A grip on data quality

Everyone can imagine what is meant by the term┬ádata 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 […]

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Datamigratie begint met Data!

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!

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Realistic and safe test data through anonymisation

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.

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The 7 proofs of a 100% data migration

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 […]

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100% Reconciliation of a Data Migration

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.

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