Data Quality Tools

The five key measures to assess data quality are accuracy, completeness, consistency, uniqueness, and validity.

1. Accuracy: The correctness of data, ensuring it aligns with real-world values. 

2. Completeness: How much of the necessary data is present.

3. Consistency: Data uniformity across disparate sources. 

4. Uniqueness: Each data point is distinct, avoiding duplicates. 

5. Validity: If the data adheres to predefined rules or formats.

Data profiling is a valuable initial step, identifying completeness and accuracy issues.

A more comprehensive approach involves using a Data Quality Assessment Framework (DQAF). DQAF examines data against specific quality standards.

Organizations can further refine their data quality assessment through custom data quality metrics, such as data-to-error ratio, empty values, percentage of dark data, or time-to-value ratio.

The most commonly recognized four dimensions of data quality are:

1. Accuracy: The data correctly reflects the real-world values it represents.

2. Completeness: All necessary data is present.

3. Consistency: Data may be pulled from different sources, yet it is processed into a consistent format for analysis.

4. Timeliness: Data is available when needed and is up-to-date.

The six C's provide a framework for assessing and improving your data. The six C’s of data quality are:

1. Current: Data is up-to-date and relevant.  

2. Complete: All necessary data is present.  

3. Clean: Data is free of errors, inconsistencies, and duplicates.

4. Consistent: Data is uniform across sources and systems.

5. Credible: Data is reliable and trustworthy.  

6. Compliant: Data adheres to regulatory standards.