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IWay Next Generation Data Quality Brent Bruin iWay Systems Architect.

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Presentation on theme: "IWay Next Generation Data Quality Brent Bruin iWay Systems Architect."— Presentation transcript:

1 iWay Next Generation Data Quality Brent Bruin iWay Systems Architect

2 One industry study estimated the total cost to the US economy of data quality problems at over US$600 billion per annum (Eckerson, 2002). Copyright 2007, Information Builders. Slide 2 iWay Software Next Generation Data Quality

3 Copyright 2007, Information Builders. Slide 3  Push, Pull, Event Based  Up-stream, In-stream and Down-stream processing  Integrated Data Quality, Data Profiling and Master Data Management  Integrated ETL and ESB Framework  Event Capture  Rich Data Profiling solution powered by WebFOCUS ‬ “I have to say that I like this approach. Not just in terms of the CEP engine but also the whole idea of embedding capabilities into an ESB. In effect, this is a turn-around from traditional approaches. Other vendors in this space primarily started as batch vendors and now offer real-time or near-real-time extensions but they are still basically batch products. iWay, on the other hand, has designed its EIM to target real-time requirements and, yes, it can do batch too. Companies whose primary requirements are for real- time processing could do worse than to take a good look at iWay EIM.” Philip Howard, Bloor Research

4 Data Quality Deployment Points Data Quality can be implemented in all information touch points.  Customer/Patient touch points  Application touch points  Up-Stream  In-Stream  Down-Stream

5 Upstream Data Data Enters from Multiple Points  Manual Data Entry  B2B Gateway  Call Center  Self-Service Portal EIM Issues  Accuracy  Completeness  Business Rule Validation  Correlation B2B Portal Call Center CRM FIN ERP

6 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 Order BOM InvoicePaymentReceipt Ship Notice

7 Downstream Data Data is collected, manipulated, and analyzed  DM/DW/Cubes/Analytical BI  Performance Management  Compliance  Auditing EIM Issues  Access  Accuracy  Completeness  Mismatched Semantics Order BOM Invoice Payment Receipt Ship Notice DM DW Cube

8 Data Quality Management Cycle Copyright 2007, Information Builders. Slide 8 ParsingParsing Format CorrectionFormat Correction Content EvaluationContent Evaluation Automatic CorrectionAutomatic Correction Content Based CleansingContent Based Cleansing StandardizationStandardization EnrichmentEnrichment UnificationUnification Duplication IdentificationDuplication Identification AssociationAssociation ProfilingProfiling Data Impact AnalysisData Impact Analysis Issue Causes IdentificationIssue Causes Identification KPI DefinitionKPI Definition Ongoing MonitoringOngoing Monitoring Deviance IdentificationDeviance Identification Monitoring & Reporting Data Understanding Data Cleansing Data Enhancement Business User IT Professional

9 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.

10 Why iWay?  Open and flexible  Integrates easily in any environment  Supports all architectural approaches and styles  One product  Data Profiling -> DQ -> MDM  Start small - grow big by enabling components  Modern  Architecture  Best practice methodology  Iterative approach and fast deployment  Components  Customers like it (Source: Information Difference)  Developers like it

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