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iWay Solutions - EIM

iWay Solutions - EIM . Vincent Deeney – Solutions Architect 6/25/2009. Information Builders Agenda. Information Builders and iWay Software Technology Overview Demonstration Use-Case Technical Demonstration. iWay Software.

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iWay Solutions - EIM

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  1. iWay Solutions - EIM Vincent Deeney – Solutions Architect 6/25/2009

  2. Information BuildersAgenda • Information Builders and iWay Software • Technology Overview • Demonstration Use-Case • Technical Demonstration

  3. iWay Software • Integration of all content –data warehouse, files, queues, CMS, documents, email, ERP, CRM, SFA. • Support spectrum of all integration patterns • ETL, SOA, B2B, MFT etc. • Integrated data quality and master data management • Enrich and feed business events and data extracts to operational data stores, data warehouses, applications, enterprise search engines • Enable real-time dashboards, scoring and analytics through embedded complex event processing

  4. Technology OverviewiWay Software Enterprise Integration

  5. A typical problem • Receiving data in multiple formats from external partners (csv, txt, edi, etc.) • Data of varying quality. • Lack of visibility into full process (end-to-end). • Various Manual Steps

  6. Upstream Data • Data/Information Enters from Multiple Points • Manual Data Entry • B2B Gateway • Call Center • Self-Service Portal • EIM Issues • Accuracy • Completeness • Business Rule Validation • Correlation

  7. In-stream Data • Data is a Flowing, Dynamic thing • Complex Processes • Derived Data • Evolving Semantics • Operational BI • EIM Issues • Error Detection and Correction • Lost or Mismatched Information • Duplication • Validation as Evolves

  8. Downstream Data • Data is collected, manipulated, and analyzed • DM/DW/Cubes/Analytical BI • Performance Management • Compliance • Auditing • EIM Issues • Access • Accuracy • Completeness • Mismatched Semantics

  9. Enterprise Information Management Requirements • Single View • Data Quality • Master Data Management • Operational Data Store • Customer Data Integration • Citizen Services • Master Patient Index • Product Information Management • Real Time Data Warehouse

  10. Master Data Management Defined • MDM for customer data systems are software products that: • Support the global identification, linking and synchronization of customer information across heterogeneous data sources • Create and manage a central, database-based system of record • Enable the delivery of a single customer view for all stakeholders • MDM architectural styles vary in: • Instantiation of the customer master data — varying from the maintenance of a physical customer profile to a more-virtual, metadata-based indexing structure • The latency of customer master data maintenance — varying from real-time, synchronous, reading and writing of the master data in a transactional context to batch, asynchronous harmonization of the master data across systems • An MDM program potentially encompasses the management of customer, product, asset, person or party, supplier and financial masters.

  11. MDM Architecture – Coexistence • Master is Single Version of Truth • Data Quality is Ongoing • Updates occur at Sources or Master • Updates propagated to other Sources Source Source Master Source Source

  12. MDM Architecture – Consolidation • Master is Single Version of Truth • Data Quality at Master • Updates occur at Sources • Updates propagated to Master Source Source Master Source Source

  13. MDM Architecture – Registry • Multiple Versions of Truth • Data Quality is Ongoing • Updates occur at Sources • Keys and Metadata updated in Registry • Updates propagated to other Sources (Optional) Source Source Master Source Source

  14. MDM Architecture – Centralized • Master is Single Version of Truth • Data Quality at Master • Updates occur at Master • Updates propagated to Sources Source Source Master Source Source

  15. iWay Solution

  16. “Dirty Data” issues Missing Data Problems : Inconsistent Formats Incorrect Data Duplicate Information

  17. Data Cleansing

  18. Data Cleansing

  19. Data Cleansing

  20. Data Cleansing

  21. Live Demonstration

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