Components of a semantic enterprise

Transcription

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 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 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 ACME President & CEO Business Unit 1 President HR IT Finance Business Unit 2 President HR IT Finance Business Unit 3 President HR IT Finance Enterprise What is it?

5 ACME President & CEO Business Unit 1 President HR IT Finance Business Unit 2 President HR IT Finance Business Unit 3 President HR IT Finance Enterprise A large organization with multiple business units sharing a common mission.

6 U.S. Navy Photo by Photographer s Mate 3rd Class Douglass M. Pearlman Enterprise strong, nimble, team

7 Enterprise cutting edge, explorers

8 1968 Paramount Pictures Enterprise future-thinking, innovative

9 Technology

10 Boolean search tools Entity extraction Rules-based classification Bayesian search algorithms

11 Metadata schemes Taxonomies Ontologies Inferencing Reasoning

12 Can machine and Human based systems work together?

13 They must! Humans don t scale Machine-learning still evolving Many techniques already are a hybrid ontologies rules-based classification

14 Case Study: ecommerce

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 Project Drivers Drive revenue Semantic 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 Project INputs Source Existing information IT Infrastructure Internal workflows & governance Market & customer research Industry best practices Internal expertise of the employees Legal Details In depth analysis of the existing information and the processes that deliver it to the website In depth analysis of the application and integration points storing and managing the information Review of the content creation and editorial process with an emphasis on areas where data consistency could be improved Analysis of web analytics, customer surveys, customer comments, overall industry trends Incorporation of lessons learned and best practices in the industry Extensive interviews with employees who are on the ground as these insights are often quite valuable Review of any legal implications or requirements in the content creation and publication process

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 Increase in the number of paths that a customer can take to get to products

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 Goals Met with Semantic Technologies Flexible information usage to drive revenue Sophisticated business rules to drive revenue and personalization Semantic integration to support integrations between systems Exposure of a common model to search, navigation, BI, etc.

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 Case study: Targeted Content Delivery

23 Where we started Kbase Media 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 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 Project INputs Source Existing information IT Infrastructure Internal workflows & governance Goals Industry best practices Internal expertise of the employees Legal Details Analysis of the existing information and information structures Analysis of the application and integration points storing and managing the information Review of the content creation and editorial process. Identify opportunities for aligning terminology and managing metadata. Analysis of inter- and intra-business unit project & product goals to find opportunities for alignment and re-use Incorporation of lessons learned and best practices in the industry Extensive interviews with front line employees and subject matter experts (SMEs) Review legal, regulatory and contractual obligations for content use

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 What we ended with HCat EE RBC I/R CMS/DAM Human Machine NLP QA

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 Modeling is not the hard part

30 Systems integration Politics Regulations Business Processes Change Management Auditing Human Resources

31 Usability Maintenance Growth

32 A hybrid, standards-based system has the greatest integrity.

33 Questions? Thank you! Gary Carlson, Christine Connors,

34 Case Study: Regulatory compliance

35 Government regulations require approval for the distribution of information Metadata schema Controlled vocabularies Systems integration Rules-based classification