1. Lucius McInnis Technical Account Manager Eastern Area New York User Forum Getting Data Ready for WebFOCUS August 10, 2011

2. Cooking Food On the GRILL! Cleansed Marinated/Rubbed Well cooked Serve to family and friends

3. Data access Cleanse Standardize Monitor Manage Your Data Needs Attention Also!! REPORT

4. When Reporting Data Goes Unmanaged? ERRORS CONFUSION DUPLICATION

5. Agenda  The Path from Data to BI  Access to Data  Data Quality  Master Data Management/Data Synchronization  Demonstration Intelligence Knowledge Information Data Business Intelligence Data For Analysis GAP Standardization Cleansing Data profiling

6. The Path from Data to Business Intelligence

7. Path from Data to Business Intelligence #1 #3 #2

8. Path from Data to Business Intelligence #1

9. Integration Approach – Start with an Integrated Infrastructure

10. Pre-packaged Integration Components 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

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

12. Path from Data to Business Intelligence #2

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 Copyright 2007, Information Builders. Slide 13

14. Data Quality – Cleansing  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  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. 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 Step 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  Master is Single Version of Truth  Data Quality at Master  Updates occur at Sources  Updates propagated to Master  Multiple Versions of Truth  Data Quality is Ongoing  Updates occur at Sources  Keys and Metadata in Registry  Updates propagated to other Sources Master Source Consolidated Registry Style Master Source Other Styles: Supported

24. Project Successes – Pathway to Maturity 1. Start with Data Profiling  Understand the data you have  Identify inconsistencies in the data  Disseminate the information about the data quality Getting to MDM – “The Golden Record” 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 4. Implement Data Governance – Issue Tracking

25. Demonstration Copyright 2007, Information Builders. Slide 25

26. Data Management Life-Cycle

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