Data enrichment is one of the core activities within data quality improvement. Data enrichment is about updating your data in order to be more real world aligned by correcting and completing with data from external reference data sources.
Traditionally data enrichment has been a follow up activity to data matching and doing data matching as a prerequisite for data enrichment has been a good part of my data quality endeavor during the recent 15 years as reported in the post The GlobalMatchBox.
During the last couple of years I have tried to be part of the quest for doing something about poor data quality by moving the activities upstream. Upstream data quality prevention is better than downstream data cleansing wherever applicable. Doing the data enrichment at data capture is the fast track to improve data quality for example by avoiding contact data entry flaws.
It’s not that you have to enrich with all the possible data available from external sources at once. What is the most important thing is that you are able to link back to external sources without having to do (too much) fuzzy data matching later. Some examples:
- Getting a standardized address at contact data entry makes it possible for you to easily link to sources with geo codes, property information and other location data at a later point.
- Obtaining a company registration number or other legal entity identifier (LEI) at data entry makes it possible to enrich with a wealth of available data held in public and commercial sources.
- Having a person’s name spelled according to available sources for the country in question helps a lot when you later have to match with other sources.
In that way your data will be fit for current and future multiple purposes.