Problems that it solves

The activities that ensure Data Quality must be part of any information project. Migrations to a new DB or a new system, BI, Analytics, DWH, among others, owe a large part of their success to the presumption of having quality data.

In the data quality projects that we carry out, we will obtain data groups guaranteeing correctness standards without duplication and accompanied by tests that ensure quality.

Main Features

Data Quality projects begin by defining the sources to be analyzed and, the scope of the analysis, then the analysis and auditing of the data to be studied are carried out. In the analysis phase, the quality rules are generated and will be applied during the construction phase of the validators. With the closure of the project, a model is implemented which "monitors" the quality of the data analyzed, this can monitor the quality of the data daily, preventing potential problems by incorporating new data that does not have the quality analysis.

Return of investment

Decisions made on correct data and decision-making systems based on correct data generates substantial savings. In addition, in the processes of implementation of models and systems, saves a lot of time since the resulting data dictionary reduces the time of implantation of data loads by 6 times.