Data quality criteria: Simply explained

Data quality is important both personally and corporately. The criteria include accuracy and timeliness

That’s why data quality is critical

Whether in companies, research institutions or in everyday life – data is collected, analyzed and used everywhere to make informed decisions. In this context, the quality of this data is of greater importance.

• Foundation for Informed Decisions: If your data is flawed or incomplete, the decisions and analysis derived from it may be unreliable. This can lead to serious consequences, whether in terms of business strategies, medical diagnoses, or personal life decisions.

• Cost Savings:Businesses that rely on inaccurate data waste resources, lose customers, and risk legal issues. Investing in improving data quality usually saves money in the long run.

• Trust and credibility: presenting poor quality data significantly affects trust in your organization or project.

• Compliance and Data Protection: In many industries and countries, there are legal requirements related to data protection and compliance. Poor quality data can violate these requirements and result in legal consequences.

The criteria of data quality

Data quality includes various criteria that ensure data is reliable, accurate, and useful.

• Accuracy: Data should accurately reflect reality. This means that you need to minimize errors and inaccuracies. You can ensure this through careful data collection, validation, and maintenance.

• Completeness: Missing data often results in bias and unreliable results. Therefore, it is important to ensure that all relevant information is captured.

• Consistency: Consistent data is free of contradictions and harmonizes with each other. Contradictions and inconsistencies between different data sources or within the same database must be avoided. In this context, uniform formats and standards are crucial.

• Currentness: In many cases, data loses value over time. Therefore, you should verify that the data is as current as possible in order to make relevant decisions.

• Relevance: When you collect unnecessary data, you waste resources and compromise the quality of the overall data collection. Therefore, clearly define in advance what data is relevant to you and the purpose at hand.

• Uniqueness: Unique data is uniquely identifiable and avoids confusion. Duplicate records or missing key information can lead to confusion and reduce your data quality.