1. Lucius McInnis, Systems Engineer – Client Services Group Kam Wong, Solutions Architect – iWay Software March 22, 2012 Getting Data Ready for WebFOCUS 1

2. Data Quality/Business Intelligence Lexicon 2 GI GO GI GO Garbage-In-Garbage-Out 1960’s Dance Craze (Image: target.com) 1958 Romantic Musical (Image: imdb.com)

3. Get Rid Of The Garbage… 3 Access Cleanse Standardize Monitor Manage Accurate data promotes accurate information and decisions…

4. 4 ERRORS CONFUSION DUPLICATION When Business Data Is Not Managed

5. AGENDA 5 Fraud, Waste, and Abuse Operations and Financial Mgmt. Information Risk, Compliance, and Governance Revenue Generation Quality of Care/Service. The Path from Data to Information Access to Data Data Quality Master Data Management/Data Synchronization Demonstration

6. Path from Data to Information 6 Infrastructure Allow for access to dataAllow for access to data Real-Time and Batch Information MovementReal-Time and Batch Information Movement ReusabilityReusability DataQuality Allow for Real-Time Data QualityAllow for Real-Time Data Quality Correct Data Quality issues before they propagateCorrect Data Quality issues before they propagate Master Data Management Centralize the management of informationCentralize the management of information Control the information throughout to organizationControl the information throughout to organization

7. Path from Data to Information 7 Infrastructure Allow for access to dataAllow for access to data Real-Time and Batch Information MovementReal-Time and Batch Information Movement ReusabilityReusability #1

8. Integration Approach – Start with an Integrated Infrastructure 8

9. Pre-packaged Integration Components 9 SFA/CRM  Amdocs/Clarify  BMC/Remedy  MSDynamics  Oracle/Siebel  Salesforce.com  SAP Data Warehouse  DB2  ETL  Oracle/Essbase  MS SSAS/OLAP  Netezza  SAP BW  Teradata B2B  Internet EDI  Legacy EDI  MFT  Online B2B  XML ERP/Financials  Ariba  I2  JD Edwards  Lawson  Manugistics  Microsoft  Oracle  SAP Industry  ACORD  CIDX  HL7  RNIF  SWIFT  1Sync Legacy Systems  CICS  IMS  VSAM .NET  Java  TUXEDO  MUMPS

10. Enterprise Data Integration Scenario 10 … Data Sources Data Integration Data Quality Reports Dashboards

11. Path from Data to Business Intelligence 11 DataQuality Allow for Real-Time Data QualityAllow for Real-Time Data Quality Correct Data Quality issues before they propagateCorrect Data Quality issues before they propagate #2

12. The Business Value of Data Quality 12 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 13 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. Data Quality – Cleansing 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

15. Data Quality – Match & Merge 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 Levensthein distance Hamming distance Edit distance Data quality scores values Data stamps of last modification Source system originating data

16. 16 Data Quality: Issue Management

17. Data Quality Issue Management 17

18. Issue Tracker Portal – Workflow Management 18

19. Issue Tracker Portal – Issue Resolution (1) 19

20. Issue Tracker Portal – Issue Resolution (2) 20

21. Path from Data to Business Intelligence 21 Master Data Management Centralize the management of informationCentralize the management of information Control the information throughout to organizationControl the information throughout to organization #3

22. Moving Towards MDM from Data Quality 22 1.Matching: Identification, linking related entries within or across sets of data. 2.Merging: Creation of the golden data based on one or several reference source and rules. 3.Propagating: Update other systems with Golden Data if required. 4.Monitoring: Deployment of controls to ensure ongoing conformance of data to business rules that define data quality for the organization.

23. MDM Architectures 23 Master is Single Version of Truth Data Quality at Master Updates occur at Sources Updates propagated to Master Master Source Consolidated Registry Style Master Source Other Styles Supported Multiple Versions of Truth Data Quality is Ongoing Updates occur at Sources Keys and Metadata in Registry Updates propagated to other Sources

24. Project Successes – Pathway to Maturity 24 1.Start with Data Profiling Understand the data you have Identify inconsistencies in the data Disseminate the information about the data quality 2. Continue with Data Quality Validate, standardize and cleanse for purpose Automate the process De-duplication (Match & Merge) 3. End with Master Data Synchronize with closed loop feedback integration Provide a single view for all stake holders Getting to MDM – “Golden Data” 4. Implement Data Governance – Issue Tracking

25. 25 Demonstration

26. 26 Data Management Life-Cycle

27. Thank You! - Questions? 27 iWay Software Because Everything Should Work Together. WebFOCUS Because Everyone Makes Decisions.