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Search Solutions 2011: Successful Enterprise Search By Design


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When your colleagues say they want Google, they don’t mean the Google Search Appliance. They mean the Google Search user experience: pervasive, expedient and delivering the information that they need. Successful enterprise search does not start with the application features, is not part of the information architecture, does not come from a controlled vocabulary and does not emerge on its own from the developers. It requires enterprise-specific data mining, enterprise-specific user-centered design and fine tuning to turn “search sucks” into search success within the firewall. This presentation looks at action items, tools and deliverables for Discovery, Planning, Design and Post Launch phases of an enterprise search deployment.

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Search Solutions 2011: Successful Enterprise Search By Design

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  4. 4. Xerox UK study: July 2009 4
  5. 5. Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009 5
  6. 6. How we look for information is different between people and between people and machines.Humans are limited by their ignorance. We don’t know what we’re looking for much of the time and sodo not know how to find it. We often rely on technology to provide parameters to narrow our scopeand put us on the right track. Unfortunately, technology is “face value” and so does not know how tointerpret our queries. Does not understand that we can have a single word mean multiple things(order a meal, put things in order) or multiple terms mean the same thing (star: celestial entity,celebrity) 6
  7. 7. This was recently put to the test in the US with an item that caused an uproar. A woman wants to buydesigner eyeglasses and save money. She chooses the #3 result on Google. The frames that aredelivered are obviously fake. When she returns them for refund, the owner of the business respondswith harassment and threats.To the customer, relevant means honest and high quality. To Google, relevant means many links andmany, many social media mentions. What the search engine did not understand is that most of thementions were warnings of bad quality and service.When the story came to light, Google’s response was that they would “tune” their sentimentalgorithm. 7
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  9. 9. today, we’re deploying a new technology that can better understand associations andconcepts related to your search, and one of its first applications lets us offer you even more usefulrelated searches (the terms found at the bottom, and sometimes at the top, of the search results page).For example, if you search for [principles of physics], our algorithms understand that “angularmomentum”, “special relativity”, “big bang” and “quantum mechanic” are related terms that couldhelp you find what you need.” spoke yesterday to Google and Ori Allon. To the extent that I understood his discussion of the wayOrion’s technology had been applied to refinements here’s what’s going on at a high level: pages arebeing scanned in “real-time” by Google after a query is entered. Conceptually and contextually relatedsites/pages are then identified and expressed in the form of the improved refinements. This is notsolely keyword based but derived from an “understanding” of content and context. 9
  10. 10. If machines are methodical, as we’ve seen, and people are emotional, as we experience, where is the middle ground? Are we working harder to reallyfind what we need or just taking what we get and calling it what we wanted in the first place?Some other search engine patentsGoogle •Improving Search using Population Information (November 2008) •Rendering Context Sensitive Ads for Multi-topic searchers (April 2008) •Presentation of Local Results (July 2008) •Detecting Novel Content (November 2008) •Document Scoring based on Document Content Update (May 2007) •Document Scoring based on Link-based Criteria (April 2007)Microsoft:Launches “decision engine” with focus on multiple meaning (contexts) as well as term indexing and topic association and tracking-Lead researcher Susan Dumais at the forefront of user behavior for prediction on search relevance-Look to recent acquisition of Powerset (semantic indexing) and FAST ESP (semantic processing)Calculating Valence of Expressions within Docum0ents for Searching a Document Index (March 2009): System for natural language search andsentiment analysis through a breakdown of the valence manipulation in documentEfficiently Representing Word Sense Probabilities (April 2009): Word sense probabilities stored in a semantic index and mapped to “buckets.”Tracking Storylines Around a Query (May 2008): Employ probabilistic or spectral techniques to discover themes within documents delivered overa stream of time Compares the query with the contents of each document to discover whether query exists implicitly or explicitly in received document Builds topic models Consolidate the plurality of info around certain subjects (track stories that continue over time) Collect results over time and sort (keeps track of the current themes and alerts to new) Track Rank (relevance) Present abstracts 10
  11. 11. AIIM Marketing Intelligence Industry Watch: SharePoint Strategies & Experiences (2010)* A majority of 58% have been able to do most of the things they needed with SharePoint. 39%have used customization to meet their needs, and 28% have added third-party applications. 27%felt there were considerable shortcomings in some or all areas. Re-porting existingcustomizations to the 2010 version is the biggest expected issue for those upgrading.* The most popularSP Enteprise Search28% working live15% rolling out23% planned in next 12-18 months18% have no plans yet9% have another solutions22% plan to use another search/analytics program added on27% felt SP search met their needs43% saw some shortcomings20% saw major shortcomings 11
  12. 12. IDC High cost of Not finding information 2010: estimates typical knowledge work spends 2.5hours per day searching for information – expect to find information within 4 minutesAIIM Ford Motor Company estimate knowledge workers spend 5-15% of their time on non-productive information-related activitiesIT Manager Fortune 500 company communications firm estimates that by improving serach andretrieval systems for just the firms 4000 engineers the investment would recover within a monthand would contribute $2 million monthly productivity gain thereafterWorkers spend a great deal of time recreating existing knowledge, ROI of enterprise searchworkers spend average of 9.5 a week on search and 8.3 hours a week gathering information fordocumentsIDC estimates a 16% savings in time spent searching with effective search solution 12
  13. 13. More storage = more things stored, whether useful or notEnterprise search engines are cross functional (able to search across many applications and aggregatethe results), more sophisticated and configurableYour company paid lots of $$$$$Those demos got everyone jacked upYou are tired of hearing search sucks 13
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  17. 17. Not everyone shares the same meanings as the guy who put it togetherUseful for facets and filtersMust let them form their own searches 17
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  22. 22. Sample objectives•Single source for all searches•Smart (learned) search•Comprehensive results•Reduced duplication of content (email versions, multiple copies, etc)•Enhanced system-derived relevance•Enable personalized finding methods•Increase customer satisfaction with search = increased usage = increased satisfaction, etcTasks•Define content repositories•Define content scopes•Define content types•Define content owners•Research existing internal applications•Define Security (governance) 22
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  24. 24. Empower users •Bespoke relevance adjustments •Consolidated results (federation) •Results filtering (facets) •Geo-location awareness •RSS feeds/alerts •Social Applications: bookmarking, wikies, tagging, blogsEvaluate content and metadata (system and thought processing biped) •Cross application searching (structured & unstructured) with protocol handlers •Document type Ifilters •Define Best Bets (editorialized results) •Spell check •Synonym mapping •Recommender system •Designate Authority pagesEducate internal content resourcesProtect resources •Security modeling 24
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  26. 26. Guided Tours: built on analysis of other user pathways and knowledge of corpusProduced Views: page of assembled content items focused on a single subjectTask List Drop Downs: “I Want To…” links to pages of assembled content focused onsingle common taskRelated Links: related as in “next steps” not what Marketing wants to be a next stepBest Bets: editorially assigned result that may not be chosen by the search engine 26
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  28. 28. Distance reflects relevance URL Depth: the further from the homepage, the less important it must be Click Distance: the further from an authority page, the less important it must beURLs Keywords found in URLs are weighted for relevance Hyphens as separators is best 28
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  30. 30. Design pre- and post-query UI to accommodate user pre query interventionLeverage system information 30
  31. 31. Users look to search engines for guidance. We can provide similar guidance with usercontrolsSearch as you type: Jquery customization for SP 2007 31
  32. 32. Jared Spool did a site search study some time ago that found users successful 37% ofthe time when using site search and 50+% of the time when navigatingUsers don’t like navigation at the outset but will use it if contextual and in a form thatthey can influenceMUST HAVESPDF and MS Office indexingWeb search partGood UI (i.e. not OOB)Department level relevance tuningUser assistance Facets/filters View in browser/resultsSocial features (where they makes sense)NICE TO HAVESContent StrategyRelational content modelingLink strategySocial 32
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  35. 35. We’re smart, search engines are a toolNeed is an experience – need to know is a state of being 35
  36. 36. Configuring search in the enterprise may seem hard but is not as hard as managingmultiple applications, interoperability and licensesBenefit is to get much more from much less and never hearing “search sucks” fromcolleagues again 36
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