Data Categories                                      Supports automated processing                                       –...
Common Structured Data© 2011 SAP AG. All rights reserved.   2
Data Categories                                      Supports automated processing                                       –...
Common Unstructured Data          A press          release          communication© 2011 SAP AG. All rights reserved.   4
Common Unstructured Data                           Forum                           p                           postings   ...
Data Categories                                      Supports automated processing                                       –...
Common Event Data© 2011 SAP AG. All rights reserved.   7
What vs. Why and When     vsIt’s generally said that…structured data tells us “what”     andevent data tells “Wh t” and “W...
Knowledge                                                                                    Strategy                     ...
Business Intelligence Typically Runs Off Structured Data© 2011 SAP AG. All rights reserved.                        10
Business Intelligence Reporting off Structured Data How can you extendyour BI investments tounstructured and event   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:  ...
http://www.twitterfall.com/
http://archivist.visitmix.com/
Technology is only an enablerBut the power is in the patterns        p               p
http://maps.linkfluence.net/vc/                How do you visualize your information?
http://www.whatdoestheinternetthink.net/
Information is Beautiful
So what can you do for me?
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                                           ...
Example
Web Intelligence reports in the BI Launch Pad© 2011 SAP AG. All rights reserved.             32
Opened WebI report© 2011 SAP AG. All rights reserved.   33
Searching on “computer”              computer© 2011 SAP AG. All rights reserved.   34
“Computer” in the Most Mentions Concepts report Computer© 2011 SAP AG. All rights reserved.               35
“Enjoy” stance in the Positive Sentiments Enjoy© 2011 SAP AG. All rights reserved.         36
“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.                        38
The data flow in the Data Services Designer© 2011 SAP AG. All rights reserved.           39
AFSUG Cafe BI - Charles de Jager
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AFSUG Cafe BI - Charles de Jager

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Content providing context at Cafe BI held in Cape Town & Johannesburg, South Africa, on 9 & 10 November 2011.

Presented by Charles de Jager

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AFSUG Cafe BI - Charles de Jager

  1. 1. 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. 1
  2. 2. Common Structured Data© 2011 SAP AG. All rights reserved. 2
  3. 3. 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. 3
  4. 4. Common Unstructured Data A press release communication© 2011 SAP AG. All rights reserved. 4
  5. 5. Common Unstructured Data Forum p postings g© 2011 SAP AG. All rights reserved. 5
  6. 6. 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. 6
  7. 7. Common Event Data© 2011 SAP AG. All rights reserved. 7
  8. 8. 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. 8
  9. 9. 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. 9
  10. 10. Business Intelligence Typically Runs Off Structured Data© 2011 SAP AG. All rights reserved. 10
  11. 11. Business Intelligence Reporting off Structured Data How can you extendyour BI investments tounstructured and event t t d d t information?© 2011 SAP AG. All rights reserved. 11
  12. 12. Do you reportjust for the sakeof reporting? f ti ?
  13. 13. Or do you innovatewith intelligence?
  14. 14. Workers Lose Productivity from InadequateInformation Access 54%Lose ProductivitySource: Economist, ‘Enterprise Knowledge Workers Study© 2011 SAP AG. All rights reserved. 14
  15. 15. 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. 15
  16. 16. 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. 16
  17. 17. http://www.twitterfall.com/
  18. 18. http://archivist.visitmix.com/
  19. 19. Technology is only an enablerBut the power is in the patterns p p
  20. 20. http://maps.linkfluence.net/vc/ How do you visualize your information?
  21. 21. http://www.whatdoestheinternetthink.net/
  22. 22. Information is Beautiful
  23. 23. So what can you do for me?
  24. 24. 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. 25
  25. 25. 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. 26
  26. 26. 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. 27
  27. 27. 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. 28
  28. 28. 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. 29
  29. 29. 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. 30
  30. 30. Example
  31. 31. Web Intelligence reports in the BI Launch Pad© 2011 SAP AG. All rights reserved. 32
  32. 32. Opened WebI report© 2011 SAP AG. All rights reserved. 33
  33. 33. Searching on “computer” computer© 2011 SAP AG. All rights reserved. 34
  34. 34. “Computer” in the Most Mentions Concepts report Computer© 2011 SAP AG. All rights reserved. 35
  35. 35. “Enjoy” stance in the Positive Sentiments Enjoy© 2011 SAP AG. All rights reserved. 36
  36. 36. “False” and “Issue” stances in the Negative Sentiments False Issue© 2011 SAP AG. All rights reserved. 37
  37. 37. Drilling down to further understand the complete context© 2011 SAP AG. All rights reserved. 38
  38. 38. The data flow in the Data Services Designer© 2011 SAP AG. All rights reserved. 39

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