Duplicate registrations is a known problem. It requirer extra maintenance effort and causes problems in the provision of information. This article describes how DX detects and corrects duplication.
In dit artikel wordt beschreven welke gegevens het Stelsel van Basisregistraties bevat, hoe u (c.q. uw organisatie) kan aansluiten en hoe u de gegevens kan toepassen.
Everyone can imagine what is meant by the term data quality, but it is difficult to say exactly when that quality is high or low. Thomas Redman provides a useful definition in his book Data Driven (http://www.dataversity.net/contributors/thomas-redman/): data quality is high if the data in question is suitable for its intended use in business activities, decision-making […]
Als datamigratie specialist komen we het maar al te vaak tegen: De opdrachtgever start een implementatietraject, waar datamigratie een noodzakelijk onderdeel van is. Zonder naar de daadwerkelijke brondata te kijken wordt aangenomen dat deze data voldoet aan de algemeen geldende business rules. De migratiestrategie en migratiespecificaties worden op basis van deze aanname (en daarmee theoretische situatie) opgesteld. Vervolgens wordt de migratieprogrammatuur gebouwd en worden de eerste test- en proefmigraties gestart. En wat blijkt: ontzettend veel uitval!
You use data to test data processing software. In most test environments, testers are not allowed to work with production data, which is why they usually use a data set created specifically for testing purposes. This article deals with anonymising data.
100% success is required when migrating large amounts of data. Success is defined by the fact that the data has been transferred correctly and that the target system works as expected. Still, how can you be sure that the data migration was successful? How do you know that from the set of hundreds of thousands […]
This case study describes how a complex data migration was 100% tested using a reconciliation solution. 100% testing means 100% coverage: all non-technical data fields of all records of all tables must be compared before and after the data migration. Custom practice with data migration is to test a representative sample survey, covering 5% or less and only the most critical data fields. Sample surveys, counters and hash codes are insufficient to exclude all possible mismatches and (un)intentional exchanges in the data. 100% coverage is necessary for critical business data, e.g. customer and contract data.
