Introduction to Enterprise Search

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Introduction to Enterprise Search. A two hour class to introduce Enterprise Search. It covers:
The problems enterprise search can solve
History of (web) search
How we search and find?
Current state of Enterprise Search + stats
Technical concept
Information quality
Feedback cycle
Five dimensions of Findability

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  • What do you want to know?\n
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  • We humans love to collect information, we have a harder time deleting/archiving.\nWhen we start valuing information correctly we can also motivate investments in search and put processes in place to keep information updated AND with high quality. \nInformation hygiene. Structure, metadata.\nInfonomics = information as an asset in the balance sheet. \n
  • Is this how you feel information is organised and structured in your organsation?\n
  • Is the information you need stored in a silo somewhere?\n
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  • Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
  • Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
  • Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
  • Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
  • Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
  • Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
  • Yahoo: The directory is organized by subject matter, the top level containing categories such as Arts and Humanities, Business and Economy, Computers and the Internet, Education, Government, Health, News and Media, Recreation and Sports, Science, Society and Culture, and so on.\nThe natural hierarchical structure of the directory allows users easy navigation through and across its categories.\nThe directory is not strictly hierarchical, as it has many cross-references between categories from different parts of the hierarchy. For example, the subcategory Musicals under the Theater category, has a reference to the Movies and Film subcategory, which comes under Entertainment.\nWeb directories provide an important alternative to search engines, especially for novice surfers, as the directory structure makes it is easy to find relevant information provided when an appropriate category for the search query can be found. The fundamental drawback of directories is their lack of coverage.\nKnowing the category of a web page that a user clicked on is very indicative of the user's interests, and may be used to recommend to the user similar pages from the same or a related category. To solve the problem of how to automatically associate a web page with a category we need to make use of machine learning techniques for automatic categorization of web pages\n
  • Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
  • Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
  • Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
  • Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
  • Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
  • Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
  • Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
  • Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
  • Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
  • Navigational queries. reach a particular site that the user has in mind, either because they visited it in the past or because they assume that such a site exists. have usually only one "right" result.\nInformational queries. find information assumed to be available on the web in a static form. \nTransactional queries. reach a site where further interaction will happen. This interaction constitutes the transaction defining these queries. The main categories for such queries are shopping, finding various web-mediated services, downloading various type of file (images, songs, etc), accessing certain data-bases (e.g. Yellow Pages type data), finding servers (e.g.for gaming) etc.\n \n 2nd gen. - Google, first engine to use link analysis as a primary ranking factor and DirectHit concentrated on click-through data. By now, all major engines use all these types of data. Link analysis and anchortext seems crucial for navigational queries.\n3rd gen. - For instance on a query like San Francisco the engine might present direct links to a hotel reservation page for San Francisco, a map server, a weather server, etc.\nRapidly changing landscape\n
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  • Information silos. They are everywhere. \nEnterprise Search can “integrate” them.\n
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  • On intranets or our web site search we do not have the equivalent of PageRank.\nWe can’t use the amount of inbound link as a factor for relevancy. \nWe have to find other ways...\n
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  • Introduction to Enterprise Search

    1. 1. INTRODUCTION TO ENTERPRISE SEARCH Kristian Norling
    2. 2. Introduction• Who is here?• Your expectations?• Kristian?• 2 hours, one break• Lifetime answer Guarantee on this class
    3. 3. Hikingartist
    4. 4. Agenda• Problem• History of (web) search• How we search and !nd?• Current state of Enterprise Search + stats• Technical concept• Information quality• Feedback cycle• Five dimensions of Findability
    5. 5. •List mrflip
    6. 6. nathansnider
    7. 7. erikref
    8. 8. The Problems• Growing amounts of Information• Changing patterns of information consumption• Information silos• Web like behaviour > Information !lters• Internal information use is still in the Digital Stone Age
    9. 9. History of SearchIn Academia search is called InformationRetrieval.It is an old discipline, dating backthousands of years...Basic concepts in Information Retrieval:Recall and Precision, more later...
    10. 10. Directories vs. Search Engines• Directories are manually compiled taxonomies of websites• Directories are far more costly and time intensive to maintain• Directories lack coverage, although it provides an important alternative, especially for novice surfers• Search engines rely mainly on automated search algorithms• Search engines rank pages by popularity on the web, the more referrals (links) the more relevant
    11. 11. Early days of Web SearchYahoo – searchable directory (1994, ~10000 websites) • Integrates  search  over  its  directory.  Organized  by  subject   ma8ers.  Sites  can  be  suggested,  but  human  editors  control   quality  of  directory  (~100  dedicated  editors)Ask – natural language search engine (1998) • used  human  editors  to  match  popular  queries.  Tried   different  algorithms  to  rank  pages  by  popularityGoogle – searchable index (1998) • Developed  Pagerank,  popularity  algorithm  that  hides  bad   content.  Set  standards  (spellchecking,  query  suggesIon,   search  results  page  design)
    12. 12. Web Search - evolutionFirst generation (1995-97) – AltaVista, Excite, WebCrawlerUses mostly on-page data (text and formatting).Informational queries.Second generation (1998-2010) – Google, YahooUse o"-page, web-speci!c data: link analysis, anchor-text, click-through data. Informational and navigational queries.Third generation (2010-present) – Google, Wolfram-Alpha,BingBlend data from many sources, tries to answer ‘‘the needbehind the query’’: semantic analysis, context determination,dynamic database selection etc. Informational, navigational, andtransactional queries.
    13. 13. Seeking information modes:InformationalFind information assumed to be availableon the web in a static form.
    14. 14. Seeking information modes:NavigationalReach a particular site that the user has inmind, either because they visited it in thepast or because they assume that such asite exists. Have usually only one "right"result.
    15. 15. Seeking information modes:TransactionalReach a site where further interaction will happen. Thisinteraction constitutes the transaction de!ning thesequeries. The main categories for such queries areshopping, !nding various web-mediated services,downloading various type of !le (images, songs, etc),accessing certain data-bases (e.g. Yellow Pages type data),!nding servers (e.g.for gaming) etc.
    16. 16. Four modes of seeking information Finding something when I know what I want and have words to describe it.
    17. 17. Four modes of seeking information Exploring when I only have some idea of what I want and may lack the words to articulate it.
    18. 18. Four modes of seeking information Finding relevant items when I don’t know what I need.
    19. 19. Four modes of seeking information Finding something I have seen before, but can’t remember where.
    20. 20. The State of Enterprise Search• Amount of information is growing everyday• What to Search for?• Where to Search?• How to Search?• Search is simple, complex and powerful• Findability Dimensions
    21. 21. STATS FROM THE“ENTERPRISE SEARCH ANDFINDABILITY SURVEY 2012” SIGN-UP
    22. 22. HOW CRITICAL IS FINDINGTHE RIGHT INFORMATION TO BUSINESS GOALS AND SUCCESS?
    23. 23. EUROPE 76.5%IMPERATIVE/SIGNIFICANT
    24. 24. Zoom Zoom
    25. 25. IS IT EASY TO FIND THE RIGHT INFORMATION WITHIN YOURORGANISATION TODAY?
    26. 26. EUROPE 77%MODERATELY/VERY HARD
    27. 27. LEVEL OF SATISFACTION?
    28. 28. proimos
    29. 29. EUROPE 18.5%MOSTLY/VERY SATISFIED
    30. 30. WHAT ARE THE OBSTACLES TO FINDING THE RIGHT INFORMATION?
    31. 31. Globally63.4% POOR SEARCH FUNCTIONALITY52.1% DONT KNOW WHERE TO LOOK51.4% INCONSISTENCY IN HOW WE TAG CONTENT50.0% LACK OF ADEQUATE TAGS33.1% DON’T KNOW WHAT TO LOOK FOR
    32. 32. Wikipedia De!nition“Enterprise search is the practice ofmaking content from multipleenterprise-type sources, such asdatabases and intranets, searchable to ade!ned audience.”http://en.wikipedia.org/wiki/Enterprise_search
    33. 33. The Concept of Enterprise Search: Precision In the !eld of information retrieval, precision is the fraction of retrieved documents that are relevant to the search. Precision takes all retrieved documents into account, but it can also be evaluated at a given cut-o" rank, considering only the topmost results returned by the system. This measure is called precision at n or P@n. Source: Wikipedia
    34. 34. The Concept of Enterprise Search: Recall Recall in information retrieval is the fraction of the documents that are relevant to the query that are successfully retrieved. For example for text search on a set of documents recall is the number of correct results divided by the number of results that should have been returned. Source: Wikipedia
    35. 35. Precision and Recall R number of M number of N number of retrieved documents relevant documents retrieved documents that are also relevant
    36. 36. Precision and RecallRecall = R / M =Number of retrieved documents that arealso relevant / Total number of relevantdocuments.Precision = R / N =Number of retrieved documents that arealso relevant / Total number of retrieveddocuments.
    37. 37. Relevance...enterprises typically have to use other query-independent factors, such as a documents recency orpopularity, along with query-dependent factorstraditionally associated with information retrievalalgorithms. Also, the rich functionality of enterprisesearch UIs, such as clustering and faceting, diminishreliance on ranking as the means to direct the usersattention. Source: Wikipedia
    38. 38. PageRank
    39. 39. RelevanceWe do not have PageRank......but we have social!Social Reconnects Enterprise SearchEmails, People Catalogues, Connections,Tagging, Sharing etc.
    40. 40. The Concept of Enterprise Search
    41. 41. Search based SolutionsExamples of implementations:- People Search- Product Search- Document Search- Intranet and Website Search- E-commerce- Dashboard / Search as a Service
    42. 42. Information / Content• Good Data/Information hygiene• Crap in = Crap out• Metadata is very important!• Taxonomy and Metadata demysti!ed• TetraPak example (video)• SimCorp example• VGR example (video)
    43. 43. •List yeraze
    44. 44. svenwerk
    45. 45. HCE (SWEDEN)DEWEY DECIMAL CLASSIFICATION
    46. 46. KristianNorling
    47. 47. Author: Douglas CouplandTitle: Hej Nostradamus!Publisher: NorstedtsYear: 2003Printed by: SmedjebackenPrinted: 2004 KristianNorling
    48. 48. MetadataSemantic KristianNorling
    49. 49. ESEO: Actionable activitiesExample: Ernst & Young• Metadata• Titles• Content Quality• Information Life Cycle Management
    50. 50. Show me the MoneyBut, an average Search budget is 100K Euro• TCO• ROI• KPISearch Analytics is key
    51. 51. Search AnalyticsImportant, delivers actionable to-dos quickly• 0-results• Top Terms Searched forVideo: Search Analytics in Practice
    52. 52. User Satisfaction• Feedback form• KPI from Search Analytics• Session time x n:o sessions = Time spent on search x hourly price = Cost per “answer”• Add search re!nements + exit page (=is the right answer)
    53. 53. Findability by Findwise 1. BUSINESSBuild solutions to support your business processes and goals 2. INFORMATION Prepare information to make it !ndable 3. USERS Build usable solutions based on user needs 4. ORGANISATION Govern and improve your solution over time 5. SEARCH TECHNOLOGYBuild solutions based on state-of-the-art search technology
    54. 54. Business• Analyze how your business goals andstrategies can be met by improvedinformation access• Set Findability goals. Examples; increase therevenue on sales, raise productivity, improveknowledge sharing, better collaboration• Specify your requirements• De!ne KPI’s and measure the success of yourinvestments
    55. 55. Information• Clean up and archive or delete outdated/unrelevant information• Ensure good quality of information byadding structured and suitable metadata• Create and use information models andtaxonomies• Tagging?
    56. 56. Users• Get to know your users and their needs• Make sure your solution is easy to use• Perform continuous usability evaluations,like usage tests and expert evaluations• Make sure users !nd what they are lookingfor• Enable feedback loops for complaints,feedback and praise
    57. 57. Organisation• Resources!• De!ne processes, roles and routines togovern the solution• Perform Search Analytics• Create easy to use administrationinterfaces• Perform training, technical and editorial• Help publishers get started with processesfor better !ndability
    58. 58. Search Technology• Select a suitable search platform or makethe most of your current solution• Design your architecture with search-as-a-service in mind• Utilise the full potential of the selectedtechnology
    59. 59. Kristian Norling Kristian Norling LinkedIn @kristiannorling @!ndwise !ndwise.com Findability Blog Slideshare Vimeo Newsroom

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