0<\/span>it could be that the entire company changes to revolve around an AI functionality.\u00a0<\/span>Nonetheless, most companies are\u00a0<\/span>heavily<\/span> investing in AI capabilities. <\/span><\/span><\/p>\n

However, most companies only want to include an AI functionality based on the hype, not because it delivers any value.\u00a0<\/span>What they neglect is the\u00a0<\/span>quality of the data<\/span>, while\u00a0<\/span>the\u00a0<\/span>AI<\/span>\u00a0model<\/span>\u00a0is only as reliable as the data it is built upon<\/span>.\u00a0<\/span>That\u2019s<\/span>\u00a0where data quality\u00a0<\/span>testing\u00a0<\/span>comes in<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n

Many AI projects\u00a0<\/span>don\u2019t<\/span>\u00a0reach\u00a0<\/span>their\u00a0<\/span>full potential because the underlying data is not of sufficient quality<\/span>.<\/span>\u00a0This is often described by the principle of \u2018<\/span>g<\/span>arbage in, garbage out<\/span>\u2019<\/span>, <\/span>indicating <\/span>that <\/span>outcomes are poor if the input information is poor.<\/span>\u00a0<\/span><\/span><\/p>\n

A good example of this is an AI powered customer support chatbot.\u00a0<\/span>Let\u2019s<\/span>\u00a0say the company is implementing a chatbot\u00a0<\/span>to help employees with questions about HR policies. The<\/span>\u00a0<\/span>AI model itself might work well, but the underlying policies that get\u00a0<\/span>fed into the model might\u00a0<\/span>contain<\/span>\u00a0outdated or incomplete policies<\/span>,<\/span>\u00a0or conflicting information in different systems.\u00a0<\/span>This will result in the chatbot\u00a0<\/span>providing inconsistent or just\u00a0<\/span>flat<\/span>–<\/span>out\u00a0<\/span>incorrect answers.\u00a0<\/span><\/span><\/p>\n

So,<\/span>\u00a0if two documents exist with a different number of vacation days,\u00a0<\/span>a different number will be provided to the employee based on what document the chatbot uses as its source.\u00a0<\/span>Let\u2019s<\/span> say your company offers 28 vacation days this year, but it was 26 last year and that document still exists. The bot could provide you the wrong number, giving you the impression that you have less vacation days this year. <\/span>The AI model\u00a0<\/span>probably\u00a0<\/span>function<\/span>s<\/span>\u00a0properly<\/span>\u00a0here<\/span>, but the lack of data quality creates issues.<\/span>\u00a0<\/span><\/span>\u00a0<\/span><\/p>\n

This is an\u00a0important\u00a0intermediary step that most organisations overlook.\u00a0They\u00a0immediately\u00a0go from the collecting data process to deploying an AI model.\u00a0They miss the step where they make sure that the data is trustworthy and\u00a0properly managed.\u00a0Before scaling AI, organisations should make sure that\u00a0their AI is ready by implementing validation,\u00a0monitoring\u00a0and governance.<\/span>\u00a0<\/span><\/p>\n

Strong Data Quality practices make sure that the data that gets used in the AI model is accurate, complete, consistent and up-to-date. Use this in combination with Data Governance and AI Governance, and you create visibility in where the data comes from, who owns the data and whether you can trust the data. Here it becomes visible that Data Quality and AI Governance co-exist and are mutually beneficial. The better the Data Quality, the better the output of the AI model.\u00a0<\/span>\u00a0<\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Poor Data Quality in AI models also creates business risks.\u00a0<\/span>This has everything to do with trust. Employees or customers might lose trust in the AI model if it does not consistently p<\/span>rovide <\/span>accurate <\/span>information. <\/span>This could lower adoption rates, reduce operational\u00a0<\/span>efficiency<\/span>\u00a0or might even create compliance risks in certain industries.<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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So how to implement Data Quality to improve your AI models?<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The first step is understanding what data is\u00a0actually being\u00a0used\u00a0by the AI model. Many organisations have data spread across multiple systems,\u00a0departments\u00a0and applications, most probably with inconsistent definitions\u00a0or duplicate records.\u00a0Before\u00a0deploying AI company-wide, organisation should\u00a0identify\u00a0critical datasets and assess whether they are fit for purpose. This includes checking for missing values, outdated records,\u00a0conflicting\u00a0business\u00a0definitions\u00a0and incomplete metadata.<\/span>\u00a0<\/span><\/p>

The second step is implementing\u00a0continuous Data Quality monitoring. Data\u00a0Quality\u00a0is not a one-time thing.\u00a0All data within in the company constantly changes, which means that new quality issues can arise every day. Monitoring allows organisations to\u00a0identify\u00a0issues early before they can negatively\u00a0impact\u00a0AI outputs. This should include validation rules, anomaly detection, duplication\u00a0checks\u00a0and alerts when certain thresholds are reached.\u00a0Want to see\u00a0what continuous monitoring looks like, read our blog about it\u00a0<\/span>here<\/span><\/a>.<\/span>\u00a0<\/span><\/p>

Another\u00a0important factor\u00a0is assigning ownership. Data Quality improves significantly when there are clear responsibilities for\u00a0maintaining\u00a0and validating data. This is where Data Governance and AI Governance become\u00a0closely connected\u00a0to AI success. By assigning data owners and stewards, organisations can create accountability and ensure that data\u00a0remains\u00a0trustworthy over longer periods of time.<\/span>\u00a0<\/span><\/p>

Standardisation also play\u00a0a big role. AI models perform better when data follows consistent formats,\u00a0definitions\u00a0and business rules across different systems. If one department defines a customer differently from another department, or if systems store data in different formats, the AI model may produce unreliable results. Standardising business definitions and creating a\u00a0shared understanding of key data elements within the organisation helps to reduce these inconsistencies.<\/span>\u00a0<\/span><\/p>

To successfully implement an AI\u00a0model,\u00a0you need all in\u00a0place,\u00a0Data Quality monitoring, a Data Governance\u00a0implementation\u00a0and AI Governance. Want to know more about how to implement\u00a0these topics, feel free to reach out, we are\u00a0always open for the conversation.\u00a0<\/span>\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

We all already know that AI is transforming the way in which organisations work.\u00a0This could be\u00a0as simple as\u00a0an AI assistant or a chatbot,\u00a0or\u00a0it could be that the entire company changes to revolve around an AI functionality.\u00a0Nonetheless, most companies are\u00a0heavily investing in AI capabilities. However, most companies only want to include an AI functionality based on…<\/p>","protected":false},"author":25,"featured_media":24481,"template":"","categories":[146],"class_list":["post-24472","blog","type-blog","status-publish","has-post-thumbnail","hentry","category-ai_governance"],"yoast_head":"\nWhy Data Quality Matters for Successful AI Implementation<\/title>\n<meta name=\"description\" content=\"Poor Data Quality leads to unreliable AI outcomes. 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