Duplicate Check: Clean and find duplicates
You want to clean up your addresses, find duplicate records and thus achieve a higher quality of your master data?
You can clean up all your existing addresses in your master data with the Duplicate check module. Find duplicate records easily Transfer them to your CRM system or ERP system.
Overview of the module Duplicate Check
- Cleaning up typos and “superfluous” characters.
- Standardization of data, such as “street” or company name
- Separation of fields for data transfers
- Finding duplicate entries (deduplication)
- Finding similar entries or swapped entries
- Rule-based checking
Why should you clean up your addresses?
Typing errors, name changes or the merging of several address databases (e.g. by consolidating applications) can lead to duplicate or incorrect addresses. Not only an inaccurate number of actual addresses, but also erroneous addresses due to mailing or duplicate mailing of documents can cause an evaluation or higher costs. In our many years of experience in the ERP application environment, we have often come into contact with these issues and have more than once performed a cleanup individually. We would now like to try to consolidate this with this module. Whether we can solve all cases with it, your feedback will show.
You are already a customer of ours and use the ID Validation? Then you get this module for free! You can also test the module first without a valid license. However, not all rules and checks are available in the test version.
Mode of operation
1. normalization of the data
We disassemble and reassemble your data. For example, we recognize the difference between
u. Co. KG and
& Co. KG. A street is a
street and also a
street without a dot and a
street with a dot.
After normalization, we have a street in which a GmbH & Co. KG trades.
Using this example, which is just one of many, we will put your data into the correct format. This will make the next step easier.
2. rule-based duplicate check
There is a large number of fields, such as Company, Name, Name1, Name2, Addition, Street with and Street without house number, etc.
Depending on the selected rules, the following examples are different or identical companies, or yet only one department:
Sample Company Ltd. Sample Street 1 12345 Sample Village
same address with department
Sample Company Ltd. Controlling Sample Street 1 12345 Sample Village
same address with department and misspelling
Sample Company Ltd. Controlling Sample Street 1 12345 Sample Vllage
3. batch processing
With the help of batch processing, you can set up a CSV, JSON or XLSX export from your ERP system and then have all the master data it contains checked directly with the duplicate check.
Select the file to be imported or drag and drop it onto the ID Validation and you can check the entire file. After a relatively short time you will have checked each record for existing duplicates or cleaned the record.
4. your decision
We will only provide you with the overview, if necessary also the cleaned data in the same form as your imported file. Now you can decide. If you use Microsoft Excel, you can filter the data according to rules, view the results and hand them over to those responsible for master data management.
We assume no liability for any data loss that may occur due to the use of the Duplicate Check module.