The keynote speech discusses the evolution from information science to data science and how this progression enables Singapore's vision of becoming a smart nation. It outlines how exponential growth in data and new technologies led to the birth of data science. Data science faces challenges regarding data size, computation limits, and ensuring privacy. The speech envisions how data analytics can help address Singapore's challenges like aging population and urban density through transformations in healthcare, transport, living and business enabled by a national smart nation platform.

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.  Welcome to Singapore!!
 From Info Science
 To Data Science
 Challenges facing us
 Smart Nation
 Conclusion
OUTLINE

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.  Course Structure
NUS INFO SCIENCE DEGREE

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. SMU INFORMATION SYSTEMS

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. 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.  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.  WORLD WIDE WEB
 CLOUD
 BIG DATA
 IN-MEMORY COMPUTING
 DATABASES
 VIRTUALIZATION
 CLUSTERED COMPUTING
 INTERNET OF EVERYTHING
 GRID COMPUTING
 etc
WORLD TECH TRENDS

11.  More Devices Than Humans
SCARY TREND #1
Anyone
Everyone
Anything
Can
Generate
Data

12.  Data is the new nugget (not your money)
SCARY TREND #2

13. SCARY TREND #3

14. SCARY TREND #4

15. SCARY TREND #5

16. DATA EXPLOSION: INFORMATION
TSUNAMI

17. DATA EXPLOSION GRAPH
From 2010 onwards

18.  Birth = Computer (Computing) + Internet (Connectivity)
 Anyone can publish information (server and client)
 Data accessible anywhere everywhere
WWW: WORLD WIDE WEB

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. WHAT ABOUT DATABASES, SERVERS,
NETWORKS, VIRTUALIZATION?
MORE
STORAGE
MORE
SERVERS
GREEN
SERVERSDIVERSE
NETWORKS
VMs

21. DATA CENTERS ARE BORN

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. BIRTH OF DATA SCIENCE:
Good it brings in multiple fields in computer and info sciences

24.  Computers
 Devices
 Internet
 IoE
 Web
 Data Explosion
 Data Understanding
 Data Reasoning
 Data Science..
WHAT FUELS THE BIRTH OF DATA
SCIENCE?

25. WHEN – DATA MINING
Make Sense?
Data + Junk
YOUR INNOVATION
HERE….

26. DATA MINING

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. WHEN – KNOWLEDGE DISCOVERY
Knowledge = Understanding + Intelligence!!!

29.  See big picture
 Insights?
 Answer to why?
WHY ANALYTICS?

30. WHEN ANALYTICS?
U SELL IN MASSES, U NEED ANALYTICS
Customer Feedback
Size & Wants

31.  101001000
 BYTE
 PACKET
 MESSAGE
 INFO ELEMENT
 Text
 Audio
 Video
 Image
 Etc.
ANALYTICS: DATA IS A REPRESENTATION

32.  Text
 Audio
 Video
 Image
 Metadata
TYPES OF DATA & THEIR CHALLENGES
Social Data Sensor Data

33. SINGAPORE GOVERNMENT DATA
DATA.GOV.SG

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.  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.  I need a picture here
CHALLENGES: SPEED VS CONVERGENCE
VS SCALE VS ACCURACY
Technical
Challenges
Of
Big Data
Analytics

37.  I need a picture here
CHALLENGES: WASTING CPU CYCLES
ANALYZING JUNK?
Data
Without
Meaning
Is junk…

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. DATA ANALTYTICS PLATFORM
Spark: 40x faster than
Hadoop;
In-memory data storage
Shark: Ported Hive in
Spark..

40. ANALYTICS: PARALLELISM &
CONVERGENCE

41.  Learn from data, make predictions on data
MACHINE LEARNING

42. DATA VISUALIZATION

43.  Plenty of room for research and development….
ADVANCED ANALYTICS

44. “CONWAY” DEFINITION OF WHAT IS A
DATA SCIENTIST

45. DEMAND FOR DATA SCIENTIST SURGING

46. SHORTAGE OF TALENTS – IDA
CULTIVATING THEM

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. SN: Improving Quality Of Life of Singaporeans
Enjoyable user experience Making meaningful choices

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. SMART NATION: KEY BUILDING BLOCKS

51. SMART NATION: DATA ANALYTICS
PLATFORM
COMPUTE
t
s
p
c

52.  Data Handling, Knowledge Building, Intelligence Creation
SMART NATION:

53.  AGEING POPULATION
 Elderly Living
 Health Care
 URBAN DENSITY
 Smart Transport
 Smart Living
SMART NATION ADDRESSES THESE
CHALLENGES..

54.  Healthcare Transformation
WHAT’S NEXT
• Personalized Medicine
• Preventive Illness
• Predictive Illness
• Implantables
• Wearables
• Smart Health

55.  Transport Transformation
WHAT’S NEXT
• Autonomous Self Driving Vehicles
• Multi-modal Transport

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.  Business Transformation (BI)
WHAT’S NEXT

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. SMART NATION PLATFORM