How Search 2.0 Has Been Redefined by Enterprise 2.0


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Presentation given by Carl Frappaolo of Information Architected at Enterprise 2.0 San Francisco 2009

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How Search 2.0 Has Been Redefined by Enterprise 2.0

  1. 1. How Search 2.0 Has Been Redefined by Enterprise 2.0 Carl Frappaolo Information Architected, Inc.
  2. 2. 62% of corporate users believe that finding information inside the firewall needed to execute their job is difficult and time consuming.
  3. 3. 82% believe experience with thefor consumer web has created demand improved enterprise search
  4. 4. 1
  5. 5. Search Web 2.0 = E2.0
  6. 6. Karina Meiri <> 6
  7. 7. Why Isn’t Search Enough?
  8. 8. 56% have no strategy for enterprise search
  9. 9. 1
  10. 10. 17
  11. 11. 18
  12. 12. (ECMs) SEARCH E-Mail & Intranet(s) Attachments SEARCH SEARCH Web/ Information Online Enterprise 2.o Workplace Libraries SEARCH SEARCH Desktop Files BPM Logs SEARCH ERP SEARCH SEARCH 12
  13. 13. Karina Meiri <> 13
  14. 14. Its NOT Search - Its Findability
  15. 15. Findability Structure, meaning Needs,behaviors Document/data types,content Information needs, audience types, objects, meta-information, existing expertise, experience,tasks, structure, future structure, volume information seeking behaviors Community Content IA Culture, technology Business models & goals, Corporate culture, technology, funding, politics, resources, Context constraints
  16. 16. Only 15% feel findability as opposed to “search” is understood and addressed within their organization.
  17. 17. What is Information Architecture? IA is the art and science of structuring organizing, and labeling information to help people find and manage information Browsing Aids Information Enabling Components Architecture Content & Tasks Search Aids Rosenfeld & Morville
  18. 18. Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Research Strategy Design Implementation Administration Review existing Create conceptual Transform high-level Build, Test, Launch Evaluate & Improve materials to framework for strategy into an IA gain high-level Structuring & Includes: Day to Day Maintenance: understanding of organizing web site − Organizing & Tagging (effort required tied directly goals/business High-level The most “work” Documents to content churn) context, existing happens here: − Testing − Select & Evaluate new IA, content, & Recommends: − Controlled vocabs. − Troubleshooting content Intended audiences − IA Administration − Taxonomies − Developing − Classification management − Technology − Thesauri Documentation − Indexing management Meetings with Integration − Metadata schema − Developing Training − CV & Thesauri management strategy team, − Top-down vs − Blueprints Programs content owners, Bottom-up − Wireframes Periodic Evaluation/Updates IT, users, − Organization & stakeholders Labeling Systems Continuous Evaluation & − Doc Type ID Improvement − Metadata field Definitions − Navigation System Design
  19. 19. Users Who are they? Which audiences are the most important? What motivates them to come to this site? What do they want from this site? What do they need from this site? How do they navigate? What terms do they use to search, browse, and classify information? What are their most important information needs?
  20. 20. Content How do I get my arms around the content we have? What content has value? What content can I get rid of? How do I make answers emerge from content? How should content be organized and labeled? What is this content object? How can I describe it? What distinguishes it from other content objects? How can I make it findable?
  21. 21. Context Who in this organization makes the decisions? What are the primary business goals and objectives for increased findability? What does the decision-maker want from the site? What political and cultural factors might impact the architecture? What similar initiatives have worked in the past? What similar initiatives have failed in the past and why? What resources (people, technology, time, $$$) are available? How will the architecture be maintained?
  22. 22. Needles & Haystacks Needles & haystacks •! A known needle in a known haystack •! A known needle in an unknown haystack •! •! Needles & Haystacks An unknown needle in an unknown haystack Any needle in a haystack •! The sharpest needle in a haystack •! Most of the sharpest needles in a haystack •! All of the needles in a haystack •! Affirmation of no needles in any haystack •! Things like needles in any haystack •! Let me know whenever a new needle shows up •! Where are the haystacks? •! Needles, haystacks - whatever End game of IR is finding whatever needle/ haystack you are looking for - whether you know it exists or not... Delphi Group • 10 Post Office Square • Boston, MA 02109-4603 • (617) 247-1511v (617) 247-4957f • ©2005 Delphi Group, A Perot Systems Company Dr. Mathew Koll
  23. 23. Putting Together a Stratgey and Design Top-Down vs. Bottom-Up
  24. 24. Organizational Schemes Exact Schemes: divide information into well-defined and mutually exclusive sections (easy to design, maintain, and use) –Alphabetical –Chronological –Geographical Ambiguous Schemes: divide information into categories that defy exact definition (difficult to design & maintain, can be difficult to use. Supports process of associative learning by grouping items in intellectually meaningful ways) –Topic or Subject –Task-oriented –Audience
  25. 25. Search Federated/Universal Search Zones Agents Parametric / Hybrids Sharing tags Analytics and Suggestions Relevancy Ranking
  26. 26. Relevancy Ranking Date (Weighted) Term Summing Omni-term skewing Term density and proximity Links and conections Analytics (Explicit and Implicit) User preferences and affiliations Dynamic/User controlled?
  27. 27. Types of Controlled Vocabularies cuisinart cuizinart Kitchen aid •Synonym ring kitchenaid Food processor blender •Authority files CT Connecticut, Con, Conn, Conneticut LA Louisiana, Loosiana, Louanna •Classification Schemes •Thesaurus Wine (BT) Bubbly (VT) Sparkling Wine Champagne (VT) (PT) Weddings (RT) Brut Champagne Mimosas (RT) (NT) •Ontologies
  28. 28. Search Brings Results... TOO MANY Wrong Results Potential Answer Enterprise Potential Potential Potential Answer Answer Answer Content The You Search Answer Potential Potential Potential Potential Potential Potential Answer Answer Answer Answer Answer Answer Elapsed time: Hours 1. You Search 4. Check 2nd result Not the right answer 2. You Find MANY hits ... Check ### result 3. Check 1st result Not the right answer Not the right answer Later that day: ... Check ### result Did I get the right Not the right answer answer? ... Check ### result Did the opportunity pass Not the right answer us by? ... Check ### result Is this the most current Not the right answer information? ?!@^%$!
  29. 29. Findability Guides the User to Find THE Answer Potential Answer Potential Potential Tax An Potential Answer Answer Inte Sea The aly o Answer You nom rch rfa tics Answer Potential Potential Potential Potential ce Potential y Answer Answer Answer Answer Potential Answer Answer 1. You have a need to 2. Interface exposes 3. Search provides some 4. Taxonomy provides a 6. When all of these extract valuable multiple ways to get to precision in retrieval, further layer of components are used in information from the information, the but can often include precision tandem, you are vast collection of simplest being a shared false positives or provided with “the Enterprise Content drive or arrangement by content irrelevant to 5. Analytics provides answer” file folders your focus further precision by putting the query and content in context Search is only PART of the answer
  30. 30. !
  31. 31. ACME General Hospital XXX XXX !
  32. 32. !
  33. 33. Areas of Medicine Members Respiratory Diseases & Treatments Oncology Treatment Dr. Mark Robbins - Chief Surgery - ACME General Research Hospital - is a specialist in eyes ears and throat Docum surgery and oncology. His recent experience includes experimental procedures in the treatment patent of laser surgery related to stigmatism. author reas of experience He holds two patents and a trademark for his work in nano-based technology for providing attorne or this attorney sight to the blind. ! He us a graduate of the University of Illinois, and did his residency at the New England (more) Matters patent that thi billed o
  34. 34. Let’s Get 2.0 Facebook: Information Architected Twitter: @IAI Facebook: Carl Frappaolo Twitter: @carlfrappaolo
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