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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4. 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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5. Using the Internet: Skill Related Problems in User Online Behavior; van Deursen & van Dijk; 2009
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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 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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7. 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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9. 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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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 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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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 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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12. 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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13. 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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17. 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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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, 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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24. 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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26. 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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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 be
URLs
Keywords found in URLs are weighted for relevance
Hyphens as separators is best
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30. Design pre- and post-query UI to accommodate user pre query intervention
Leverage system information
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31. 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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32. 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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35. We’re smart, search engines are a tool
Need is an experience – need to know is a state of being
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36. 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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