Details of these 5 steps can be found in our practical guide.
- Framework
- Data Profiling
- Data Cleaning and, if necessary, Data Enrichment
- Sustainability through Data Governance Rules
- Monitor
When we consider data quality, we often think of cleaning data (deduplication, incomplete data enrichment, harmonization, 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 in creating business value.
Data quality is not an occasional process, it is a continuous endeavor, that should be automated as much as possible, with human expertise, to have complete, homogeneous, integrated, useful and up-to-date data at all times.
