World Café. Social Business Intelligence.World Café Social Business Intelligence#CafeBI (www.twitter.com/afsug)Facilitated...
Data Categories                                      Supports automated processing                                       –...
Common Structured Data© 2011 SAP AG. All rights reserved.   3
Data Categories                                      Supports automated processing                                       –...
Common Unstructured Data          A press          release          communication© 2011 SAP AG. All rights reserved.   5
Common Unstructured Data                           Forum                           p                           postings   ...
Data Categories                                      Supports automated processing                                       –...
Common Event Data© 2011 SAP AG. All rights reserved.   8
What vs. Why and When     vsIt’s generally said that…structured data tells us “what”     andevent data tells “Wh t” and “W...
From the Business Perspective“If you are not analyzing text – if you’reanalyzing only transactionalinformation – you’re mi...
Text Analytics Boosts Business Results“Organizations embracing textanalytics all report having anepiphany moment when th  ...
Text Analytics Expands Your Vision of BusinessIntelligence“The bulk of information value isperceived as coming from data i...
Knowledge                                                                                    Strategy                     ...
Business Intelligence Typically Runs Off Structured Data© 2011 SAP AG. All rights reserved.                        14
Business Intelligence Reporting off Structured Data How can you extendyour BI investments tounstructured text data?   t   ...
Do you reportjust for the sakeof reporting?  f       ti ?
Or do you innovatewith intelligence?
Workers Lose Productivity from InadequateInformation Access                  54%Lose ProductivitySource: Economist, ‘Enter...
The Goal: Be a Best Run Business                                                                  77%  “77% of high      p...
IT Is Looking for Flexibility in Sharing RelevantInformation                                      Organizations require:  ...
RELEVANT INFORMATION                                     Mobile                            Large                          ...
Discussion Session 1
EverythingThe Social Media MasterClass 2011© 2011 SAP AG. All rights reserved.   23
@pfeiffer44:  POTUS to address the nation @pfeiffer44: “POTUS to address the nation   tonight at 10.30pm eastern time”    ...
Twitter explodes. Debate rages  Twitter explodes Debate ragesabout whether Qaddafi had been                  Q     fkilled...
2900 Tweets per second. 2900 Tweets per second
@keithurbahn: “So I’m told by a reputable person they have killed       bl          h h      kill d  Osama Bin Laden. Hot ...
The rumor turns out to be true.   ‐ approximately 10.45pm           i    l 10 45
@nytimes:  NYT NEWS ALERT: @nytimes: “NYT NEWS ALERT:                       ,Osama bin Laden Is Dead, White         House ...
@foxnews:  FoxNews Chad    @foxnews: “FoxNews’ Chad                  Pergram confirms Osama bin Laden    g is dead us...
@cnnbrk: “Osama bin Laden is dead usama d d        osamabinladen”                   bi l d ”
3200 Tweets per second. 3200 Tweets per second
Just before Obama makes his     address at 11.30pm…      dd       11 30
5106 Tweets per second. 5106 Tweets per second
From 10.45pm – 2.20am on                 p1st and 2 nd May 2011, there was an average of 3000 Tweets per second.   The hig...
Everything is going  real time . Everything is going “real time”.
Why?Because the mobile has squashed Because the mobile has squashed        time and space.        time and space.
This is changingThis is changing everything…
From the way we discover                yinformation, to the way we share   information, to the way we  consume i f       ...
Meme. Noun.          M     N  An idea, behavior or style that           ,              yspreads from person to person in a...
Copyright 2011 All Rights Reserved
I’m a “giant” this doesn’t effect me?I’    “ i t” thi d      ’t ff t     ?
Get practical about it
But never forget the number one      rule of the social web…       l f h        i l b
It’s all about balance and common      sense at the end of the day.
We want to authentic, transparent, conversations! We want to engage!        ti ! W         tt        !
Technology is only an enablerBut the power is in the patterns        p               p
One tweet does not a pattern make.  So do you trust it?t t it?
http://www.tweetreach.com
http://archivist.visitmix.com
http://www.whatdoestheinternetthink.net/
http://twendz.waggeneredstrom.com/
How do you visualize your information?http://maps.linkfluence.net/vc/
Information is Beautiful
Discussion Session 2
Text Data Processing Defined                                                         Structured        ructured Text      ...
Automate Research Analysis Text data processing semantically understands the meaning and context of information, not just ...
SAP BusinessObjects Data ServicesData integration, data quality, data profiling, and text data processing                 ...
Text Data Processing on the Data Services PlatformNative Text Data Processing on the Data Services p                      ...
Supported Entity Types for Extraction  Who: people, job title, and national          Where: addresses, cities, states,   i...
Pre-defined Extraction of Sentiments, Events, andRelationshipsVoice of Customer                                           ...
Understanding Sentiment   “Sentiment analysis or opinion   mining refers to the application of   natural language processi...
Voice of the CustomerApply text data processing toenhance customer service andsatisfaction by understandingcustomer opinio...
Social Media is Noisy“The challenge lies in identifyingstatistically valid data related to specificbusiness priorities fb ...
Your Best Customer May Be Your Worst EnemyWhen Unhappy Customers StrikeBack on the Internet Double Deviation – customers ...
Opinions Do Matter“78% of consumers trust peerrecommendations.”-- The Broad Reach of Social Technologies,Forrester Researc...
Demo
Web Intelligence reports in the BI Launch Pad© 2011 SAP AG. All rights reserved.             72
Opened WebI report© 2011 SAP AG. All rights reserved.   73
Searching on “computer”              computer© 2011 SAP AG. All rights reserved.   74
“Computer” in the Most Mentions Concepts report Computer© 2011 SAP AG. All rights reserved.               75
“Enjoy” stance in the Positive Sentiments Enjoy© 2011 SAP AG. All rights reserved.         76
“False” and “Issue” stances in the Negative Sentiments False       Issue© 2011 SAP AG. All rights reserved.               ...
Drilling down to further understand the complete context© 2011 SAP AG. All rights reserved.                        78
The data flow in the Data Services Designer© 2011 SAP AG. All rights reserved.           79
AFSUG Cafe BI - Durban 8 Nov 2011
AFSUG Cafe BI - Durban 8 Nov 2011
AFSUG Cafe BI - Durban 8 Nov 2011
AFSUG Cafe BI - Durban 8 Nov 2011
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AFSUG Cafe BI - Durban 8 Nov 2011

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Content providing context at Cafe BI held in Durban, South Africa, on 8 November 2011.

Presented by Charles de Jager

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AFSUG Cafe BI - Durban 8 Nov 2011

  1. 1. World Café. Social Business Intelligence.World Café Social Business Intelligence#CafeBI (www.twitter.com/afsug)Facilitated by Manti Grobler (SAP) and Charles de Jager (SAP)
  2. 2. Data Categories Supports automated processing –C f Conforms with d t models associated with d t b ith data d l i t d ith databases and d Structured spreadsheets – Granular data stored in fields Generally does not support automated processing – No data model or not easily understood Unstructured – Insufficient metadata – Noisy data communications such as an email message, blog or document High Volume of small data bits – Huge volume Event – Only act on exceptions – Captured at source© 2011 SAP AG. All rights reserved. 2
  3. 3. Common Structured Data© 2011 SAP AG. All rights reserved. 3
  4. 4. Data Categories Supports automated processing –C f Conforms with d t models associated with d t b ith data d l i t d ith databases and d Structured spreadsheets – Granular data stored in fields Generally does not support automated processing – No data model or not easily understood Unstructured – Insufficient metadata – Noisy data communications such as an email message, blog or document High Volume of small data bits – Huge volume Event – Only act on exceptions – Captured at source© 2011 SAP AG. All rights reserved. 4
  5. 5. Common Unstructured Data A press release communication© 2011 SAP AG. All rights reserved. 5
  6. 6. Common Unstructured Data Forum p postings g© 2011 SAP AG. All rights reserved. 6
  7. 7. Data Categories Supports automated processing –C f Conforms with d t models associated with d t b ith data d l i t d ith databases and d Structured spreadsheets – Granular data stored in fields Generally does not support automated processing – No data model or not easily understood Unstructured – Insufficient metadata – Noisy data communications such as an email message, blog or document High Volume of small data – Huge volume Event – Only act on exceptions – Captured at source© 2011 SAP AG. All rights reserved. 7
  8. 8. Common Event Data© 2011 SAP AG. All rights reserved. 8
  9. 9. What vs. Why and When vsIt’s generally said that…structured data tells us “what” andevent data tells “Wh t” and “When” t d t t ll “What” d “Wh ” andunstructured data tells us “why” why© 2011 SAP AG. All rights reserved. 9
  10. 10. From the Business Perspective“If you are not analyzing text – if you’reanalyzing only transactionalinformation – you’re missingi f ti ’ i iopportunity or incurring risk.”-- Seth Grimes, Alta Plana© 2011 SAP AG. All rights reserved. 10
  11. 11. Text Analytics Boosts Business Results“Organizations embracing textanalytics all report having anepiphany moment when th i h t h theysuddenly knew more than before.”-- Phillip Russom, The DataWarehousing Institute© 2011 SAP AG. All rights reserved. 11
  12. 12. Text Analytics Expands Your Vision of BusinessIntelligence“The bulk of information value isperceived as coming from data inrelational tables. Th reason i th t l ti l t bl The is thatdata that is structured is easy to mineand analyze.”-- Prabhakar Raghavan, YahooResearch© 2011 SAP AG. All rights reserved. 12
  13. 13. Knowledge Strategy telligence e External Information Int PP formation n FI Plan HR CO SD Inf PM MM Operate / Generates Data© 2011 SAP AG. All rights reserved. 13
  14. 14. Business Intelligence Typically Runs Off Structured Data© 2011 SAP AG. All rights reserved. 14
  15. 15. Business Intelligence Reporting off Structured Data How can you extendyour BI investments tounstructured text data? t t dt td t ?© 2011 SAP AG. All rights reserved. 15
  16. 16. Do you reportjust for the sakeof reporting? f ti ?
  17. 17. Or do you innovatewith intelligence?
  18. 18. Workers Lose Productivity from InadequateInformation Access 54%Lose ProductivitySource: Economist, ‘Enterprise Knowledge Workers Study© 2011 SAP AG. All rights reserved. 18
  19. 19. The Goal: Be a Best Run Business 77% “77% of high performers have above average analytical y 23% capability” Low HighSource: Competing on Analytics, Thomas Davenport Performers Performers© 2011 SAP AG. All rights reserved. 19
  20. 20. IT Is Looking for Flexibility in Sharing RelevantInformation Organizations require: • Trusted, consolidated, and , , actionable information • From a variety of data y sources • Self-service access© 2011 SAP AG. All rights reserved. 20
  21. 21. RELEVANT INFORMATION Mobile Large Device Scale Business Suite Microsoft Self Office Service LESS RELIANCE ON IT © 2011 SAP AG. All rights reserved. 21© SAP AG 2010. All rights reserved. / Page 21
  22. 22. Discussion Session 1
  23. 23. EverythingThe Social Media MasterClass 2011© 2011 SAP AG. All rights reserved. 23
  24. 24. @pfeiffer44:  POTUS to address the nation @pfeiffer44: “POTUS to address the nation tonight at 10.30pm eastern time” ‐ 1 May 2011, 9.45pm,  1 May 2011 9 45pm Dan Pfeiffer,  Communications director at the White House
  25. 25. Twitter explodes. Debate rages  Twitter explodes Debate ragesabout whether Qaddafi had been  Q fkilled or Bin Laden tracked down. 
  26. 26. 2900 Tweets per second. 2900 Tweets per second
  27. 27. @keithurbahn: “So I’m told by a reputable person they have killed  bl h h kill d Osama Bin Laden. Hot damn. Osama Bin Laden Hot damn ” ‐ 1 May 2011, 10.25pm Keith Urbahn Chief of staff for Donald Rumsfeld
  28. 28. The rumor turns out to be true. ‐ approximately 10.45pm i l 10 45
  29. 29. @nytimes:  NYT NEWS ALERT: @nytimes: “NYT NEWS ALERT: ,Osama bin Laden Is Dead, White  House Says.”
  30. 30. @foxnews:  FoxNews Chad @foxnews: “FoxNews’ Chad  Pergram confirms Osama bin Laden  g is dead usama osamabinladen”
  31. 31. @cnnbrk: “Osama bin Laden is dead usama d d osamabinladen” bi l d ”
  32. 32. 3200 Tweets per second. 3200 Tweets per second
  33. 33. Just before Obama makes his  address at 11.30pm… dd 11 30
  34. 34. 5106 Tweets per second. 5106 Tweets per second
  35. 35. From 10.45pm – 2.20am on  p1st and 2 nd May 2011, there was an average of 3000 Tweets per second.  The highest sustained rate of  The highest sustained rate of Tweets. Ever. 
  36. 36. Everything is going  real time . Everything is going “real time”.
  37. 37. Why?Because the mobile has squashed Because the mobile has squashed time and space. time and space.
  38. 38. This is changingThis is changing everything…
  39. 39. From the way we discover yinformation, to the way we share information, to the way we  consume i f information and most  ti d timportantly, the way we connectimportantly the way we connect with others. 
  40. 40. Meme. Noun. M N An idea, behavior or style that  , yspreads from person to person in a  culture.
  41. 41. Copyright 2011 All Rights Reserved
  42. 42. I’m a “giant” this doesn’t effect me?I’ “ i t” thi d ’t ff t ?
  43. 43. Get practical about it
  44. 44. But never forget the number one  rule of the social web… l f h i l b
  45. 45. It’s all about balance and common  sense at the end of the day.
  46. 46. We want to authentic, transparent, conversations! We want to engage! ti ! W tt !
  47. 47. Technology is only an enablerBut the power is in the patterns p p
  48. 48. One tweet does not a pattern make.  So do you trust it?t t it?
  49. 49. http://www.tweetreach.com
  50. 50. http://archivist.visitmix.com
  51. 51. http://www.whatdoestheinternetthink.net/
  52. 52. http://twendz.waggeneredstrom.com/
  53. 53. How do you visualize your information?http://maps.linkfluence.net/vc/
  54. 54. Information is Beautiful
  55. 55. Discussion Session 2
  56. 56. Text Data Processing Defined Structured ructured Text Database 1.Extract meaning g d 2.Transform into structured Once structured it can be… data for analysis Integrated 3. 3 Cleanse and match Unstr Queried Analyzed Visualized Vi li d Reported against Unlocks Key Information from Text Sources to Drive Business Insight© 2011 SAP AG. All rights reserved. 60
  57. 57. Automate Research Analysis Text data processing semantically understands the meaning and context of information, not just the words themselves.  Applies linguistic and statistical techniques to extract entities, concepts and sentiments  Discerns facts and relationships that were previously unprocessable  Allows you to deal with information overload by mining very large corpora of words and making sense of it without having to read every sentence© 2011 SAP AG. All rights reserved. 61
  58. 58. SAP BusinessObjects Data ServicesData integration, data quality, data profiling, and text data processing SAP BusinessObjects Data Services 4.0 ata Business UI Technical UI ructured Da (Information (Data Services) Steward) Str Unified M t d t U ifi d Metadata One Runtime Architecture & Services ETL Data Quality uctured Profiling Unstru Text Analytics Data One Administration Environment (Scheduling, S (S h d li Security, U it User M Management) t) One Set of Source/Target Connectors Provides access to all critical business data (regardless of data source, type, ( g , yp , or domain) enabling greater business insights and operational effectiveness© 2011 SAP AG. All rights reserved. 62
  59. 59. Text Data Processing on the Data Services PlatformNative Text Data Processing on the Data Services p g platformwith the Entity Extraction transform to extract : Predefined entities (like company, person, firm, city, country, …) Sentiment Analysis (e.g. Strong positive, Weak positive, Neutral, Weak Negative, Strong Negative) Custom entities (customized via dictionaries)Languages supported (for version 4.0) English German French Spanish Japa ese Japanese Simplified Chinese … (expanding to 31 languages in next releases) © 2011 SAP AG. All rights reserved. 63
  60. 60. Supported Entity Types for Extraction Who: people, job title, and national Where: addresses, cities, states, identification numbers countries, facilities, internet What: Wh t companies, organizations, fi i i ti financial i l addresses, addresses and phone numbers indexes, and products How much: currencies and units of When: dates, days, holidays, months, measure years, times, and time periods Generic Concepts: “text data”, “global piracy”, and so on Current Languages supported with Data Services 4.0: English, French, German, Simplified Chinese, Spanish, Japanese (concepts only) Chinese Spanish Some of the additional Languages coming: Arabic, Dutch, Farsi, Italian, Korean, Japanese (with concepts), Portuguese, Russian© 2011 SAP AG. All rights reserved. 64
  61. 61. Pre-defined Extraction of Sentiments, Events, andRelationshipsVoice of Customer Public Sector:Sentiments: strong positive, weak Such as person-organization, person- positive, neutral, weak negative, alias, travel events and security strong negative, problemsRequests: customer requests Enterprise: Mergers and acquisitions, as well as M d i iti ll executive job changes Language Support: E li h F L S t English, French, h Language Support: E li h L S t English, German, Spanish Simplified Chinese These are starter packs that can be built upon for a specific deployment© 2011 SAP AG. All rights reserved. 65
  62. 62. Understanding Sentiment “Sentiment analysis or opinion mining refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials.” -- Wikipedia© 2011 SAP AG. All rights reserved. 66
  63. 63. Voice of the CustomerApply text data processing toenhance customer service andsatisfaction by understandingcustomer opinions on blogs, forumpostings, and social media.© 2011 SAP AG. All rights reserved. 67
  64. 64. Social Media is Noisy“The challenge lies in identifyingstatistically valid data related to specificbusiness priorities fb i i iti from th mountain of the t i favailable content. You don’t want tooverthrow a key marketing campaignbecause a fb few bloggers write snide bl it idthings. ”-- Leslie Owens, Text Analytics TakesBusiness Insight To New Depths socialimplications.com© 2011 SAP AG. All rights reserved. 68
  65. 65. Your Best Customer May Be Your Worst EnemyWhen Unhappy Customers StrikeBack on the Internet Double Deviation – customers have been victims of not only a product or service failure, but also failed resolutions Betrayal – primary driver of what causes customers to complain online p-- Thomas M. Tripp and YanyGrégoire,G é i MIT Sloan Management Sl M tReview© 2011 SAP AG. All rights reserved. 69
  66. 66. Opinions Do Matter“78% of consumers trust peerrecommendations.”-- The Broad Reach of Social Technologies,Forrester Research© 2011 SAP AG. All rights reserved. 70
  67. 67. Demo
  68. 68. Web Intelligence reports in the BI Launch Pad© 2011 SAP AG. All rights reserved. 72
  69. 69. Opened WebI report© 2011 SAP AG. All rights reserved. 73
  70. 70. Searching on “computer” computer© 2011 SAP AG. All rights reserved. 74
  71. 71. “Computer” in the Most Mentions Concepts report Computer© 2011 SAP AG. All rights reserved. 75
  72. 72. “Enjoy” stance in the Positive Sentiments Enjoy© 2011 SAP AG. All rights reserved. 76
  73. 73. “False” and “Issue” stances in the Negative Sentiments False Issue© 2011 SAP AG. All rights reserved. 77
  74. 74. Drilling down to further understand the complete context© 2011 SAP AG. All rights reserved. 78
  75. 75. The data flow in the Data Services Designer© 2011 SAP AG. All rights reserved. 79
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