{"id":24650,"date":"2026-07-28T09:41:47","date_gmt":"2026-07-28T07:41:47","guid":{"rendered":"https:\/\/www.cleverrepublic.com\/?post_type=blog&p=24650"},"modified":"2026-07-28T10:21:38","modified_gmt":"2026-07-28T08:21:38","slug":"data-quality-framework","status":"publish","type":"blog","link":"https:\/\/www.cleverrepublic.com\/nl\/resources\/blog\/data-quality-framework\/","title":{"rendered":"A Data Quality Framework That Works in\u00a0Practice\u00a0"},"content":{"rendered":"\t\t
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Many theories, books and step by step plans describe how to start with Data Quality using a Data Quality Framework. It rightly feels like a milestone when this framework is on paper and ready for implementation. However, this is often where the biggest challenge begins: how do you bring\u00a0these\u00a0well considered plans into practice?<\/span>\u00a0<\/span><\/p>

A\u00a0framework can be strong in terms of content,\u00a0but\u00a0at the same time\u00a0it only creates value when employees understand what it means, which responsibilities they have and how they should use it in their daily work.\u00a0This blog therefore focuses on three questions: what is a Data Quality Framework, which conditions are\u00a0required\u00a0for implementation and, most importantly, how do you ensure that the framework becomes embedded in the business?<\/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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What is a Data Quality Framework? <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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A Data Quality Framework is the coherent way in which an organisation defines, governs, measures, monitors and continuously improves the desired quality of its data.<\/span>\u00a0<\/span><\/p>

The framework provides direction for how an\u00a0organisation\u00a0manages Data Quality. It clarifies which data\u00a0is\u00a0important, what the\u00a0organisation\u00a0considers to be\u00a0sufficient\u00a0quality\u00a0and what needs to happen when data does not meet the agreed expectations.<\/span>\u00a0<\/span><\/p>

A Data Quality Framework should define at least the following topics.<\/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

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1. Which data is important to the organisation?\u00a0<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Define which datasets and critical data elements are essential for business processes, decision making, reporting, Compliance and AI applications.<\/span>\u00a0<\/span><\/p>

Not all data has the same value or risk profile. It is therefore important to\u00a0determine\u00a0which data requires\u00a0additional\u00a0attention.\u00a0Adoption\u00a0of the framework also becomes\u00a0much\u00a0more\u00a0straightforward\u00a0when employees genuinely\u00a0recognise\u00a0the critical data elements\u00a0as being\u00a0critical.<\/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

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2. What does quality mean?\u00a0<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Define which Data Quality dimensions and Data Quality standards apply to the intended use of the data.\u00a0Also define which Business Rules apply. For a deep dive into\u00a0Data Quality dimensions read our\u00a0<\/span>blog\u00a0\u201cthe\u00a0six\u00a0most used data\u00a0quality\u00a0dimensions\u201d<\/a>.<\/span>\u00a0<\/span><\/p>

What counts as sufficient quality differs depending on the situation. Data may, for example, need to meet different requirements when it is used in an operational process than when the same data is used for reporting or an AI application.<\/span>\u00a0<\/span><\/p>

For this reason, well defined Business Rules help clarify when data is correct,\u00a0complete\u00a0or usable. Employees should recognise these Business Rules from their daily work.\u00a0<\/span>This is why<\/span>\u00a0these Business Rules should be gathered from the operational teams.<\/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

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3. How is quality measured?\u00a0<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Describe which controls and measurement methods are used, in which tools they are performed, how often measurements take place and how the results are recorded. Want to know more about sufficient tooling? Read our <\/span>blog\u00a0\u201cdata\u00a0quality\u00a0tools\u201d<\/a>.<\/span>\u00a0<\/span><\/p>

By making clear agreements in advance about how quality is measured, the organisation creates a consistent view of quality. This makes it possible to compare results,\u00a0monitor\u00a0developments and\u00a0identify\u00a0deviations in time.<\/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

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4. How are Data Quality problems resolved?\u00a0<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Define how issues are registered, prioritised, assigned, investigated,\u00a0corrected\u00a0and formally closed.<\/span>\u00a0<\/span><\/p>

Root cause analysis is an important part of this process.\u00a0However, correcting inaccurate data alone is not enough. By also investigating and addressing the cause, the organisation can reduce the number of recurring issues. Make sure that this process supports the business and test it in practice.<\/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

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5. How are recurring and new problems prevented?\u00a0<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Describe the\u00a0methodology\u00a0for the controls and measures needed to prevent new errors.\u00a0In addition,\u00a0define how the organisation periodically evaluates the framework. This ensures that continuous improvement becomes a fixed part of the approach.<\/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

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Conditions for successful implementation <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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To put these elements into practice<\/span>, several conditions are needed for successful implementation.\u00a0These include technical conditions, management mandate, a clear connection with business\u00a0objectives\u00a0and vision, and an established Data Governance organisation.<\/span>\u00a0<\/span><\/p>

In our view, clear\u00a0roles\u00a0and responsibilities\u00a0in the area of\u00a0Data Quality are among the most important conditions. The organisation must clearly understand who\u00a0is responsible for\u00a0the data, who performs the daily management, who manages the framework and who provides support for content related or technical questions.<\/span>\u00a0<\/span><\/p>

In practice, this includes the following roles:<\/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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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tData Owner\t\t\t\t\t\t\t<\/a>\t\t\t\t\t\t<\/h4>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
Responsible for the data and the related Data Quality issues.\u00a0<\/div>\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tData Steward\t\t\t\t\t\t\t<\/a>\t\t\t\t\t\t<\/h4>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
Performs the daily management and monitors definitions, rules,\u00a0issues\u00a0and improvement actions.\u00a0<\/div>\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tData Quality Manager\t\t\t\t\t\t\t<\/a>\t\t\t\t\t\t<\/h4>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
Manages the Data Quality Framework and\u00a0monitors\u00a0its coherence and application.\u00a0<\/div>\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSubject Matter Expert\t\t\t\t\t\t\t<\/a>\t\t\t\t\t\t<\/h4>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
Has detailed knowledge of the data and supports the development and assessment of Business Rules.\u00a0<\/div>\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tIT Specialist\t\t\t\t\t\t\t<\/a>\t\t\t\t\t\t<\/h4>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
Provides support for problems in\u00a0data processing, technical\u00a0questions\u00a0and the implementation of Data Quality Rules.\u00a0<\/div>\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Together, these roles create the foundation for clear ownership and effective collaboration.\u00a0To make this practical, document the roles and responsibilities in a RACI and communicate them clearly to the business. As the Data Governance organisation matures and grows, expand the role allocation accordingly.<\/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

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Pragmatic tips for implementation <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Now that you know what a Data Quality Framework is and which minimum conditions are\u00a0required, we share several best practices below to help bring the framework into practice.<\/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

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Implement the framework within one data domain, process or use case first.\u00a0Preferably choose data with visible business impact.\u00a0As a result, it becomes easier to\u00a0demonstrate\u00a0the value that the framework can deliver.\u00a0Based on this experience, develop the approach further before rolling out the framework more widely.<\/span>\u00a0<\/span><\/p>

By starting small, the implementation\u00a0remains\u00a0manageable and the organisation can learn what does and does not work in practice.<\/span>\u00a0<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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Define roles, responsibilities and\u00a0decision making\u00a0authority before the organisation starts measuring.\u00a0At a minimum, clarify who acts as the Data Owner, Data\u00a0Steward\u00a0and Data Quality Manager. When these responsibilities are unclear, it is not possible to implement the framework described above properly.<\/span>\u00a0<\/span><\/p>

In other words, a framework only works when it is clear who needs to act when data does not meet the agreed quality standards, and when colleagues feel both the urgency and responsibility to\u00a0take action.<\/span>\u00a0<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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Work with enthusiastic and experienced Data Stewards to assess whether the framework is understandable,\u00a0workable\u00a0and useful.\u00a0Data Stewards can quickly\u00a0identify\u00a0where procedures are too theoretical,\u00a0unclear\u00a0or administratively burdensome. They are close to the daily use of data and can therefore\u00a0properly assess\u00a0whether the agreements align with practice.<\/span>\u00a0<\/span><\/p>

For this reason, do not develop the framework solely from the perspective of Data Governance or IT.\u00a0<\/span>Use\u00a0feedback\u00a0from\u00a0Data Stewards\u00a0to\u00a0make\u00a0the\u00a0framework\u00a0more\u00a0workable.\u00a0<\/span>\u00a0<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tMake well defined Business Rules a central component <\/a>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\t

Moreover, B<\/span>usiness Rules form the basis for Data Quality Rules.<\/span>\u00a0<\/span>As described earlier, employees recognise Business Rules from their daily work. To make the story around the framework clear, it is therefore important to start by implementing Business Rules and explaining the framework using these\u00a0<\/span>known\u00a0<\/span>rules.<\/span><\/span>\u00a0<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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Develop a clear visualisation that shows the main components and how they relate to each other.\u00a0<\/span>\u00a0<\/span><\/p>

A\u00a0one page\u00a0visualisation makes the framework more accessible to the business.\u00a0At the same\u00a0time, it\u00a0prevents employees from first having to read an extensive policy document before they understand the main points.<\/span>\u00a0<\/span><\/p>

Use the visualisation actively in communication, workshops and conversations with Data Owners and Data Stewards.<\/span>\u00a0<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tPlan interim evaluations<\/a>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\t

Evaluate the framework regularly. Gather feedback from Data Stewards, Data Owners, data\u00a0consumers\u00a0and technical teams. Look not only at the content of the framework, but also at how practical it is to use.\u00a0<\/span>\u00a0<\/span><\/p>

However, a\u00a0framework can be complete on paper and still\u00a0fail to\u00a0align sufficiently with practice.\u00a0Therefore, interim evaluations help\u00a0identify\u00a0bottlenecks early and make it possible to adjust the approach in a focused way.<\/span>\u00a0<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

\n\t\t\t\t\t
\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/path><\/svg><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tCarry out periodic assessments <\/a>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\t

When a framework is already in place, it is important to periodically assess how far it is from daily practice.\u00a0<\/span>\u00a0<\/span><\/p>

For example, assess whether roles are\u00a0actually being\u00a0performed, whether Business Rules are understandable and considered valuable, whether Data Quality issues are followed up and whether monitoring leads to concrete improvements.<\/span>\u00a0<\/span><\/p>

An assessment shows where differences exist between the designed framework and its application in the business.\u00a0As a result, the organisation can work specifically on reducing these differences.<\/span>\u00a0<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\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

\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

From framework to practice <\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Ultimately, a\u00a0Data Quality Framework is an important starting point, but its value only\u00a0emerges\u00a0through application.\u00a0At the same time, success does not depend solely on the content of the framework. Clear responsibilities, engaged employees, understandable Business Rules and an approach that aligns with existing business processes are just as important.<\/span>\u00a0<\/span><\/p>

Therefore, start small, test regularly and continue developing the framework together with the business. A framework does not need to be perfect\u00a0immediately.\u00a0Above all, it\u00a0must be clear,\u00a0workable\u00a0and useful.<\/span>\u00a0<\/span><\/p>

Do you need support assessing the gaps between your framework and the business, or are you curious about how our Data Quality experience and knowledge can be applied within your\u00a0organisation? Are you finding it difficult to achieve Data Quality adoption? Feel free to contact us<\/a>.<\/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>\n\t\t","protected":false},"excerpt":{"rendered":"

Many theories, books and step by step plans describe how to start with Data Quality using a Data Quality Framework. It rightly feels like a milestone when this framework is on paper and ready for implementation. However, this is often where the biggest challenge begins: how do you bring\u00a0these\u00a0well considered plans into practice?\u00a0 A\u00a0framework can…<\/p>","protected":false},"author":29,"featured_media":24657,"template":"","categories":[148],"class_list":["post-24650","blog","type-blog","status-publish","has-post-thumbnail","hentry","category-data_quality"],"yoast_head":"\nA Data Quality Framework That Works in\u00a0Practice\u00a0 - Clever Republic<\/title>\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.cleverrepublic.com\/nl\/resources\/blog\/data-quality-framework\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Data Quality Framework That Works in\u00a0Practice\u00a0 - Clever Republic\" \/>\n<meta property=\"og:description\" content=\"Many theories, books and step by step plans describe how to start with Data Quality using a Data Quality Framework. 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