Components of a Semantic Enterprise

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A presentation given by Gary Carlson and Christine Connors at the 2010 Semantic Technology Conference in San Francisco.

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  • Components of a Semantic Enterprise

    1. 1. COMPONENTS OF A SEMANTIC ENTERPRISE Gary Carlson, Gary Carlson Consulting Christine Connors, TriviumRLG LLC Semantic Technology Conference San Francisco, CA ◆ June 23, 2010
    2. 2. GARY CARLSON Gary Carlson brings over 20 years of experience as a taxonomist, consultant, project manager, product manager, and information manager working for small to Fortune 100 companies. The past ten years have been spent helping organizations boost revenue, customer satisfaction and efficiency via well executed information and knowledge management initiatives. He has worked extensively on major information and knowledge management projects and products spanning taxonomy tools, search, auto-categorization, expert systems, content management, governance and overall information infrastructure. Gary is currently focused on helping companies develop their information infrastructure to meet enterprise requirements.
    3. 3. CHRISTINE CONNORS Ms. Connors amassed extensive experience in knowledge-base design and development prior to forming TriviumRLG. Global Director, Semantic Technology Solutions, Dow Jones • Platform taxonomies, ontologies and metadata research and development • Business Champion, Synaptica® • Dow Jones Consulting (partner) Knowledge Architect, Intuit • Online content management • Semantic search Metadata Architect, Raytheon Company • Enterprise knowledge representation • Enterprise search • Large-scale taxonomies, metadata schema and rules-based classification Cybrarian at CEOExpress.com • Cataloging and classification of web-based content
    4. 4. ACME President & CEO Business Unit 1 Business Unit 2 Business Unit 3 President President President HR HR HR IT IT IT Finance Finance Finance ENTERPRISE What is it?
    5. 5. ACME President & CEO Business Unit 1 Business Unit 2 Business Unit 3 President President President HR HR HR IT IT IT Finance Finance Finance ENTERPRISE A large organization with multiple business units sharing a common mission
    6. 6. U.S. Navy Photo by Photographer’s Mate 3rd Class Douglass M. Pearlman ENTERPRISE Solving concrete problems
    7. 7. http://galaxywire.net/knowledge-base/space-shuttle-enterprise/ ENTERPRISE Cutting edge problems
    8. 8. © 1968 Paramount Pictures ENTERPRISE Future-thinking, innovative
    9. 9. TECHNOLOGY
    10. 10. • Boolean search tools • Entity extraction • Rules-based classification • Bayesian search algorithms
    11. 11. • Metadata schemes • Taxonomies • Ontologies ‣ Inferencing ‣ Reasoning
    12. 12. CAN MACHINE AND HUMAN BASED SYSTEMS WORK TOGETHER?
    13. 13. • They must! • Humans don’t scale • Machine-learning still evolving • Many techniques already are a hybrid • ontologies • rules-based classification
    14. 14. CASE STUDY: ECOMMERCE
    15. 15. WHERE WE STARTED • Home grown systems didn’t scale • Organic taxonomy not meeting needs • Lack of data governance • Multiple disparate systems which didn’t play nicely with each other • General understanding that change was required but unable to justify the initial expense
    16. 16. PROJECT DRIVERS • Drive revenue • Semantics allow for flexible relation of products • “More products to the people” • Support brand health • A common semantic model used across all content types greatly increased the ability to re-use content, expose the company’s message and expertise • Increase operational efficiency • A common semantic model allowed for much easier and flexible information integrations • New channels or modification of existing channels was much easier to accomplish with the common model
    17. 17. PROJECT INPUTS Source Details In depth analysis of the existing information and the processes that Existing information deliver it to the website In depth analysis of the application and integration points storing and IT Infrastructure managing the information Internal workflows & Review of the content creation and editorial process with an governance emphasis on areas where data consistency could be improved Analysis of web analytics, customer surveys, customer comments, Market & customer research overall industry trends Industry best practices Incorporation of lessons learned and best practices in the industry Internal expertise of the Extensive interviews with employees who are “on the ground” as employees these insights are often quite valuable Review of any legal implications or requirements in the content Legal creation and publication process
    18. 18. SOLUTION - SUCCESS METRICS • Increased revenue caused by a larger number of people getting to product pages • Reduced involvement of developer/production resources in updating of content or relationships between content • Reduce the number of customers leaving the site because they could not find what they were looking for • Increasein the number of paths that a customer can take to get to products
    19. 19. INTERESTING TAKE-AWAYS • Modeling was not the hard part • Existing systems, workflows, staffing expertise and reports made it exceedingly difficult and expensive to consider a full “ontology” approach • The hard part was politics, inertia and demonstrating a significant increase of functionality over a “traditional” approach
    20. 20. GOALS MET WITH SEMANTIC TECHNOLOGIES • Flexible information usage to drive revenue • Sophisticatedbusiness rules to drive revenue and personalization • “Semantic integration” to support integrations between systems • Exposure of a common model to search, navigation, BI, etc.
    21. 21. HURDLES OF A “SEMANTIC SOLUTION” • Expertise mismatch • High bus factor • New developer skills required • Expectation mismatch • Lots of excellent future functionality was recognized, but the enterprise was still having trouble managing a flat list of vendor names • Tool mismatch • Most tasks were quite simple and could be done in MS Excel • One of the most important “taxonomies” was 12 terms • Existing systems were unable to interact with sophisticated models
    22. 22. CASE STUDY: TARGETED CONTENT DELIVERY
    23. 23. WHERE WE STARTED Kbase Media Readers • Duplicating content for rigid pre-defined channels • Inconsistent terminology to define content attributes • “If we share our content, you won’t need us anymore” mindset
    24. 24. PROJECT DRIVERS • Re-use/re-purpose content assets in multiple delivery channels to maximize ROI • Reduce expenditures on recreating existing assets • Protect intellectual property / reduce risks of copyright violation • Present a more cohesive brand across business units
    25. 25. PROJECT INPUTS Source Details Existing information Analysis of the existing information and information structures Analysis of the application and integration points storing and IT Infrastructure managing the information Internal workflows & Review of the content creation and editorial process. Identify governance opportunities for aligning terminology and managing metadata. Analysis of inter- and intra-business unit project & product goals to Goals find opportunities for alignment and re-use Industry best practices Incorporation of lessons learned and best practices in the industry Internal expertise of the Extensive interviews with front line employees and subject matter employees experts (SMEs) Legal Review legal, regulatory and contractual obligations for content use
    26. 26. HOW WE DID IT • Centrally managed metadata • Metadata Schema • Taxonomies • “Light-weight” ontologies • Entity extraction • Find subjects, people, companies, places, products, goals • Rules-based classification • Define those entities, and add more precise tags with which distribution and search tools can deliver the most relevant content • Federated, faceted search & delivery tools • Tested UI/UX for ease of use and precision of results
    27. 27. WHAT WE ENDED WITH Tagging EE RBC I/R CMS/DAM Human Machine NLP QA Processing Repository Query layer Multimedia Mobile Kbase Text
    28. 28. SUCCESS METRICS • Increase in content reuse • Decrease in content re-creation • Decreased time to deliver • Improved workflow efficiency • More cohesive branding across products
    29. 29. MODELING IS NOT THE HARD PART
    30. 30. CHALLENGES • Systems integration • Auditing • Politics • Human Resources • Regulations • Usability • Business Processes • Maintenance • Change Management • Growth
    31. 31. A HYBRID, STANDARDS- BASED SYSTEM HAS THE GREATEST INTEGRITY
    32. 32. THE COMPONENTS • Integrated legacy and semantic systems • Repository-agnostic data models • Artificial intelligence tools • Defined workflows • Governance • User-focused interaction paradigms • Committed resource plan
    33. 33. QUESTIONS? THANK YOU! Gary Carlson, http://garycarlsonconsulting.com/contact.html Christine Connors, http://triviumrlg.com/contact

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