Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

From Info Science to Data Science & Smart Nation

717 views

Published on

Keynote Speech on "From Info Science to Data Science" by Dr. Toh

Published in: Data & Analytics
  • Be the first to comment

From Info Science to Data Science & Smart Nation

  1. 1. Keynote Speech: “From Information Science to Data Science to Smart Nation” Prof. Toh Chai Keong Assistant Chief Executive (Engineering & Technology) Infocomm Development Authority of Singapore Pg 1
  2. 2.  Welcome to Singapore!!  From Info Science  To Data Science  Challenges facing us  Smart Nation  Conclusion OUTLINE
  3. 3.  The science of creating, handling, and processing information???  Some say it is “application” focus rather than development  Others say it is more tied to business, IT architectures and operations INFORMATION SCIENCE
  4. 4.  Course Structure NUS INFO SCIENCE DEGREE
  5. 5.  Digital and New Media Marketing  Mobile & Ubiquitous Commerce  E-Commerce  E-Business  Enterprise Social Systems  Technology Strategy & Management  IT and Customer Relationship Management  Mobile Apps Development  Strategic IS Planning  IT in Financial Services SOME NUS INFO SCIENCE MODULES
  6. 6. SMU INFORMATION SYSTEMS
  7. 7.  Business Process Modeling & Solution Blueprinting  Enterprise Integration & Service-Oriented Architectures  Information Security & Trust  Architectural Analysis  Enterprise Web Solutions (web portals)  Interaction Design & Prototyping ADVANCED INFO SCIENCE TOPICS
  8. 8. NUS IS Research Areas SMU IS Research Areas  IS Healthcare ■ Cybersecurity  E-Commerce ■ Data Mgmt & Analytics  Social Computing ■ IS Mgmt  Service Integration ■ Intelligent Systems  Info Mgmt ■ Cyber-Physical Systems  Economics of IS INFO SCIENCE RESEARCH AREAS
  9. 9.  The science of computation?  If so, you would think of Alan Turner’s Turing Machine  Or you may think of all the hardware and software technologies behind the computer!! COMPUTER SCIENCE IBM 5100 1975 Ed Roberts’s Altair 1975 APPLE 1 1976
  10. 10.  WORLD WIDE WEB  CLOUD  BIG DATA  IN-MEMORY COMPUTING  DATABASES  VIRTUALIZATION  CLUSTERED COMPUTING  INTERNET OF EVERYTHING  GRID COMPUTING  etc WORLD TECH TRENDS
  11. 11.  More Devices Than Humans SCARY TREND #1 Anyone Everyone Anything Can Generate Data
  12. 12.  Data is the new nugget (not your money) SCARY TREND #2
  13. 13. SCARY TREND #3
  14. 14. SCARY TREND #4
  15. 15. SCARY TREND #5
  16. 16. DATA EXPLOSION: INFORMATION TSUNAMI
  17. 17. DATA EXPLOSION GRAPH From 2010 onwards
  18. 18.  Birth = Computer (Computing) + Internet (Connectivity)  Anyone can publish information (server and client)  Data accessible anywhere everywhere WWW: WORLD WIDE WEB
  19. 19.  Search yields “convergence” but not necessarily “intelligence”  Based on what is out there……..  It does not quite “reason” or even “verify”! WEB NEEDS SEARCH ENGINES
  20. 20. WHAT ABOUT DATABASES, SERVERS, NETWORKS, VIRTUALIZATION? MORE STORAGE MORE SERVERS GREEN SERVERSDIVERSE NETWORKS VMs
  21. 21. DATA CENTERS ARE BORN
  22. 22.  Data Storage  Data Organization  Data Access  Data handling  Data processing  Data Filtering  Modeling  Reasoning  Knowledge Creation  Big Picture  Insights  DATA SCIENCE IS BORN !!!!! WHAT TO DO WITH ALL THESE DATA? Computing Internet Web Data Analytics
  23. 23. BIRTH OF DATA SCIENCE: Good it brings in multiple fields in computer and info sciences
  24. 24.  Computers  Devices  Internet  IoE  Web  Data Explosion  Data Understanding  Data Reasoning  Data Science.. WHAT FUELS THE BIRTH OF DATA SCIENCE?
  25. 25. WHEN – DATA MINING Make Sense? Data + Junk YOUR INNOVATION HERE….
  26. 26. DATA MINING
  27. 27.  Data Mining:  Task of discovering interesting patterns from big data..  Data Warehousing:  Data storage and memory  Data Mining Tools:  Microsoft SQL  DBMiner  Oracle Data Mining DATA MINING & DATA WAREHOUSING
  28. 28. WHEN – KNOWLEDGE DISCOVERY Knowledge = Understanding + Intelligence!!!
  29. 29.  See big picture  Insights?  Answer to why? WHY ANALYTICS?
  30. 30. WHEN ANALYTICS? U SELL IN MASSES, U NEED ANALYTICS Customer Feedback Size & Wants
  31. 31.  101001000  BYTE  PACKET  MESSAGE  INFO ELEMENT  Text  Audio  Video  Image  Etc. ANALYTICS: DATA IS A REPRESENTATION
  32. 32.  Text  Audio  Video  Image  Metadata TYPES OF DATA & THEIR CHALLENGES Social Data Sensor Data
  33. 33. SINGAPORE GOVERNMENT DATA DATA.GOV.SG
  34. 34.  Narrative  Can describe things down to each component  Too little data – back to square 1  Too much data – takes time to make sense  Too too too much data – blurred…  Giga bytes – 2^30 = 1000MBytes  Tera bytes – 2^40 bytes  Peta bytes – 2^50 bytes  Exa bytes – 2^60 bytes  Zetta Bytes – 2^70 Bytes  Yotta Bytes – 1000ZB – Too Big to Imagine THE IRONY OF BIG DATA
  35. 35.  When DATA is too big…  When DATA is too small…  When there is a lot of junk…  When MODEL is not good enough…  When Memory hits the limit….  When Computation hits the limit…. CHALLENGES: SIZE VS COMPUTATION
  36. 36.  I need a picture here CHALLENGES: SPEED VS CONVERGENCE VS SCALE VS ACCURACY Technical Challenges Of Big Data Analytics
  37. 37.  I need a picture here CHALLENGES: WASTING CPU CYCLES ANALYZING JUNK? Data Without Meaning Is junk…
  38. 38.  Data privacy- anonymous (source and/or user unknown)  Data protection – accessibility  Data anomaly – odd, outlier, fake, alteration, etc…. CHALLENGES: DATA ANOMALY & DATA PRIVACY
  39. 39. DATA ANALTYTICS PLATFORM Spark: 40x faster than Hadoop; In-memory data storage Shark: Ported Hive in Spark..
  40. 40. ANALYTICS: PARALLELISM & CONVERGENCE
  41. 41.  Learn from data, make predictions on data MACHINE LEARNING
  42. 42. DATA VISUALIZATION
  43. 43.  Plenty of room for research and development…. ADVANCED ANALYTICS
  44. 44. “CONWAY” DEFINITION OF WHAT IS A DATA SCIENTIST
  45. 45. DEMAND FOR DATA SCIENTIST SURGING
  46. 46. SHORTAGE OF TALENTS – IDA CULTIVATING THEM
  47. 47.  Big data  Cloud  Cybersecurity  Green ICT  Future Comms  Social Media  New Digital Economy  User Interface  Internet of Things  Data Science  Leads to ………………………………………………SMART NATION IDA INFOCOMM TECH ROADMAP
  48. 48. SN: Improving Quality Of Life of Singaporeans Enjoyable user experience Making meaningful choices
  49. 49. SMART NATION EVOLUTION…. SPATIAL DIMENSION Quality of life/Biz Productivity Meeting citizens’ needs Unlimited Possibilities Smart Nation (Singapore) Use of technology to create innovative solutions for Future Smart Home, Office & City Smart City/Town Anticipatory govt that is citizen centric & co-ordinated govt service delivery Smart Home/Office/Buildings - Unified smart home experiences - Smart work solutions for greater business opportunities
  50. 50. SMART NATION: KEY BUILDING BLOCKS
  51. 51. SMART NATION: DATA ANALYTICS PLATFORM COMPUTE t s p c
  52. 52.  Data Handling, Knowledge Building, Intelligence Creation SMART NATION:
  53. 53.  AGEING POPULATION  Elderly Living  Health Care  URBAN DENSITY  Smart Transport  Smart Living SMART NATION ADDRESSES THESE CHALLENGES..
  54. 54.  Healthcare Transformation WHAT’S NEXT • Personalized Medicine • Preventive Illness • Predictive Illness • Implantables • Wearables • Smart Health
  55. 55.  Transport Transformation WHAT’S NEXT • Autonomous Self Driving Vehicles • Multi-modal Transport
  56. 56.  Living Transformation SMART NATION Smart homes - Energy mgmt - Lighting ctl - Temp ctl - Noise ctl - Mental State Use of Sensors Ambient Intelligence Life style
  57. 57.  Business Transformation (BI) WHAT’S NEXT
  58. 58.  Information Science should include Data Science  Data Science can enhance the lives of Singaporeans  Through Transformation in  Transport,  Living,  Healthcare  And others. Together, we will make this happen. SMART NATION: CONCLUSION
  59. 59. SMART NATION PLATFORM

×