Operational Intelligence

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Operational Intelligence

  1. 1. 9 t h E u r o p e a n T D W I C o n f e r e n c e , M u n i c h , 1 5 . 0 6 . 2 0 0 9 Dr. Olivera Marjanovic University of Sydney ChrisFan Schieder Chemnitz University of Technology Opera&onal Intelligence An IntroducFon to Event-driven, Rule-based Business Process Intelligence SoluFons
  2. 2. © 2009 by ChrisFan Schieder 2 Agenda What is OperaFonal Intelligence about? Understanding OperaFonal Intelligence Concepts BI/BPM IntegraFon Roadmap, Strategies, Methods and Training Technologies for OperaFonal Intelligence Case Study II: RealFme ReporFng in CamshaT Manufactoring Case Study I: Change Data Capture @ Sony Online Entertainment Case Study III: Customer Centric Decisioning: Deliver the Perfect Message at the Perfect Moment
  3. 3. What is Opera&onal Intelligence about? …turning data into acFonable intelligence…
  4. 4. © 2009 by ChrisFan Schieder 4 A brief history of Opera&onal Intelligence
  5. 5. © 2009 by ChrisFan Schieder 5 From Sun Tsu‘s “Art of War”… by 663highland @ wikipedia “Superior commanders succeed in situaFons where ordinary people fail because they obtain more Fmely informaFon and use it more quickly.” Sun Tsu, The Art of War, 6th century BC
  6. 6. © 2009 by ChrisFan Schieder 6 …to Churchill‘s Adap&ve Enterprise… Bletchley Park Intelligence Capture Storey‘s Gate Cabinet War Room Whitehall Minitries Supply Chain Management Bentley Prior RealFme Monitoring
  7. 7. © 2009 by ChrisFan Schieder 7 …and modern BaFlefield Surveillance… US AF Air and Space Operations Center Screenshot from Command & Conquer The Combined US Air and Space Operations Center at an undisclosed Mideast location. Sgt.JimVarhegyi/USAF/AP
  8. 8. © 2009 by ChrisFan Schieder 8 …to current Business Monitoring and Automa&on
  9. 9. © 2009 by ChrisFan Schieder 9 Defining Opera&onal Intelligence OperaFonal Intelligence focuses on providing real-&me monitoring of business processes and acFviFes as they are executed within computer systems, and in assisFng in op&mizing these acFviFes and processes by iden&fying and detec&ng situa&ons that correspond to interrupFons and bo^lenecks. Source: h^p://en.wikipedia.org/wiki/OperaFonal_Intelligence, 12.05.2009 Courtesy: BBC 2008, h^p://www.youtube.com/view_play_list?p=F5D324185EE73FEC
  10. 10. © 2009 by ChrisFan Schieder 10 The Cost of Latency… Source: Hackathorn, Richard: Minimizing AcFon Distance, h^p://www.tdan.com/i025fe04.htm, 01-07 -2003. Business transacFon Decision taken available Data available in DW AcFon performed Data latency Analysis latency Decision latency AcFon latency Time (PotenFal) Value of Decision Analysis results
  11. 11. © 2009 by ChrisFan Schieder 11 …differs! Strategic D TacFcal Decisioning OperaFonal Decisioning AddiFonal Benefit from immediate Decision Time ecisioning
  12. 12. © 2009 by ChrisFan Schieder 12 Eckerson‘s Layer Model Source: Eckerson, Wayne W.: Best PracFces in OperaFonal BI, TDWI Best PracFses Report, 2007, p. 6. LOW HIGH HIGH DATA LATENCY BUSINESS VALUE EXECUTE PROCESSES FACILITATE PROCESSES MONITOR PROCESSES ANALYZE PROCESSES OperaFonal reports OperaFonal dashboards Composite applicaFons Event-driven AnalyFc plamorms REAL TIME DAILY LOW
  13. 13. © 2009 by ChrisFan Schieder 13 The past, the present and the situa&on… Business Process Intelligence Business Ac&vity Monitoring Complex Event Processing §  Ex post §  Long-term staFsFcal Process Control §  „The Rear Mirror“ §  Rule/Model generaFon §  In situ §  AlerFng §  Surveillance of defined processes in execuFon §  In situ §  DetecFon of complex events/ situaFons §  CorrelaFon of distributed Events …3 Pillars of Opera&onal Intelligence
  14. 14. Understanding Opera&onal Intelligence Concepts …sense…interpret…analyze…decide…respond… Concept by Silvia Helena Cardoso, Ph.D. Center for Biomedical InformaFon, University of Campinas, Brazil
  15. 15. © 2009 by ChrisFan Schieder 15 Scale (sec) System Stratum 107 106 105 Social 104 103 102 Task Task Task RaFonal 101 100 10-1 Unit Task OperaFons Deliberate Act CogniFve 10-2 10-3 10-4 Neural Circuit Neuron Organelle Biological Time Scale of Human Ac&ons
  16. 16. © 2009 by ChrisFan Schieder 16 §  System CapabiliFes §  Interface Design §  Stress & Workload §  Complexity §  AutomaFon §  AbiliFes §  Experience §  Training §  Goals & ObjecFves §  PreconcepFons (ExpectaFons) InformaFon Processing Mechanisms Long Term Memory Store AutomaFcity Performance of AcFons Decision Situa&on Awareness ProjecFon of future Status Comprehension of current SituaFon PercepFon of Elements in current SituaFon State of the Environment Level 1 Level 2 Level 3 Feedback Individual Factors Task Factors Endsley, M. R. (1995b). Toward a theory of situaFon awareness in dynamic systems. Human Factors 37(1), 32-64 Situa&on Awareness
  17. 17. © 2009 by ChrisFan Schieder 17 §  System CapabiliFes §  Interface Design §  Stress & Workload §  Complexity §  AutomaFon §  AbiliFes §  Experience §  Training §  Goals & ObjecFves §  PreconcepFons (ExpectaFons) ApplicaFons Data Warehouse Business Rules Performance of AcFons Decision Situa&on Awareness PredicFve AnalyFcs Business Intelligence Business AcFvity Monitoring State of the Environment Level 1 Level 2 Level 3 Feedback Individual Factors Task Factors Processes Strategy People IT Infrastructure Business Situa&on Awareness
  18. 18. © 2009 by ChrisFan Schieder 18 Key Requirements for Opera&onal Intelligence Responsiveness Agility Flexibility
  19. 19. © 2009 by ChrisFan Schieder 19 Never forget the importance of agility… h^p://geekandpoke.typepad.com/geekandpoke/2009/03/simply-explained-part-37-agility.html
  20. 20. Technologies for Opera&onal Intelligence responsiveness, agility, and flexibility Courtesy: Lookheed MarFn Corp.
  21. 21. © 2009 by ChrisFan Schieder 21 Opera&onal Intelligence Trends
  22. 22. © 2009 by ChrisFan Schieder 22 Opera&onal Intelligence IT Architectures Source: Fair Issac Source: Eckerson, TDWI Source: Terradata Source: Microstrategy
  23. 23. © 2009 by ChrisFan Schieder 23 Event-driven IT Architectures Source: Oracle Source: SAP Source: IBM
  24. 24. © 2009 by ChrisFan Schieder 24 A Simple Model for Complex Event Processing EXTERNAL DISTRIBUTED LOCAL EVENT SERVICES EVENT PROFILES DATA BASES OTHER DATA Complex Event Processing HUMAN COMPUTER INTERACTION EVENT SOURCES LEVEL 0 EVENT PREPROCESSING LEVEL 1 EVENT REFINEMENT LEVEL 2 SITUATION REFINEMENT LEVEL 3 IMPACT ASSESSMENT LEVEL 4 PROCESS REFINEMENT DB MANAGEMENT Historical Data Profiles & Pa^erns Adapted from: David L. Hall , James Llinas (Eds.): Handbook of MulFsensor Data Fusion, CRC, 2001, Front-Cover. Source: Tim Bass, Event-Decision Architecture for PredicFve Business, 2006, h^p://www.slideshare.net/TimBassCEP/cep-eventdecision-architecture-for-predicFvebusiness-july-2006-presentaFon.
  25. 25. © 2009 by ChrisFan Schieder 25 Level 0 Discovery and Detec&on – Mining and fusing data Source: James Llinas, Christopher Bowman, Galina Rogova, Alan Steinberg, Ed Waltz, Frank White: RevisiFng the JDL Data Fusion Model II. In P. Svensson and J. Schubert (Eds.), Proceedings of the 7th InternaFonal Conference on InformaFon Fusion (FUSION 2004), 2004, p. 8. 1 2 3 OperaFonal Data Stores Level 1 Fusion Object Database Level 2 Fusion SituaFon Database Level 3 VisualizaFon Data Ware- house Post-DWH ETL Model Repository EnFty RelaFonship Clustering Model CreaFing Model TesFng AnalyFc GeneralizaFon ValidaFon Data Fusion Real-Fme detec%on of known pa^erns Data Mining Off-line discovery of new pa^erns Pre-DWH ETL SituaFons Impacts Discovered Templates
  26. 26. © 2009 by ChrisFan Schieder 26 Opera&onal Business Intelligence Architecture ETL-Batch Event Streams Purchasing Sales ProducFon ETL-Batch Micro Batches ERP SCM CRM Legacy Workflow- Engine ODS Data Warehouse Stream Cache Business Rules Engine Business Rules Repository Dashboards OLAP Reports Mobile Devices Data Mining ExecuFves Analysts Decision Makers Processes Systems Data IntegraFon Data Storage Decisions Analysis Users Event Processing Engine
  27. 27. © 2009 by ChrisFan Schieder 27 Concepts and Technologies for Latency reduc&on Data latency Micro Batches Change Data Capture Data ReplicaFon Event Stream Processing In-Memory Databases Data SynchronizaFon Analysis latency Data Mining Process Mining Visual Analysis Business AcFvity Monitoring Decision latency Business Rules Complex Event Processing AcFon latency ERP/ CRM/ SCM Workflow AutomaFon
  28. 28. Case Study I – Talend Sony Online Entertainment leverages Talend's Change Data Capture for its real-&me gaming analy&cs
  29. 29. © 2009 by ChrisFan Schieder 30 Agenda What is OperaFonal Intelligence about? Understanding OperaFonal Intelligence Concepts BI/BPM IntegraFon Roadmap, Strategies, Methods and Training Technologies for OperaFonal Intelligence Case Study II: RealFme ReporFng in CamshaT Manufactoring Case Study I: Change Data Capture @ Sony Online Entertainment Case Study III: Customer Centric Decisioning: Deliver the Perfect Message at the Perfect Moment
  30. 30. Case Study II – DMC Group Real&me Repor&ng in Camshae Manufactoring
  31. 31. BI/BPM Integra&on Roadmap, Strategies, Methods and Training …geung down to business… By teliko82 @ flickr
  32. 32. Case Study III – Accenture Customer Centric Decisioning
  33. 33. Add on h^p://geekandpoke.typepad.com/geekandpoke/2009/03/simply-explained-part-37-agility.html
  34. 34. © 2009 by ChrisFan Schieder 37 h^p://web.mit.edu/sheffi/www/ h^p://www.lessons-from-history.com h^p://www.senseandrespond.com/SHHaeckel.html Literature I
  35. 35. © 2009 by ChrisFan Schieder 38 Literature II h^p://complexevents.com/ h^p://www.manning.com/etzion/ h^p://epthinking.blogspot.com/
  36. 36. © 2009 by ChrisFan Schieder 39 Contact ChrisFan Schieder Research Associate Chemnitz University of Technology Faculty of Business AdministraFon and Economics Chair for Systems Engineering and InformaFon Systems Dr. Olivera Marjanovic Senior Lecturer University of Sydney Faculty of Economics and Business Thueringer Weg 7, 09126 Chemnitz, Germany Tel. : +49 (0)371 531 35792 Fax : +49 (0)371 531 8 35792 Mail: chrisFan.schieder@wirtschaT.tu-chemnitz.de h^p://www.tu-chemnitz.de/wirtschaT/wi2/mitarbeiter/cschie.php Room 434, H69 - Economics and Business Building, The University of Sydney, NSW 2006 Australia Phone: +61 2 9351 8477 Fax: +61 2 9351 7294 Mail: O.Marjanovic@econ.usyd.edu.au h^p://www.econ.usyd.edu.au/staff/oliveram
  37. 37. © 2009 by ChrisFan Schieder 40 Bildnachweis Flugzeug h^ps://pixabay.com/de/flugzeug-flugzeug-kreuzfahrt-h%C3%B6hen-897048/ Cockpit h^ps://pixabay.com/de/cockpit-flugzeug-jet-pkw-pilot-100624/ Sunzi h^ps://de.wikipedia.org/wiki/Sunzi#/media/File:Enchoen27n3200.jpg JoysFck h^ps://de.wikipedia.org/wiki/JoysFck#/media/File:Joyopis.svg Control Center h^ps://upload.wikimedia.org/wikipedia/commons/f/fd/STS-128_MCC_space_staFon_flight_control_room.jpg AutoprodukFon h^ps://upload.wikimedia.org/wikipedia/commons/5/55/Geely_assembly_line_in_Beilun,_Ningbo.JPG Schach h^ps://pixabay.com/de/schach-strategie-schachbre^-316658/

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