Notre vision

Poor data quality has a direct impact on your corporate performance and gives rise to costs or lessened profitability for your organization.

An incorrect customer address is a reminder letter or a fruitless sales visit. This has a direct impact on your ROI and campaign turnover.

Not knowing the legal ties between two customers could lead to the launch of litigation for a minor debt against the subsidiary of a loyal and strategic customer.

Incomplete or non-consolidated partner information can give rise to negotiations based on faulty assumptions or agreements with insolvent suppliers.

Cost control
Incomplete data on the real state of purchasing or delivery processes prevent cost control from detecting possible overbilling by suppliers.

Customer benefits

Data quality can be affected by different factors such as the growing quantity of data to be managed, widely differing source data, different application silos, human input errors …

To counteract these effects, Data Quality Management (DQM) offers a set of tools and best practice active throughout the data life cycle, so as to ensure their quality.

A Data Quality Management approach enables profit generation, from one or several axes, defined in relation to the needs of the organization.


Our DQM approach is based on a sustainable process. We propose a series of initiatives ranging from data measurement, correction and quality control to the implementation of data governance best practice.

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