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Xerox UK study: July 2009 http://www.pitchengine.com/xeroxcorporation/xerox-survey-finds-information-overload-a-hinder-to-electronic-health-records-/15485/

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Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009

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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 so do not know how to find it. We often rely on technology to provide parameters to narrow our scope and put us on the right track. Unfortunately, technology is “face value” and so does not know how to interpret 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)

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This was recently put to the test in the US with an item that caused an uproar. A woman wants to buy designer eyeglasses and save money. She chooses the #3 result on Google. The frames that are delivered are obviously fake. When she returns them for refund, the owner of the business responds with harassment and threats. To the customer, relevant means honest and high quality. To Google, relevant means many links and many, many social media mentions. What the search engine did not understand is that most of the mentions were warnings of bad quality and service. When the story came to light, Google’s response was that they would “tune” their sentiment algorithm.

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http://www.googleblog.blogspot.com/2009/03/two-new-improvements-to-google-results.html Starting today, we’re deploying a new technology that can better understand associations and concepts related to your search, and one of its first applications lets us offer you even more useful related 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 “angular momentum”, “special relativity”, “big bang” and “quantum mechanic” are related terms that could help you find what you need.” http://searchengineland.com/google-implements-orion-technology-improving-search-refinements-adds-longer-snippets-17038 I spoke yesterday to Google and Ori Allon. To the extent that I understood his discussion of the way Orion’s technology had been applied to refinements here’s what’s going on at a high level: pages are being scanned in “real-time” by Google after a query is entered. Conceptually and contextually related sites/pages are then identified and expressed in the form of the improved refinements. This is not solely keyword based but derived from an “understanding” of content and context.

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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 really find what we need or just taking what we get and calling it what we wanted in the first place? Some other search engine patents Google

•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 and sentiment analysis through a breakdown of the valence manipulation in document Efficiently 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 over a 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

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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 existing customizations to the 2010 version is the biggest expected issue for those upgrading. * The most popular SP Enteprise Search 28% working live 15% rolling out 23% planned in next 12-18 months 18% have no plans yet 9% have another solutions 22% plan to use another search/analytics program added on 27% felt SP search met their needs 43% saw some shortcomings 20% saw major shortcomings

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IDC High cost of Not finding information 2010: estimates typical knowledge work spends 2.5 hours per day searching for information – expect to find information within 4 minutes AIIM Ford Motor Company estimate knowledge workers spend 5-15% of their time on non-productive information-related activities IT Manager Fortune 500 company communications firm estimates that by improving serach and retrieval systems for just the firm's 4000 engineers the investment would recover within a month and would contribute $2 million monthly productivity gain thereafter Workers spend a great deal of time recreating existing knowledge, http://online.bcc.cts.edu/econ/kst/BriefReign/BRwebversion.htm Google ROI of enterprise search workers spend average of 9.5 a week on search and 8.3 hours a week gathering information for documents IDC estimates a 16% savings in time spent searching with effective search solution

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More storage = more things stored, whether useful or not Enterprise search engines are cross functional (able to search across many applications and aggregate the results), more sophisticated and configurable Your company paid lots of $$$$$ Those demos got everyone jacked up You are tired of hearing search sucks

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Not everyone shares the same meanings as the guy who put it together Useful for facets and filters Must let them form their own searches

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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, etc Tasks •Define content repositories •Define content scopes •Define content types •Define content owners •Research existing internal applications •Define Security (governance)

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Empower users •Bespoke relevance adjustments •Consolidated results (federation) •Results filtering (facets) •Geo-location awareness •RSS feeds/alerts •Social Applications: bookmarking, wikies, tagging, blogs

Evaluate 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 pages

Educate internal content resources Protect resources

•Security modeling

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Guided Tours: built on analysis of other user pathways and knowledge of corpus Produced Views: page of assembled content items focused on a single subject Task List Drop Downs: “I Want To…” links to pages of assembled content focused on single common task Related Links: related as in “next steps” not what Marketing wants to be a next step Best Bets: editorially assigned result that may not be chosen by the search engine

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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 be

URLs Keywords found in URLs are weighted for relevance Hyphens as separators is best

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Design pre- and post-query UI to accommodate user pre query intervention Leverage system information

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Users look to search engines for guidance. We can provide similar guidance with user controls Search as you type: Jquery customization for SP 2007

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Jared Spool did a site search study some time ago that found users successful 37% of the time when using site search and 50+% of the time when navigating Users don’t like navigation at the outset but will use it if contextual and in a form that they can influence MUST HAVES PDF and MS Office indexing Web search part Good UI (i.e. not OOB) Department level relevance tuning User assistance

Facets/filters View in browser/results

Social features (where they makes sense) NICE TO HAVES Content Strategy Relational content modeling Link strategy Social

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We’re smart, search engines are a tool Need is an experience – need to know is a state of being

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Configuring search in the enterprise may seem hard but is not as hard as managing multiple applications, interoperability and licenses Benefit is to get much more from much less and never hearing “search sucks” from colleagues again

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