Isoiec25024pdf14

What is ISO/IEC 25024:2015 and why is it important for data quality

ISO/IEC 25024:2015 is an international standard that defines data quality measures for quantitatively measuring the data quality in terms of characteristics defined in ISO/IEC 25012. Data quality is the degree to which data meets the expectations of its users in a specific context of use [^1^]. Data quality measures are numerical values that indicate how well data satisfies a certain quality characteristic [^2^].

ISO/IEC 25024:2015 contains the following:

a basic set of data quality measures for each characteristic;

a basic set of target entities to which the quality measures are applied during the data-life-cycle;

an explanation of how to apply data quality measures;

a guidance for organizations defining their own measures for data quality requirements and evaluation.

The standard also provides informative annexes with tables of quality measure elements, quality measures associated to each quality measure element and target entity, considerations about specific quality measure elements, a list of quality measures in alphabetic order, and a table of quality measures grouped by characteristics and target entities [^2^].

ISO/IEC 25024:2015 is important for data quality because it provides a common framework and terminology for defining, measuring, and evaluating data quality. It can help organizations to:

establish data quality requirements and specifications;

assess the current state of data quality and identify improvement opportunities;

monitor and control data quality throughout the data-life-cycle;

demonstrate compliance with data quality standards and regulations;

enhance user satisfaction and trust in data.

The standard can be applied to any kind of data retained in a structured format within a computer system used for any kinds of applications. People managing data and services including data are the primary beneficiaries of the quality measures. The standard is intended to be used by people who need to produce and/or use data quality measures while pursuing their responsibilities, such as acquirers, evaluators, developers, maintainers, suppliers, users, quality managers, and owners [^2^].

Examples of data quality measures

Data quality measures are numerical values that indicate how well data satisfies a certain quality characteristic. Different data quality dimensions may require different types of measures to capture their essence. For example, completeness can be measured by the percentage of missing values, while accuracy can be measured by the percentage of correct values. Some data quality measures are common across different dimensions, such as frequency, distribution, and standard deviation.

Here are some examples of data quality measures for each dimension:

Dimension

Definition

Example measure

Completeness

The degree to which all required data is present.

Percentage of missing values.

Accuracy

The degree to which data correctly reflects the real-world objects or events it represents.

Percentage of correct values.

Validity

The degree to which data conforms to the syntax, structure, and rules of its domain.

Percentage of values that match a predefined format or pattern.

Consistency

The degree to which data is coherent and compatible across different sources and systems.

Percentage of values that are identical across different datasets.

Uniqueness

The degree to which data does not contain duplicate or redundant entries.

Number of distinct values.

Timeliness

The degree to which data is available or updated within a suitable time frame for its use.

Average age or latency of data.

Fitness for purpose

The degree to which data meets the expectations and requirements of its users in a specific context of use.

User satisfaction score or feedback.

Data quality measures can help organizations to monitor and improve their data quality over time. However, they are not sufficient by themselves. Data quality measures need to be accompanied by data quality rules, standards, policies, and processes that define the expected level of data quality and how to achieve it. Data quality tools can help organizations to automate and streamline their data quality management activities. aa16f39245