Data Quality Management :

Do you know that the cost of data non-quality can represent up to 25% of a company’s operational profit?

In order to optimise the quality of your data, our experts base their methodology on both best practices and experience. Our know-how in DQM allows us to select the tools which respond the best to our clients needs in term of costs and functionalities.

A DQM project is not only about dealing with the quality of the data. It is as well, and foremost, about improving your systems of information.

With the support of reliable data, your systems will keep their promises and give you better results.
Data Quality Management is therefore most of all a business issue. The technical aspects are in the hands of our partners - among the best actors on the market - thereby guaranteeing the success of your projects without restraint.
We take the data quality management in charge by dealing the sense of the data, and thereby we guarantee the success of your project.

We analyse and correct your data in order to improve their overall quality and normalisation.

Our methodologies lie on the data quality best-practices which are based mainly on five approaches:

• Data Profiling, the exploration of the data, root of a project’s success.
• Data Quality, standardisation and validation, in order to homogenize the data to understand and control it.
• Data Integration, consolidation for identifying redundancies.
• Data Enrichment, data evolution in order to enlarge the knowledge significantly.
• Data Monitoring, maintenance of data quality within duration.

As clients and projects are heterogeneous, we present targeted offers among which our clients choose according to their needs:

• Profiling (meta-data capture)
• Parsing
• Cleansing (data cleaning)
• Matching (links identification and merging of entries)
• Array -Matching (cross-comparison)