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iWay Data Quality Center Essentials

iWay Data Quality Center Essentials. Introduction. Objectives: Discuss the concept of data quality Examine the capabilities of the iWay Data Quality Center Demonstration with WebFOCUS. What is Data Quality?.

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iWay Data Quality Center Essentials

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  1. iWay Data Quality Center Essentials

  2. Introduction Objectives: • Discuss the concept of data quality • Examine the capabilities of the iWay Data Quality Center • Demonstration with WebFOCUS

  3. What is Data Quality? • Data quality is the measure of data accuracy, completeness, and consistency across a business.

  4. Address Business issues • No ‘holistic’ customer view • How many customers do we have? • Which piece of data is accurate? • Multiple views of a customer • Fraud detection • Credit card • Tax evaders • Problems in regulatory reporting • Marketing campaigns not effective • Geo-marketing not possible • Extensive duplicate in mail campaigns

  5. Solve Data Quality Problems • Data is invalid or missing • Variant spellings of names • Incomplete or incorrect addresses • Duplicates created within systems and across systems • Not all master data captured at first contact • Data may be corrected on one system but not on others

  6. Information Flow

  7. Data-quality Methods: Profiling • Process of gathering statistics about enterprise data. • Effective means of obtaining in-depth understanding of corporate data

  8. Data-quality Methods: Cleansing

  9. Data-quality Methods: Enriching

  10. Data-quality Methods: Match and Merge

  11. What is iWay Data Quality Center? • Tool for complex data quality management • Designed to evaluate, monitor, and manage data quality in different information systems as well as prevent incorrect data from entering

  12. iDQC Capabilities • Centralized management of all data-quality activities • Bundled administration tools • A platform-independent architecture • Parallel processing methods • Advanced semantic profiling • Ability to easily access external data sources • A set of algorithms that efficiently perform approximate matching in record unification, regardless of internal data structures.

  13. Supported Data-quality Management Methods • Data Profiling • Data Cleansing • Data Enrichment • Match and Merge

  14. Business Example: Analysis

  15. Business Example: After Automation

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