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High data quality across the company through error-tolerant matching and duplicate checking of all data objects in bulk mode
Consistent, correct and duplicate-free customer data is not only a must for companies, a uniformly high quality of all company data is also the prerequisite for lean and flexible business processes in all parts of the company, reliable forecasts and reports as a basis for efficient corporate planning and controlling as well as legally secure, documentable compliance.
The error-tolerant matching of any desired business data in one or more databases is indispensable for this, because duplicate data stored in a variant form not only increases the IT costs, it also provides inaccurate information which can to lead incorrect business decisions. Efficient operative business activities are also inhibited if the relevant data has to be manually searched for and corrected first of all.
dataMatch is preferentially used in all situations, in which duplicate objects in business data have to be identified via unformatted fields (text data). The special analysis and search capabilities play a particular role in the case of text fields, such as article or product descriptions, books or music titles, authors or performers, job site or project descriptions. In this respect, dataMatch always offers you the possibility of executing error-tolerant duplicate matching by means of a variety of search attributes combined with error tolerance (fuzzy logic, phonetics, synonyms, …), so that you can also safely identify duplicates in the event of typing, hearing or reading errors.
With dataMatch, Uniserv offers a high-performance tool for matching all business data and increasing the data quality across the company. Unlike mailBatch, which processes customer data by means of a standardized knowledge base for names and addresses, dataMatch uses dictionaries, similarity, decomposition and context rules as well as other analysis parameters which are defined sector- and project-specifically.
Amongst other things, dataMatch supports you in the following:
With dataMatch, you increase the data quality in all business applications:
dataMatch is ideal for use within the framework of the Uniserv DQ Batch Suite and is eminently suitable for matching extensive data stocks from various sources. In conjunction with dataRetrieval, the Uniserv solution for efficient real-time searching in any desired company data, dataMatch puts you on the shortest route to consistent and duplicate-free data with the highest data quality.
With dataMatch, Uniserv offers a high-performance tool for matching all business data and increasing the data quality across the company. Unlike mailBatch, which processes customer data by means of a standardized knowledge base for names and addresses, dataMatch uses dictionaries, similarity, decomposition and context rules as well as other analysis parameters which are defined sector- and project-specifically.
Amongst other things, dataMatch supports you in the following:
- Identifying and preventing duplicates
- Merging information about existing data from different sources
- Enhancing data records with additional information by matching them against external lists.
With dataMatch, you increase the data quality in all business applications:
- MDM systems - e.g. for matching product data in Product Information Management (PIM)
- CRM systems - e.g. for matching sales opportunities and articles
- ERP systems - for matching product and material master data
- SCM/procurement systems - for matching product and material master data supplemented e.g. by terms and conditions and supplier data
- Data migration and integration - matching of any type of business data
dataMatch is ideal for use within the framework of the Uniserv DQ Batch Suite and is eminently suitable for matching extensive data stocks from various sources. In conjunction with dataRetrieval, the Uniserv solution for efficient real-time searching in any desired company data, dataMatch puts you on the shortest route to consistent and duplicate-free data with the highest data quality.
