Data Quality

The most successful applications or algorithms are ineffective if the underlying data is false or incomplete. The work of completion is time-consuming and must refer to well- bounded methodologies to avoid waste of energy.

In the area of ​​data quality the concept of optimum makes sense as the 100% is often difficult to achieve and unnecessary. Companies must then adopt cycle methodologies.

We implement continuous improvement processes in two times : the first time to allow users to start analyzing their data as quickly as possible by ensuring a minimum level of quality. The second time is the improvement phase targeting the opening of additional functionality initialy suspended or the increase of the results accuracy.

We also intervene in data audit and optimization of operations for the delivery of the expected results.