Technology Forecast

1. Copyright © 2014 by Jaeho BAE. All Right Reserved.
No Part of this document may be circulated, quoted, or reproduced for distribution outside the client organization without prior written approval from Jhbae@Osan
2014. 6.
오산대학교 교수 배재호
jhbae@osan.ac.kr
IT Trend 眺望

2. Who am I ?
l저서 - 공급망관리: 실무적용을 위한 계획에서 운영까지, 도서출판 두남, 2010. 6.
lTrading Area Analysis Using Modified Huff Model Based on Analytic
Hierarchy Process, JKKITS, 9(1), pp.179-190, 2014.
lEfficiency Comparision and Performance Targets for Academic Departments
in the Local Private College Using DEA, JKIIE, 39(4), pp.298-312, 2013.
lDerivation of Key Process Input Variables on Flim Production Line Using
Analytic Hierarchy Process, JKIPE, 17(4), pp.35-44, 2012
lAn Empirical Approach to Evaluate Management Performance Using a
Trading Area Analysis: Focused on Small and Medium-sized Retail Business,
JDS, 10(12), pp.5-11, 2012.
lDerivation of Key Process Input Variables on Film Production Line Using
Analytic Hierarchy Process, JKIPE, 17(4), pp.35-44, 2012.
lMeasurement of Overall Equipment Effectiveness Considering Processing
Materials and Methods, JKIPE, 16(3), pp.25-33, 2011.
lMature Market Sub-segmentation and Its Evaluation by the Degree of
Homogeneity, JDS, 8(3), pp.27-35, 2010.
lA Study on the Customer Experience Analysis for the Silver Generation in
the Communication Service Market using CEM, JSKISE, 32(2), pp.66—75, 2009.
lPractical setup time implementation in the roll-based manufacturing
practice having print operations, IE Interface, 22(1), pp. 85-94, 2009.
lQuality control system development corresponding to the floor status for
improving process control level, JKIPE, 13(2), pp. 59-67, 2008.
Books/
Papers
Major
Consulting
Practices
배재호 (Jae-Ho Bae, Ph. D.)
공학박사 (인공신경망, SCM, 성과관리)
2014 최우수논문상 (한국지식정보기술학회)
2012 설비관리 학술상 (대한설비관리학회)
2012 우수논문상 (한국지식정보기술학회)
2008 우수논문상 (대한설비관리학회)
l현) 대전과학기술대학교 물류유통경영과 교수/학과장
l현) 대한설비관리학회 이사, 편집위원
l전) EIB Korea, 상무이사
l전) PWC Consulting, Principal Consultant
l전) 아주대학교 e-Business 학부, 겸임교수
l전) 삼육대학교, 외래교수

3. Who am I ?
Books/
Papers
Major
Consulting
Practices
Researches or Consultations
l 2012.03~2012.12: 혜천대학교 학과/계열의 성과평가 및 목표수준 제시
l 2012.12~2012.12: 대중소협력재단, Dr. R&R (과제 수행 자문)
l 2010.06~2011.05: 중기청,고효율 에너지 기자재 규격에 적합한 LED 가로등 개발
l 2009.07~2009.10: 지경부, Innovation Mentor (자동차 산업 ISP 수립)
PI 및 업무 혁신 부문
l 2010.07~2010.12: 율촌화학 필름공장의 CTQ 도출 및 개선 방안 수립
l 2008.01~2008.12: 율촌화학 PI Master Plan 및 PI 1차 과제 수행
l 2007.09~2007.11: KT 고객경험관리를 통한 신제품/서비스 개발
l 2006.04~2006.06: 도레이새한 PI Master Plan 수립
l 2004.04~2004.06: 율촌화학 생산 부문 PI Master Plan 수립
l 2002.03~2002.06: SKT 내부 IT 고객 지원 프로세스 개선
l 2001.07~2001.09: CCKBC (코카콜라) 생산 전략 수립
알고리즘 설계 및 구현 부문
l 2007.03~2007.08: KCC의 생산계획 알고리즘 설계
l 2006.11~2007.03: 동진쎄미켐의 스케줄링 알고리즘 설계
l 2004.08~2005.01: KAC의 스케줄링 알고리즘 설계
l 1998.09~1999.10: 산업자원부, 한국형 ERP 개발을 위한 제조부문 설계
l 1997.06~1998.06: 농심의 재고 자동 보충 시스템/수요예측 시스템의 설계 및 개발
l 1996.11~1997.06: 정보통신부, 직렬통신 설비의 원격제어를 위한 converter 개발
l 1995.11~1996.03: 과학기술처, MMI 구현
기타 시스템 구축
l 동진쎄미켐의 품질관리 시스템 구축
l 율촌화학의 BI 시스템 구축
l 동진쎄미켐의 MES 시스템 구축
l MCM의 ERP Roll-out
l 율촌화학의 생산계획 시스템 구축
l 율촌화학의 MES 구축
l KAC의 MES 구축
l KT의 ERP 구축
l 동부전자의 ERP 구축
l 팬택의 ERP 구축
l 풀무원의 ERP 구축
배재호 (Jae-Ho Bae, Ph. D.)
공학박사 (인공신경망, SCM, 성과관리)
2014 최우수논문상 (한국지식정보기술학회)
2012 설비관리 학술상 (대한설비관리학회)
2012 우수논문상 (한국지식정보기술학회)
2008 우수논문상 (대한설비관리학회)
l현) 대전과학기술대학교 물류유통경영과 교수/학과장
l현) 대한설비관리학회 이사, 편집위원
l전) EIB Korea, 상무이사
l전) PWC Consulting, Principal Consultant
l전) 아주대학교 e-Business 학부, 겸임교수
l전) 삼육대학교, 외래교수

4. It is not the strongest of
the species that survives
nor the most intelligent, it
is those most adaptive
to change.
"
"
가장 강한 자가 살아 남는 것이 아니라,
변화에 잘 적응하는 자가 살아 남는다.
Charles Robert Darwin, 1809.2.12 ~ 1882.4.19
영국의 생물학자, 철학자. 주요 저서: 종의 기원

5. haos and Order
C IT Trend prediction

6. art 1
P2012년부터 지속적으로 제기되고 있는 Keyword는?
Mobile
Cloud
Social
IoT

7. eference 1
RGartner 선정 10대 전략 기술 추이
Rank 2011년 2012년 2013년 2014년
1 클라우드 컴퓨팅 미디어 태블릿 그 이후 모바일 대전 다양한 모바일 기기 관리
2 모바일 앱과 미디어 태블릿 모바일 중심 애플리케이션
과 인터페이스
모바일 앱 & HTML 5 모바일 앱과 애플리케이션
3 소셜 커뮤니케이션 및 협업 상황인식과 소셜이 결합된
사용자 경험
퍼스널 클라우드 만물 인터넷
4 비디오 사물인터넷 사물인터넷 하이브리드 클라우드와
서비스 브로커로서의 IT
5 차세대 분석 앱스토어와 마켓 플레이스 하이브리드 IT & 클라우드
컴퓨팅
클라우드/클라이언트
아키텍처
6 소셜 분석 차세대 분석 전략적 빅데이터 퍼스널 클라우드의 시대
7 상황인식 컴퓨팅 빅데이터 실용분석 소프트웨어 정의
8 스토리지급 메모리 인메모리 컴퓨팅 인메모리 컴퓨팅 웹 스케일 IT
9 유비쿼터스 컴퓨팅 저전력 서버 통합생태계 스마트 머신
10 패브릭 기반 컴퓨팅 및
인프라스트럭처
클라우드 컴퓨팅 엔터프라이즈 앱 스토어 3D 프린팅
http://www.alibabaoglan.com/blog/gartners-technology-predictions-2014-2015-2016/

8. 2014년 이후의 IT Trend는?
Converging Forces
Derivative Impact
Future Disruption
集約
派生
混亂
art 2
P

9. onverging Forces
CMobile Device Diversity & Management
Diverse Devices, Computing Style, and User
Environment
BYOD(Bring your own device)
Balancing between Privacy vs. Security

10. onverging Forces
CMobile Apps. and Applications
HTML5
Growing Mobile Apps. and declining Application
Voice & Video related apps. based on HTML5

11. onverging Forces
CThe Internet of Everything
IoT to IoE
Internet of People (1.11B people on Facebook, Mar. 2013)
Internet of Things (25B things by 2020)
Internet of Information (30T web pages in Google index, 2013)
Internet of Places (3B Foursqure check ins, Jan 2013)

12. onverging Forces
CHybrid Cloud and IT as Service Broker
Private Cloud vs. Public Cloud
Hybrid Cloud

13. erivative Impact
DCloud/Client Architecture
Changing envirionment of Cloud & Client System
Utilizing space and CPU performance of client
devices

14. erivative Impact
DThe Era of Personal Cloud
Independance from devices
Data repository on Personal cloud storage not
your PC
not a device-centric, but a service-centric Change

15. erivative Impact
DSoftware Defined Anything
SDx (Software Defined Everything/Anything)
Virtualization, Scalability & Elasticity

16. erivative Impact
DWeb-scale IT
Web-oriented Architectures
like Amazone, Google, Facebook, salesforce.com

17. uture Disruption
FSmart Machine
Siri, Waston
Era of smart machine will be come by 2020

18. uture Disruption
F3D Printing
Increase 75% in 2014, 200% by 2015
Custome manufacturing era

19. eview - IT Trends
R

20. Enterprise Application Development Trends?
6
art 3
P

21. rend 1T Enterprise Application Development Trends
Development moves to the client side
Mainframe C/S Web
Smart
Client

22. Business applications get
“consumerized”
rend 2T Enterprise Application Development Trends
Business applications of the past focused on function over form.
A well-designed, intuitive interface wasn’t so important as a powerful
applications, and users accepted this as normal.
These days, that’s changing. Users now expect the types of applications they
can access on their smartphone and tablets-powerful, yet easily
understandable applications.

23. Integration takes ceter stage
rend 3T Enterprise Application Development Trends
According to Gartner, “if application integration does not become a true
area of expertise, companies will find themselves at a serious competitive
disadvantage within the next few years.”

24. Enterprise applicatios get extended to
mobile devices
rend 4T Enterprise Application Development Trends
With the rise of mobile devices, the concept of a “typical user” has vanished.
The web is no longer limited to a desktop PC.
These days, a user might access a web application using one of many
devices.

25. Application development and delivery
shifts to the cloud
rend 5T Enterprise Application Development Trends
Now, am I saying that most businesses will shift their application
development to the cloud? Not at all.
However, I believe we’ll see a growing push towards this approach in the
coming year.

26. HTML5 gets widespread business
adoption
rend 6T Enterprise Application Development Trends
Browser Ver. Scores
Chrome 35 507
Firefox 29 467
Internet Explorer 11 376
Opera 21 496
Safari 7.0 397
Android 4.4 428
iOS 7.0 412
Windows Phone 8 332
source: http://html5test.com

27. Several keywords to predict future IT Trends
PaaS and BPM
Mobile and HTML5
Big data and Real-time Analytics
Elastic Application Platform (EAP)
BYOD vs. Security
art 4
P

28. Money Ball
New York Yankees
$114,457,768
vs
$39,722,689
Oakland Athletics
타율, 타점, 홈런
출루율, 장타율, 사사구율

29. Big Data
What is Big Data?
How big is BIG?

30. Big Data
“Big Data is the frontier of a firm’s ability to store, process, and access (SPA) all the data it
needs to operate effectively, make decisions, reduce risks, and server customers.”Forrester
BO
RING!“Big Data is general id defined as high volume, velocity and variety information assets that
demand cost-effective, innovative formas of information processing for enhanced insight and
decision making”
Gartner
“Big Data is data that exceeds the processing capacity of conventional database ststems.The
data is too big, moves too fast, or doesn’t fit the structures of your database architectures.To
gain value from this data, you must choose an alternative way to process it.”
O’Reilly
“Big Data is the data characterized by 3 attributes: volume, variety and velocity.”IBM
“Big Data is the data characterized by 4 key attributes: volume, variety, velocity and value.”Oracle

31. Big Data

32. Big Data
Byte : one grain of rice

33. Big Data
Byte : one grain of rice
Kilobyte : cup of rice

34. Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice

35. Big Data
ByteByte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Gigabyte : 3 Semi trucks

36. Big Data
ByteByte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Terabyte : 2 Container Ships
Gigabyte : 3 Semi trucks

37. Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Gigabyte : 3 Semi trucks

38. Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets West Coast States
Gigabyte : 3 Semi trucks

39. Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte
Zettabyte : Fills the Pacific Ocean
: Blankets West Coast States
Gigabyte : 3 Semi trucks

40. Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte
Zettabyte : Fills the Pacific Ocean
Yottabyte :A Earth Size Rice Ball
: Blankets West Coast States
Gigabyte : 3 Semi trucks

41. Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte
Zettabyte : Fills the Pacific Ocean
Yottabyte :A Earth Size Rice Ball
: Blankets West Coast States
Gigabyte : 3 Semi trucks

42. Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte
Zettabyte : Fills the Pacific Ocean
Yottabyte :A Earth Size Rice Ball
: Blankets West Coast States
Gigabyte : 3 Semi trucks

43. Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte
Zettabyte : Fills the Pacific Ocean
Yottabyte :A Earth Size Rice Ball
: Blankets West Coast States
Gigabyte : 3 Semi trucks

44. Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte
Zettabyte : Fills the Pacific Ocean
Yottabyte :A Earth Size Rice Ball
: Blankets West Coast States
Gigabyte : 3 Semi trucks

45. Big Data
Byte : one grain of rice
Kilobyte : cup of rice
Megabyte : 8 bags of rice
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte
Zettabyte : Fills the Pacific Ocean
Yottabyte :A Earth Size Rice Ball
: Blankets West Coast States
Gigabyte : 3 Semi trucks

46. Big Data
Exabyte
Zettabyte : Fills the Pacific Ocean
: Blankets West Coast States

47. Big Data
is not about the size of the data.
is about the value within the data.
is good for correlation analysis
is not good for cause and effect analysis

48. Most people don’t know what to do with all the data
that they already have.
Big Data is not big, if you know how to use it.

49. Questions,
Q & A
Answers

50. Your Desire,
Our Instinct!!
Thank you for your patient listening
Jun. 2014, Jaeho BAE@Osan.

51. IT Trend 조망
End of Document
Created by JHBae.