Data quality
The challenges of data quality
- How to measure the databases quality?
- Are the tools and methods used operational for all types of databases (IMS, relational, XML...)?
- What are the risky programs regarding the database?
- What is the impact of data non quality on the organisation?
- What are the criteria's used to declare an erroneous data ?
- What are the corrections to be provided?
- How to correct an erroneous data?
- How to prevent new errors?
- Is it possible to define different intervention levels regarding data quality?
- Will the control and validation process consume many resources of the production system?
How can REVER help you measure and improve your data quality?
- By measuring the database compliancy with the data model (obtained by remodelling).
- By providing tools for all types of databases: flat files, hierarchical (IMS), network (IDS2), relational, XML ...
- By allowing measurements on a dedicated and independent engine
- By a progressive process depending on the intervention level
- By allowing new data rules addition into the data model, allowing thus a data quality improvement process.
- By automating to a maximum the process
