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Getting Data Ready for WebFOCUS

Getting Data Ready for WebFOCUS. March 22, 2012. Lucius McInnis, Systems Engineer – Client Services Group Kam Wong, Solutions Architect – iWay Software . Data Quality/Business Intelligence Lexicon. GOGO. 1960’s Dance Craze (Image: target.com). GIGI. GIGO. 1958 Romantic Musical

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Getting Data Ready for WebFOCUS

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  1. Getting Data Ready for WebFOCUS March 22, 2012 Lucius McInnis, Systems Engineer – Client Services Group Kam Wong, Solutions Architect – iWay Software
  2. Data Quality/Business Intelligence Lexicon GOGO 1960’s Dance Craze (Image: target.com) GIGI GIGO 1958 Romantic Musical (Image: imdb.com) Garbage-In-Garbage-Out
  3. Get Rid Of The Garbage… Access Cleanse Standardize Monitor Manage Accurate data promotes accurate information and decisions…
  4. When Business Data Is Not Managed ERRORS DUPLICATION CONFUSION
  5. AGENDA The Path from Data to Information Access to Data Data Quality Master Data Management/Data Synchronization Demonstration Revenue Generation Quality of Care/Service . Information Operations and Financial Mgmt. Fraud, Waste, and Abuse Risk, Compliance, and Governance
  6. Path from Data to Information
  7. Path from Data to Information #1
  8. Integration Approach – Start with an Integrated Infrastructure
  9. Pre-packaged Integration Components ERP/Financials Ariba I2 JD Edwards Lawson Manugistics Microsoft Oracle SAP Legacy Systems CICS IMS VSAM .NET Java TUXEDO MUMPS Industry ACORD CIDX HL7 RNIF SWIFT 1Sync Data Warehouse DB2 ETL Oracle/Essbase MS SSAS/OLAP Netezza SAP BW Teradata SFA/CRM Amdocs/Clarify BMC/Remedy MSDynamics Oracle/Siebel Salesforce.com SAP B2B Internet EDI Legacy EDI MFT Online B2B XML
  10. Enterprise Data Integration Scenario Reports Dashboards Data Integration Data Quality Data Sources …
  11. Path from Data to Business Intelligence #2
  12. The Business Value of Data Quality Improves customer-facing processes: Promotes accurate client address and household information Enables advanced analysis: Facilitates the use of data-mining, market predictions, fraud detection, and future client value Credit and behavioral scoring: Helps financial institutions improve risk management - Basel Capital Accord III (2010) Assists healthcare organizations: Develop an Enterprise Master Patient Index (EMPI) leveraging connectivity to legacy systems and databases
  13. Data Quality Center – Profiling Profiling – Technical (Pre-built) Basic Analysis Minimums Maximums Averages Counts Etc. Patterns / Masking Extremes Quantities Frequency Analysis Foreign Key Analysis Profiling – All Charting Grouping / Aggregate Drilldown / Interactive Displays
  14. Parsing data parsed into components (pattern based) Standardization transformation into standard format (Jim Smith -> James Smith) standard and nonstandard abbreviations (Str. -> Street) language-specific replacements Data quality validation validation against rules validation against reference tables Large number of domain oriented algorithms Address Party Vehicle Name Identification number Credit Card number Bank account number Extension by custom validation steps using complex function and rules including Levensthein distance SoundEx internal (java-based) functions Data Quality – Cleansing
  15. Unification identification of the candidate groups company address person product …etc. Deduplication best representation of the identified subject golden record creation Identification new data entries – to identify subject (person, address, etc.) to which the new record is connected (matched) Fuzzy logic and scoring Same name + same address Same name + similar address Similar name + same address Similar name + similar address Complex business rules using sophisticated algorithms and functions including Levenstheindistance Hamming distance Edit distance Data quality scores values Data stamps of last modification Source system originating data Data Quality – Match & Merge
  16. Data Quality: Issue Management
  17. Data Quality Issue Management
  18. Issue Tracker Portal – Workflow Management
  19. Issue Tracker Portal – Issue Resolution (1)
  20. Issue Tracker Portal – Issue Resolution (2)
  21. Path from Data to Business Intelligence #3
  22. Moving Towards MDM from Data Quality Matching: Identification, linking related entries within or across sets of data. Merging: Creation of the golden data based on one or several reference source and rules. Propagating:Update other systems with Golden Data if required. Monitoring: Deployment of controls to ensure ongoing conformance of data to business rules that define data quality for the organization.
  23. Source Source Master Source Source MDM Architectures Source Source Consolidated Master is Single Version of Truth Data Quality at Master Updates occur at Sources Updates propagated to Master Master Source Source Registry Style Multiple Versions of Truth Data Quality is Ongoing Updates occur at Sources Keys and Metadata in Registry Updates propagated to other Sources Other Styles Supported
  24. Project Successes – Pathway to Maturity Getting to MDM – “Golden Data” Start with Data Profiling Understand the data you have Identify inconsistencies in the data Disseminate the information about the data quality Continue with Data Quality Validate, standardize and cleanse for purpose Automate the process De-duplication (Match & Merge) End with Master Data Synchronize with closed loop feedback integration Provide a single view for all stake holders Implement Data Governance – Issue Tracking
  25. Demonstration
  26. Data Management Life-Cycle
  27. Thank You! - Questions? iWay Software Because Everything Should Work Together. WebFOCUS Because Everyone Makes Decisions.
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