{"id":1917,"date":"2026-01-12T19:20:16","date_gmt":"2026-01-12T18:20:16","guid":{"rendered":"https:\/\/www.yourdata.de\/datenqualitatsmanagement\/"},"modified":"2026-10-22T16:47:49","modified_gmt":"2026-10-22T14:47:49","slug":"data-quality-management","status":"publish","type":"page","link":"https:\/\/www.yourdata.de\/en\/data-quality-management\/","title":{"rendered":"Data quality management \u2013 Data quality check"},"content":{"rendered":"\n
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High data quality is the foundation for effective processes, well-informed decisions and sustainable customer relationships. Yet many companies are unaware of the true state of their data quality. Our data quality check sheds light on the situation \u2013 systematically, transparently and with a clear outcome. We analyse your customer data against key criteria such as completeness, consistency, plausibility, redundancy and timeliness. In doing so, we identify weaknesses and draw up specific recommendations for action. This provides you with the basis for targeted optimisation measures \u2013 regardless of whether you use a CRM, ERP or bespoke systems.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Requirements for a sustainable data quality check<\/h2>\n\n\n\n
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<\/path><\/svg><\/span>Completeness<\/strong><\/span><\/h3>\n\n\n\n

All mandatory fields (e.g. name, address, contact details) must be completed. Missing values are detected and flagged as incomplete records.<\/p>\n<\/div>\n\n\n\n

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<\/path><\/g><\/svg><\/span>Accuracy<\/span><\/h3>\n\n\n\n

Data must be factually correct and plausible (e.g. valid email addresses, existing postcodes, syntactically correct telephone numbers).<\/p>\n<\/div>\n\n\n\n

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\n \n\n <\/path>\n<\/svg><\/span>Absence of redundancy<\/span><\/h3>\n\n\n\n

Duplicate detection ensures that each customer is recorded only once in the system. Similar entries (e.g. those with slightly different spellings) are merged or flagged for review.<\/p>\n<\/div>\n\n\n\n

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\n \n <\/path>\n<\/svg><\/span>Timeliness<\/span><\/h3>\n\n\n\n

Data must be checked regularly to ensure it is up to date (e.g. to identify out-of-date addresses, inactive email addresses or expired contracts).<\/p>\n<\/div>\n\n\n\n

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\n \n <\/path>\n<\/svg><\/span>Consistency<\/span><\/h3>\n\n\n\n

Data must not contradict each other within the system (e.g. country and dialling code must match, and title and gender must correspond).<\/p>\n<\/div>\n\n\n\n

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\n \n <\/path>\n<\/svg><\/span>Integrity<\/span><\/h3>\n\n\n\n

Links to other data objects (e.g. assignment to a sales region, customer category or contact person) must be valid and exist.<\/p>\n<\/div>\n\n\n\n

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<\/path><\/g><\/svg><\/span>Logging<\/span><\/h3>\n\n\n\n

To ensure a sustainable process, all vulnerabilities identified and the actions taken to address them must be documented.<\/p>\n<\/div>\n\n\n\n

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\n \n <\/path>\n<\/svg><\/span>Prioritisation<\/span><\/h3>\n\n\n\n

Quality defects must be assessed according to their business relevance and prioritised for subsequent processing.<\/p>\n<\/div>\n\n\n\n

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<\/path><\/g><\/svg><\/span>Action-oriented approach<\/span><\/h3>\n\n\n\n

Action-oriented: The results must identify specific, actionable measures for checking and correcting data.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Our structured audit process<\/h2>\n\n\n\n
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Prepare the analysis<\/strong><\/div>\n\n\n\n
Defining systems, data sources and objectives<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Check quality<\/strong><\/div>\n\n\n\n
Data profiling based on technical and subject-matter criteria<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Summarise the results<\/strong><\/div>\n\n\n\n
Prioritise defects, create a visualisation<\/div>\n<\/div>\n<\/div>\n\n\n\n
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To make recommendations<\/strong><\/div>\n\n\n\n
Identify measures for optimisation<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Submit a report<\/strong><\/div>\n\n\n\n
Documentation with dashboards and a management summary<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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Your benefits at a glance<\/h2>\n\n\n\n
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    Objective quality status of your data<\/div>\n\n\n\n
    Transparent assessment rather than subjective judgement.<\/div>\n<\/li>\n\n\n\n
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    Rapid identification of vulnerabilities<\/div>\n\n\n\n
    A clear overview of critical data fields and areas requiring action.<\/div>\n<\/li>\n\n\n\n
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    Audit-proof documentation<\/div>\n\n\n\n
    A clear and comprehensible analysis with a robust presentation of results.<\/div>\n<\/li>\n\n\n\n
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    Cross-system analysis<\/div>\n\n\n\n
    A comprehensive overview of your data landscape.<\/div>\n<\/li>\n\n\n\n
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    Clear recommendations for action<\/div>\n\n\n\n
    Concrete, prioritised measures rather than theoretical concepts.<\/div>\n<\/li>\n\n\n\n
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    Can be expanded in a modular fashion to include data cleansing and monitoring<\/div>\n\n\n\n
    A direct transition to operational optimisation is possible.<\/div>\n<\/li>\n\n\n\n
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    Minimal effort for your IT and business departments<\/div>\n\n\n\n
    Efficient analysis using a clearly structured approach.<\/div>\n<\/li>\n<\/ul>\n<\/div>\n\n\n\n
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    120<\/div>\n\n\n\n
    Monthly audit reports<\/div>\n<\/div>\n\n\n\n
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    10 Mio<\/div>\n\n\n\n
    Customer data<\/div>\n<\/div>\n\n\n\n
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    24<\/div>\n\n\n\n
    Assessment criteria\/KPIs<\/div>\n<\/div>\n\n\n\n
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    80 %<\/div>\n\n\n\n
    Improvement rate<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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    Find out what your data has to offer.<\/h2>\n\n\n\n

    The data quality check is the first step towards becoming a data-driven organisation. It highlights where action is needed \u2013 and how you can prepare your data for CRM, BI, automation and digital customer processes. We would be happy to support you in the next step, whether that involves operational data cleansing or setting up a continuous quality monitoring system.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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    Contact<\/strong><\/h2>\n\n\n\n

    Let\u2019s work together to measurably improve your data quality \u2013 please get in touch.<\/p>\n\n\n

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