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

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

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

When Reporting Data Goes Unmanaged? ERRORS CONFUSION DUPLICATION

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

The Path from Data to Business Intelligence

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

Path from Data to Business Intelligence #1

Integration Approach – Start with an Integrated Infrastructure

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

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

Path from Data to Business Intelligence #2

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

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

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

Data Quality: Issue Management

Data Quality Issue Management

Issue Tracker Portal – Workflow Management

Issue Tracker Portal – Issue Resolution (1)

Issue Tracker Portal – Issue Resolution (2)

Path from Data to Business Intelligence #3

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.

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

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

Demonstration Copyright 2007, Information Builders. Slide 25

Data Management Life-Cycle

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