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Semantic Media Wiki Approach To Metadata
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Semantic Media Wiki Approach To Metadata

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Presentation given at EDW 2012 (Atlanta)

Presentation given at EDW 2012 (Atlanta)

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  • 1. Semantic MediaWikiApproach to MetadataScott E. ThompsonManager - Data ArchitectureOntario Teachers’ Pension Plan
  • 2. 2 Agenda 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Why? Mashup of slides I’ve used before… – What is Semantic MediaWiki? – Proof of Concept – The Unexpected Wrap Up Questions
  • 3. 3 pinterest.com/thompland777 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up SELECT ?Person WHERE { ?Person :hasExperience :Semantic Technologies . ?Person :hasExperience :Meta Data. ?Person :hasExperience :Capital Markets }
  • 4. 4 Ontario Teachers’ Pension Plan 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Fixed Income Public Equities Private Capital Real Estate Infrastructure Foreign Currency Commodities Hedge Funds
  • 5. 5 The Challenge: Metadata 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 6. 6 Current: Low Confidence 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up 42? ETL Correct Trade IT Data Warehouse Reload Reload Data Rerun Report
  • 7. 7 Future: Nirvana 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 8. 8 Business Requirements 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Findability of Data Ownership of Data Data Quality Consistent Business Terminology Added later… Ownership of Metadata Metadata Quality
  • 9. 9 Business Requirements 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Value of Meta Data & Meta Data Tool • Allows business users / end users to gain the required insight into what the data and reports they are looking at means • Makes data available and visible to others • Creates a searchable set of information about the firm’s data. This allows data developers and users to search for existing data and avoid data duplication. • Provides a platform for sharing and publicizing data. This reduces the workload of developers (interfaces, reports, etc.) and users and increases efficiency. • Quality control, data restrictions and uses can be applied to the entire data set. • Metadata documentation transcends people and time. Staff turnover and balancing of multiple projects can be mitigated with metadata, providing data permanence and the documentation of institutional knowledge.
  • 10. 10 MDM? 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up MDM could stand for Master Data Management or Meta Data Management… coincidence? “Lets go get all the key pieces of data and put them in one place, which is really more of an enterprise data warehouse but master data management then says… it’s almost a map… here is what each of those data fields are, here is how you can find them, here is what they mean, here is where they came from.” Blake Johnson Consulting Professor Stanford University “The Truth and Power of Master Data Management” (Teradata) http://www.youtube.com/watch?feature=player_embedded&v=p6VHpIlDfu4#!
  • 11. 11 One Truth? 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Pre-Trade Post-trade Investment Portfolio Trade & Strategy & Research & Deal Securities Collateral &Cash Portfolio Planning Analytics Management Operations Management Accounting V = f(trade, market context, model, business context) Trades Reconciliation Trades Market Context Market Context Model Model Trades Business Context Business Context Market Context Model Business Context Total Fund Reporting Market Credit & Counterparty Liquidity Risk Risk Risk Performance Compliance Management Management Management
  • 12. 12 What is a Wiki? 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Hawaiian for “quick” Allows large numbers of people to create and edit the same content Effective for reaching a credible consensus from a large group Wikipedia is the world’s largest collaboratively edited source of encyclopedic knowledge
  • 13. 13 What is the Semantic Web? 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 14. 14 MediaWiki (Web 2.0) 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 15. 15 Semantic MediaWiki (Web 3.0) 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 16. 16 Future Opportunities 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Simple search algorithms would suffice to provide a precise answer to the question…
  • 17. 17 Faceted Search 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 18. 18 Graphs (relate/infer) 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up otpp:Index-Linked Bond subClassOf otpp:Debt f As eO same ubt yp p:s otp dbpedia: otpp:Fixed-Rate Bond subClassOf otpp:Debt Inflation-Linked Bond otpp:Amortizing subClassOf otpp:Index-Linked Bond Index-Linked Bond otpp:Index-Linked <sameAs> dbpedia:Inflation Bond Linked Bond
  • 19. 19 Who Needs Consistency? 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 20. 20 Linked Open Data Graph (OLD) 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 21. 21 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up FIBO
  • 22. 22 Proof of Concept 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Build a knowledgebase about: Our structured data (schemas, tables, columns) Our business terminology (business process, products, attributes) Prove that the technology could: • Automatically load technical metadata and relate it with business metadata • Customize workflow to collect and govern the manual business input
  • 23. 23 Data Architecture Ontology 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Schema Group BelongsToA Instances: TOOLKIT CORE PRODUCT Schema FUNCTIONAL BUAD IsPartOfA Instances: ACCT MREF MKT FIQR Table Instances: Table1 Table2 View1 View2
  • 24. 24 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 25. 25 Data Management Ontology 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Table sA ha hasDataOwner ha sD at aS A tew ha s ar d Organizational Quality State Group Instances: Instances: User Investment Division – Asset Mix & Risk Authoratative Finance Division – Data Management SLA Instances: SLA1 SLA2
  • 26. 26 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 27. 27 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 28. 28 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 29. 29 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 30. 30 Workflow 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 31. 31 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 32. 32 Product Attribute Ontology 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up CallsA ReferencesA Product Group Stored Procedure belongsToA Table ha hasA sD M Q Product ua l it yT Quality Test es t Instances : Missing Stale Null Value Column m Comparative Fr o ata ha Tolerance sD ge t sAtt Changed ribu e t Focus on this data entry form Product Attribute Metadata to be curated by DM Metadata to be curated by AM &R
  • 33. 33 % Sourced from Core Schemas? 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up {{#sparql: SELECT DISTINCT ?Product ?Product_attribute ?Column ?Schema WHERE { ?Product property:HasAttribute ?Product_Attribute . ?Product_attribute property:GetsDataFrom ?Column . ?Column MDM:belongsToSchema ?Schema . } |merge=true|link=all}}
  • 34. 34 Data Management Indexes 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 35. 35 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 36. 36 It’s a New Kind of Database! 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up
  • 37. 37 SMW+ in a nutshell 1. Why? 2. SMW? 3. The PoC 4. The Unexpected 5. Wrap Up Semantic MediaWiki MediaWiki WYSIWYG extension Enhanced Retrieval Extension Deployment Framework Web Server
  • 38. “The smartest organizations are notthose with the smartest people butthose with the quickest access to theircollective knowledge”- Rod Collins (wiki-management.com)