[Practical Guide] Your Data Quality process in 5 steps

Details of these 5 steps can be found in our practical guide.

When we consider Data Quality, we often think of cleaning data (deduplication, incomplete data enrichment, harmonisation, deletion of obsolete data). But be careful, this is only the tip of the iceberg! The upstream (definition of rules, audits, etc.) and downstream phases (sustainability and monitoring) are also essential to start a sustainable approach to create business value.

Data Quality is not an occasional process, it is a continuous endeavour, that you should automate as much as possible and complete with human expertise, to have complete, homogeneous, integrated, useful and up-to-date data at all times.