.: DataQualityTools (en) 4.05

   DataQualityTools is a collection of tools to help businesses improve the quality of their databases. The central components are a series of functions which help to find duplicate records and, above all, a function for error-tolerant doublet search based on postal addresses. This allows you, in the case of direct marketing, to avoid double solicitations and the redundant maintenance of customer addresses and other records. Of course, this saves significantly on your expenses. But it also improves the outward image of your company. And through the consideration of advertising black lists, you can also avoid trouble with advertising recipients who do not wish to receive advertising.

Here is a brief overview of the features of DataQualityTools:

- Fast, largely automated and thus easy to use comparison of address databases for double addresses. The postal address (error-tolerant matching), telephone number, email address, address or customer number and tax number can all serve as matching criteria.
- Option to compare with blacklists and the enhancement of an address database using the comparison results with data from a second address database.
- User-defined universal matching, that can be used for any form of data.
- Numerous options for the further processing of the matching results: beside the direct deletion in the source file, it is also possible to flag the results in the source file, or to process further using a stored procedure, a duplicate-, results- or archive-file.
- A project history, which delivers information about what was done when with a given project.
- Various functions for the preparation of address- and other databases, such as a case correction, a function to replace terms, and a function to delete individual records systematically.

And much more ...

Available in four editions.

Remark:
Not every one of the functions described above is available in the Professional Edition (USD 498).

Screenshot:	Download Now File size 16.60 MB Buy Now
deduplication, dedupe, list cleaning, data quality,
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