TOLERANT Tools for data stewards
Simple answer:
Because TOLERANT, as a data quality professional, knows what a Herculean task clean data can be.
That is why we have developed special tools for this task. With these tools you will achieve better data quality in just three steps:
Curious?
Reserve your personal appointment for a web session now.
We will show you how easy data quality is with TOLERANT.
1.
Find weak points
The first step is to get a clear picture of the current situation. This will help you identify where you need to clean up data and whether there are systematic sources of errors. With our tools, you can reliably measure the essential quality characteristics of customer data:
- Quality of postal addresses
- E-mail and telephone numbers
- names
- duplicates
- Moved and deceased customers
2.
Clean up
For data cleansing, we combine automated and manual steps. Fully automated functions:
- Standardize postal addresses and mark incorrect addresses.
- Determine relocation addresses and update addresses
- Standardize telephone numbers and e-mail addresses and remove unnecessary content
- Check the plausibility of salutations and add them
- Isolate academic titles from name fields
- Enrich missing characteristics (e.g. commercial register, industry)
- Merge secure duplicates
For manual cleansing, our tools identify the data records to be checked:
- Ambiguous addresses
- Suspected cases of false names
- Incomplete data records requiring additions
- Duplicate candidates
3.
Prevent
After the cleanup, your data should remain permanently clean. That’s why we provide you with the tools you need to eliminate systematic sources of error. You can also use them to validate and complete data entries at an early stage. The most important tools and utilities:
- Fast, error-tolerant customer search that delivers reliable results even with deviating spellings
- Validate and complete addresses as they are entered
- Validate phone numbers and e-mail addresses
- Duplicate check before storage
- Enrichment of company characteristics directly during data entry
- Plausibility check of names
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