Catalog
concept#Data#Analytics#Data Quality#Quality Assurance

Data Quality Dimensions

Data quality dimensions are important criteria for assessing data quality in organizations.

Data quality dimensions assist organizations in systematically assessing and improving the quality of their data.
Established
Medium

Classification

  • Medium
  • Business
  • Architectural
  • Intermediate

Technical context

Data management systemsAnalytics toolsReporting systems

Principles & goals

Continuous improvement of data qualityTransparent data management processesInvolvement of stakeholders
Iterate
Enterprise

Use cases & scenarios

Compromises

  • Incorrect data interpretations
  • Overestimation of quality assurance
  • Lack of user acceptance
  • Conduct regular data reviews
  • Document data quality policies
  • Train all relevant employees

I/O & resources

  • Existing datasets
  • Defined quality metrics
  • Technical infrastructure
  • Insights into data quality
  • Optimization strategies
  • Action instructions

Description

Data quality dimensions assist organizations in systematically assessing and improving the quality of their data. They encompass various aspects such as accuracy, completeness, and consistency.

✔

  • Increased data integrity
  • Better decision-making
  • More efficient data management

✖

  • Dependence on high-quality data sources
  • Not all dimensions are always relevant
  • Requires continuous monitoring

  • Data Error Rate

    Percentage of erroneous data in the total dataset.

  • Completeness Metrics

    Metrics that assess the completeness of data.

  • Data Reusability

    Assessment of how well data can be used in different contexts.

Application at Company A

Company A has achieved significant improvements through the implementation of data quality dimensions.

Data Cleansing Project at Company B

Company B has elevated its data quality through a targeted data cleansing project.

Monitoring Approach at Organization C

Organization C has established an effective monitoring system to track its data quality metrics.

1

Evaluating the current data situation

2

Defining quality dimensions

3

Implementing the defined measures

⚠️ Technical debt & bottlenecks

  • Legacy data management tools
  • Insufficient data storage solution
  • Insufficient data integration options
Data errorsLack of documentationLegacy systems
  • Misuse of data through incorrect application
  • Ignoring the recommendations for data quality
  • Insufficient training leading to errors
  • Lack of adaptation to changes
  • Underestimating training needs
  • Ignoring user feedback
Knowledge in data governanceExperience with data analysisFamiliarity with relevant technologies
Compliance with data protection regulationsTechnological trends in data managementBusiness strategic requirements
  • • Legal regulations
  • • Technological infrastructure
  • • Resource allocation