There are plenty of data quality issues related to phone numbers in party master data. Despite that a phone number should be far less fuzzy than names and addresses I have spend lots of time having fun with these calling digits.
- Completeness – Missing values
- Precision – Inclusion of country codes, area codes, extensions
- Reliability – Real world alignment, pseudo numbers: 1234.., 555…
- Timeliness – Outdated and converted numbers
- Conformity – Formatting of numbers
- Uniqueness – Handling shared numbers and multiple numbers per party entity
You may work with improving phone number quality with these approaches:
Here you establish some basic ideas about the quality of a current population of phone numbers. You may look at:
- Count of filled values
- Minimum and maximum lengths
- Represented formats – best inspected per country if international data
- Minimum and maximum values – highlighting invalid numbers
National number plans can be used as a basis for next level check of reliability – both in batch cleansing of a current population and for an upstream prevention with new entries. Here numbers not conforming to valid lengths and ranges can be marked.
Also you may make some classification telling about if it is a fixed net number or cell number – but boundaries are not totally clear in many cases.
In many countries a fixed net number includes an area code telling about place.
Match and enrichment:
Names and addresses related to missing and invalid phone numbers may be matched with phone books and other directories having phone numbers and thereby enriching your data and improving completeness.
Then you of course may call the number and confirm whether you are reaching the right person (or organization). I have though never been involved in such an activity or been called by someone only asking if I am who I am.