High data quality creates sustainable added value by:

• enabling more efficient processes,
• reducing error costs,
• supporting better decisions,
• increasing user or customer satisfaction.

• Introducing clear data governance: Defining guidelines for data management.
• Data maintenance and optimisation processes: Error checking and monitoring as early as data entry.
• Employee training: Raising awareness of guidelines and processes.
• Technical solutions: Using suitable tools to control and improve data quality.

Duplicates are double entries in databases caused by different spellings (e.g. “Müller” vs. “Mueller”). They are often the result of human error and can significantly impair data quality.

The main causes can be divided into four groups:

1. Organisational problems: No clear responsibility for data management.
2. Lack of data governance: No defined guidelines or checks when data is transferred.
3. Human error: Insufficient training and unclear processes lead to duplicates or inconsistencies.
4. Technical challenges: Unsuitable database structures or tools for data control.

Data is of high quality if it:

• is accessible and editable,
• is presented in a comprehensible, uniform and consistent way,
• is relevant, up to date and complete and can be used to create value,
• is consistent, credible and error-free.

Data quality is crucial for generating added value from data and making value-adding decisions. In the age of big data, it is essential that data is suitable and “fit for use” for the respective purpose.