1. Accuracy
Definition: The degree to which customer data correctly represents real-world entities or facts.
Example: A customer’s phone number and email address must be accurate to ensure successful communication.
Measurement Method:
- Cross-reference customer data with authoritative sources (e.g., government databases, third-party validation tools).
- Compute the percentage of records with verified fields: Accuracy (%)=Number of Correct RecordsTotal Records×100\text{Accuracy (\%)} = \frac{\text{Number of Correct Records}}{\text{Total Records}} \times 100
2. Completeness
Definition: The extent to which all required customer data fields are populated.
Example: Missing customer email addresses in a CRM system reduce the dataset’s completeness.
Measurement Method:
- Identify mandatory fields (e.g., first name, last name, email).
- Calculate the proportion of non-empty mandatory fields: Completeness (%)=Number of Populated FieldsTotal Required Fields×100\text{Completeness (\%)} = \frac{\text{Number of Populated Fields}}{\text{Total Required Fields}} \times 100
3. Consistency
Definition: The uniformity of data values across the dataset or related systems.
Example: A customer’s address listed in two different systems (e.g., billing and shipping) must match.
Measurement Method:
- Compare overlapping fields across systems or within the dataset.
- Compute the percentage of consistent records: Consistency (%)=Number of Consistent RecordsTotal Records×100\text{Consistency (\%)} = \frac{\text{Number of Consistent Records}}{\text{Total Records}} \times 100
4. Timeliness
Definition: The degree to which customer data is up-to-date and available within a required timeframe.
Example: A customer’s contact information should reflect recent updates, such as address changes.
Measurement Method:
- Identify records with outdated information based on timestamps or update logs.
- Calculate the timeliness rate: Timeliness (%)=Number of Up-to-Date RecordsTotal Records×100\text{Timeliness (\%)} = \frac{\text{Number of Up-to-Date Records}}{\text{Total Records}} \times 100
5. Uniqueness
Definition: The extent to which data contains no duplicate entries.
Example: Duplicate customer records with the same email address but different IDs can cause operational inefficiencies.
Measurement Method:
- Identify duplicate entries using unique identifiers (e.g., email, phone number).
- Compute the uniqueness rate: Uniqueness (%)=Total Records – Duplicate RecordsTotal Records×100\text{Uniqueness (\%)} = \frac{\text{Total Records – Duplicate Records}}{\text{Total Records}} \times 100
