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Data quality is high on the agenda of many organisations. Which is very understandable because poor (or unknown) data quality has quite a few consequences. Examples include reputational damage, making poorly substantiated decisions or producing unreliable reports. Many business processes depend on the quality of the data. Plenty of reason therefore to take data quality seriously!
Data quality is a critical success factor in data migration. Data quality is therefore always something that needs to be discussed and investigated during any data migration. Knowing data quality is crucial in determining a migration strategy. Also, data migration is an excellent opportunity to clean up, enrich and standardise the data and to eliminate double entries. DX therefore recommends using a data migration to improve data quality at the same time.
Why customers opt for DX
Experience
150+ data migration projects successfully completed
Tooling
Specialist tools for cleaning, enriching, standardising and eliminating double entries
Domain knowledge
We know everything about Finance, Utilities, Telecommunications, Government, Healthcare and Education
Five-step approach
- Data intake & strategy. A data intake provides insight into the main (technical) properties of the (meta-)data. The results from the data intake serve as input for the next steps.
- Establishing the standard. Data quality can only be measured if the standard is known. What are your expectations regarding the content and is 100% a requirement, or is 80% good enough? We define business rules and set the desired standards by means of a number of structured workshops.
- Measuring data quality. We create insight into the extent to which data deviates. The data is analysed for any quality issues using smart tools. The findings determine the scope for the next step: improving.
- Improving data quality. Data that does not meet the standard is cleaned, enriched, standardised and/or double entries are removed. These issues are tackled partly with the help of automated conversion rules and partly manually.
- Monitoring. Continuous measurements mean that you gain insight into the developments and trends of your data quality. Any structural problems can be tackled directly at the source.
