Functions for data cleansing.

Inconsistent master data is a risk for the entire company, because data is the raw material for information. Knowledge comes from information and this knowledge forms the basis for strategic business decisions, from which in turn corporate goals are derived and business processes are modelled. So anyone who wants to operate successfully in the market needs high-quality data!

Turn your data into a success factor for your company! We'll show you how.

Identity resolution: identifying and removing redundant records.

Anyone who is concerned with data cleansing in a CRM or a data warehouse project or with list management tasks in direct marketing is inevitably confronted with the issue of address duplicates and duplicate matching and merging. This does not just concern cost minimization in mailings. CRM and ERP can only be successful if unambiguous identification of the customer is guaranteed.

Data Quality on Demand enables you to remove duplicates and prevent new ones. You match and merge your data quickly and simply. You select the level at which the links are created. In the case of company addresses, you can choose between "company" and "contact" as a reference level; you link private addresses according to "household" or "person".

Address cleansing: analysis, optimization, correction.

Incorrect or missing postal information is not usually detected until it is too late, i.e. goods, correspondence or consignments are returned as undeliverable and customers complain and are annoyed.

With Data Quality on Demand from Uniserv, you profit from the best possible automatic assignment rate through sophisticated algorithms. In this respect, country-specific rules and delivery procedures are considered, and you have access to all the knowledge bases for each country - for analyses, determination of similarities and phonetics.

Assignment of the title code: analysis, categorization, standardization.

An incorrect form of address annoys addressees and damages the image of the company. Incorrect or missing attributes also prevent the systematic exploitation of additional information and lead to incorrect analysis and incorrect assignment to target groups.

Data Quality on Demand analyses name elements and supplies the title code and more. Each database can be broadly classified according to private individuals, company addresses with and without contact persons, male or female. The first name, last name, title and designations are also differentiated. The title code is determined on the basis of first names, titles and job titles.
This function is available as standard for German addresses. Please inform us if you wish to check other countries.

Bank data check: ensuring payment.

The IBAN, the International Bank Account Number, replaces the sort code and account number from February 2014, thereby ensuring standardized data formats in the Single Euro Payments Area (SEPA). Fines and annoyed business partners are the inevitable result if your bank data is not in a suitable format.

Our bank data check enables you to meet the challenges associated with SEPA. You can easily, comfortably and effectively validate and convert the bank account details of your customers.

The solution converts the data into the new format and establishes whether the IBAN is syntactically correct, all the requirements for creating an IBAN are met and the IBAN can be unambiguously assigned to a bank. Typing errors and transposed digits are therefore reliably prevented. You append any missing information on the bank, such as the complete bank name, sort code, BIC, postcode or place, with the reference data of Swift, the Society for Worldwide Interbank Financial Telecommunication.