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SAP Data Services delivers a solution to help analyze, cleanse, and match all types of data -  customer, supplier, product, or material data, structured or unstructured – to ensure highly accurate and complete information anywhere in the enterprise.

The data quality framework and process can be applied in many scenarios as data is entered, updated, or moved within an organization – almost any time data is touched. The most common scenario is basic data entry when employees add or update new entities, during which process errors can occur. In data migration projects, information from the source isn’t always accurate and may not map seamlessly to the new location or target.

Data cleansing is the process of parsing, standardizing and correcting name, firm, and operational data (e.g product, financial, etc.).

Duplicate records often exist in one or more source systems, so the goal of data matching is to determine whether records refer to the same entity. This involves evaluating how well the individual fields, or record attributes, match each other.

Matching algorithms can help correct data entry errors, character transposition, and other data errors to match records. You can set rules based on combinations of various elements matching at a certain threshold - for example, you may require the address line information and the first-name information to match in order for records to be flagged as a possible match.

Once matches have been identified, data from these matched groups can be salvaged and posted to form a single best record, or posted to update all matching records.

Data Quality reports and dashboards can be used to continuously monitor and govern your information assets.

A blueprint is a sample SAP Data Services job that is configured with best practices to solve a specific scenario. Each blueprint is an end-to-end job that includes sample data and may be run in the customer environment with only a few modifications. Some jobs include batch data flows and some include real-time data flows; some jobs include party data and some include product data.

Data Quality Blueprints

  • Sample jobs configured to illustrate best practice settings for common Data Quality use cases involving party data when the data consists of multiple countries or in specific countries
  • Miscellaneous jobs configured to illustrate best practice settings for specific Data Quality matching use cases
  • Sample jobs configured to illustrate the use of Text Data Processing in conjunction with Data Quality
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