1. Copyright 2009, Information Builders. Slide 1 Why Data Quality Matters Chris Bevilacqua iWay Solutions Architect

2. Copyright 2009, Information Builders. Slide 2 Copyright 2007, Information Builders. Slide 2 Data Quality is… The Cornerstone of Accurate BI

3. Copyright 2009, Information Builders. Slide 3 Stated another way: Copyright 2007, Information Builders. Slide 3 BI on bad data is a disaster!

4. Copyright 2009, Information Builders. Slide 4 What is Data Quality? Data quality measures: Accuracy Completeness Consistency Uniqueness Timeliness Validity

5. Copyright 2009, Information Builders. Slide 5 The Real Cost of Bad Data  More than 25% of critical data within large businesses is somehow inaccurate or incomplete. 1  Poor data quality costs the typical company at least 10% of revenue; 20% is probably a better estimate. 2  Poor quality customer data costs U.S. businesses $611 billion a year in postage, printing, and staff overhead.” 3 Your Company’s Revenue = $DDD/yr What is the cost of your bad data?

6. Copyright 2009, Information Builders. Slide 6 Here are a Few Approaches to Solving the Data Quality Problem….

7. Copyright 2009, Information Builders. Slide 7 We’ll get there…someday… Whether or not you believe in climate change, the business will change…

8. Copyright 2009, Information Builders. Slide 8 But when (really) will ALL your data be in the “One System”? We have One System…..To Rule Them All!

9. Copyright 2009, Information Builders. Slide 9 It works, but ends up being pretty messy… and did we mention change? We’ll build it ourselves… what we need, when we need it.

10. Copyright 2009, Information Builders. Slide 10 iWay EIM Delivers Data Quality

11. Copyright 2009, Information Builders. Slide 11 iWay EIM Delivers Data Quality

12. Copyright 2009, Information Builders. Slide 12 iWay Data Quality Center

13. Copyright 2009, Information Builders. Slide 13 iWay Data Quality Center 1. Best practices are “baked in” 2. Built from the ground up for data quality 3. True real-time capabilities 4. Robust international support 5. Pluggable into any topology Profiling Analysis Parsing Standardization Validation Pattern Matching Enrichment Record Matching Lookups Scoring Merging/Unification De-duplication

14. Copyright 2009, Information Builders. Slide 14 Address IdentifierAlter FormatApply ReplacementsApply Template Character Groups AnalyzerColumn AssignerConditionConvert Phone Numbers Create Matching ValueCreate Postal Address CZData Format ChangerData Quality Indicator Dbf File ReaderDictionary Lookup Generator Dictionary Lookup IdentifierDictionary Lookup Reader Erase Spaces In NamesExcel File ReaderExcel File WriterExtract Filter FilterFixed Width File ReaderFrequency AnalysisGenerate Fake RC CZ Get Birth Date From RC CZGet Person Type CZGroup AggregatorGuess Name Surname Incremental Manual Override Builder Indexed Table ReaderIntegration InputIntegration Output Intelligent Swap Name Surname Jdbc ReaderJdbc WriterJoin Kill Unsupported Characters LookupLookup BuilderLookup Reader Manual Override BuilderMatching Lookup ReaderMatching ValuesMultiplicative Guess Name Surname Multiplicative LookupMultiplicative Pattern Parser Multiplicative Regex Matching Multiplicative Validate Phone Number MultiplicatorPattern ParserProfilingRC Validator CZ RVN ValidatorRecord CounterRegex MatchingRelation Analysis Repository Key ConverterRepository ReaderRepository WriterRepositoryReader/2 RepositoryReader/3.0RepositoryReader/3.5Representative CreatorSIN Validator SQL ExecuteSQL SelectScoringScoring Simple Selective Matching Lookup Reader Selective TransliterateSelectorSimple Group Classifier SortSplit Out Trailing NumbersSplitterStatistics String LookupString Lookup ReaderStrip TitlesSwap Name Surname Table MatchingTail TrashingText File ReaderText File Writer TokenizerTransform Legal FormsTransliterateTrash UIR ADR Generator CZUnificationUnification ExtendedUnion Union SameUpdate GenderUpdate Person Type By IC RC CZ Validate Bank Account Number CZ Validate Birth Number UAValidate DICValidate EmailValidate IC CZ Validate ID CardValidate In RESValidate Phone NumberValidate RZ CZ Validate RZ SKValidate VINValue ReplacerWeb Lookup Word AnalyzerXml Writer 40+ DQ Focused Objects

15. Copyright 2009, Information Builders. Slide 15 Real World Use Case The Goal Services organization supporting the airline industry sells decision support information to the industry members. The Challenge Data Quality was adversely affecting the customer base satisfaction Data Quality was impacting new revenue generation opportunities The Strategy Profile analysis according to specific business validation rules Monitor rolling 13 month window comparison of monthly data profiles Accumulate and report analysis to data providers The Benefits Improves customer satisfaction and confidence in the information Increases reliability of the information as new data sources are added Documents and audits quality-control processes for customer review Reduces the dependency on human resources to detect and correct data quality issues

16. Copyright 2009, Information Builders. Slide 16 Real World Use Case The Goal Manufacturer Pharmacy Automation and Nursing Automation Platforms Data Synchronization and Data Quality Address parts and technicians being sent to wrong facilities The Challenge Four different systems Two MS SQL Server, 1 Oracle, 1 Progress databases Product, Shipping, and Customer data out of sync The Strategy Standardize, Cleanse Data across all systems Match and Merge Data and maintain ongoing integrity The Benefits Deliver Dynamic Single Views Prepare for an ultimate MDM initiative

17. Copyright 2009, Information Builders. Slide 17 Impact of Data Quality Address Data 36 % Naturally Correct 64 % Manual Attention

18. Copyright 2009, Information Builders. Slide 18 3 % Manual Attention Impact on Data Quality Address Data 61 % Automated Cleansing 36 % Naturally Correct +

19. Copyright 2009, Information Builders. Slide 19 Real World Use Case Goal Performance Management Business Intelligence Change Management Process The Challenge 100 Locations 14 Systems with out-of-sync master data The Strategy Cleanse, Standardize, Match Master Data Management – Directorate, Borough, Site, Service Type, Service Point, Team, Staff, Patient Master Data Governance Workflow The Benefits Dynamic organizational change to support strategic initiatives Complete visibility into performance of organization vs goals

20. Copyright 2009, Information Builders. Slide 20 Real World Use Case The Goal Major hospital group is building a Master Patient Index Need to bring in acquisitioned systems Cleanse, Standard, Deduplicate The Challenge Previously manually processed by hiring temporary staff Current phase projected to take temporary staff of 20 over 18 months The Strategy Automate the cleansing, matching and merging business rules Data Stewardship provides human oversight to automated process The Benefits Identifies the duplicate records according to very complex business rules Reusable rules for future phases Significantly reduced project time – from 18 down to 4 months. Over 400% ROI projected

21. Copyright 2009, Information Builders. Slide 21 iWay Data Quality

22. Copyright 2009, Information Builders. Slide 22 Your Data Data Quality Challenge Data Quality Profile No COST, No kidding!

23. Copyright 2009, Information Builders. Slide 23 Thank You