4차산업혁명포럼

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3 SYNOPSIS 01_ 조직 공동대회장 : 안문석 The e-bridge 편집위원장 / 고려대학교명예교수이정배 e-bridge 연구회위원장 / 부산외국어대학교교수 조직위원장 : 이영상데이터스트림즈대표 학술위원장 : 고한석고려대학교교수 02_ 등록안내 사전및현장등록 : 일반 10만원, 학생 1만원 ( 학생증제시 ) 사전등록방법 - 한국정보처리학회홈페이지 ( 의 4차산업혁명포럼 2018 배너클릭 - 무통장입금 : 국민은행 사 ) 한국정보처리학회 03_ 행사문의 한국정보처리학회김은순국장 ( , uskim@kips.or.kr) 04_ 오시는길 고려대학교자연계캠퍼스하나스퀘어강당 (02841) 서울시성북구안암로 145

4 PROGRAM 시간 1:00-1:30 4차산업혁명포럼 2018 Bridge to the 4 th Industrial Revolution 세부내용등록 1:30-1:40 1:40-2:00 2:00-2:30 2:30-4:00 개회식 사회 : 정진욱 ( 성균관대학교명예교수 ) 4 차산업혁명시대의국가정책혁신전략 사회 : 김명준 ( 소프트웨어정책연구소소장 ) 개회사 환영사 기조강연 발제발표 (60 분 ) 패널토의 (30 분 ) - 이정배공동대회장 (e-bridge 연구회위원장, 부산외대교수 ) - 안문석공동대회장 (The e-bridge 편집위원장, 고려대명예교수 ) 4 차산업혁명시대! 대학교육의미래와변화 - 염재호 ( 고려대학교총장 ) 1. I-KOREA 4.0 소프트웨어정책추진방향 - 노경원 ( 과학기술정보통신부 SW 정책관 ) 2. 4 차산업혁명시대의전자정부혁신 - 정윤기 ( 행정안전부전자정부국국장 ) 3. 미래를대비하는 SW 교육, 현황과과제 - 변태준 ( 한국교육학술정보원교육정보본부본부장 ) 4. 지능정보시스템적용사례 : 우정사업본부 - 강성주 ( 우정사업본부본부장 ) 종합토론 4:00-4:30 Booth 전시탐방 (Coffee Break) (30 분 ) 4:30-5:50 ICBM 시대의기술혁신전략 사회 : 김문조 ( 강원대학교석좌교수 ) 발제발표 (70 분 ) 패널토의 (10 분 ) 1. Successful startups in computer vision - Shmuel Peleg (Hebrew Univ., Briefcam) 2. Deep learning-based video analytic strategies - Hanseok Ko (Korea Univ., ML&BigData) 3. 4차산업시대, Data를어떻게다룰것인가? - 이영상 ( 데이터스트림즈대표 ) Q/A 5:50-6:00 폐회사 이정배공동대회장 (e-bridge 연구회위원장 )

5 CONTENTS 4차산업혁명포럼 2018 Bridge to the 4 th Industrial Revolution 기조강연 / 1 - 사회 : 정진욱 ( 성균관대학교명예교수 ) 4차산업혁명시대! 대학교육의미래와변화 / 3 - 염재호 ( 고려대학교총장 ) 01 4 차산업혁명시대의국가정책혁신전략 / 23 - 사회 : 김명준 ( 소프트웨어정책연구소소장 ) 1. I-KOREA 4.0 소프트웨어정책추진방향 / 25 - 노경원 ( 과학기술정보통신부 SW정책관 ) 2. 4차산업혁명시대의전자정부혁신 / 43 - 정윤기 ( 행정안전부전자정부국국장 ) 3. 미래를대비하는 SW 교육, 현황과과제 / 53 - 변태준 ( 한국교육학술정보원교육정보본부본부장 ) 4. 지능정보시스템적용사례 : 우정사업본부 / 71 - 강성주 ( 우정사업본부본부장 ) 02 ICBM 시대의기술혁신전략 / 83 - 사회 : 김문조 ( 강원대학교석좌교수 ) 1. Successful startups in computer vision / 85 - Shmuel Peleg (Hebrew Univ., Briefcam) 2. Deep learning-based video analytic strategies / 93 - Hanseok Ko (Korea Univ., ML&BigData) 3. 4차산업시대, Data를어떻게다룰것인가? / 이영상 ( 데이터스트림즈대표 )

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7 4차산업혁명포럼 2018 Bridge to the 4 th Industrial Revolution 기조강연 - 사회 : 정진욱 ( 성균관대학교명예교수 ) 1. 4 차산업혁명시대! 대학교육의미래와변화

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9 강사소개 4 차산업혁명시대! 대학교육의미래와변화 abstract 고려대학교제 19 대총장염재호 ( 廉載鎬 ) 21세기는또하나의문명사적대전환기를맞게된다. 인공지능, IOT, 로봇, 생명의연장등으로사회및문명시스템에획기적인변화가일어나고있다. Ray Kurzweil 등은 2050년즈음에는특이점 (singularity) 이나타나서, 7만년전네안데르탈인과현재인류와의차이이상으로지금의인류와전혀다른모습의, 기계가내재화된신인류의출현까지예고하고있다. 향후삼십년안에일, 교육, 가족, 사회적관계등모든면에서획기적인변화가일어날것이다. 전통적인개념의정부, 대학, 기업의역할도변화하게되고, 이런변화에어떻게대응하는가에따라각조직은진화하거나소멸하게될것이다. 산업혁명이후정부대학, 기업의기능, 역할, 운영방식등이빠르게변화한것처럼, 각조직은새로운사회적양식을만들어내고, 이에적응하면서진화하게될것이다. 이발표에서는거시적관점에서 21세기, 정부, 사회, 기업의진화모형을예견하고, 혁신적이고선도적인대학의대응방안을탐구해보고자한다. 학력 고려대학교일반대학원행정학과졸업 (1980 행정학석사 ) 미국 Stanford University, Department of Political Science 졸업 (1989 정치학박사 ) 교내경력 고려대학교정경대학행정학과조교수, 부교수, 교수 (1990-현재) 고려대학교대교협평가준비위원장 ( ) 고려대학교행정대외부총장 ( ) 고려대학교총장 (2015-현재) 국내. 외학술연구활동 중국인민대학객좌교수 (2001-현재) 한국정책학회회장 (2007) 교외경력 한국고등교육재단이사 (1997-현재) 행복나눔재단이사 (2010-현재) 서울연구원이사 (2011-현재) 한국연구재단정책자문위원 (2012-현재) 기초과학연구원정책자문위원 (2012-현재) Universitas 21(U21) 집행위원 (2015 현재 ) 환태평양대학협회 (APRU) 운영위원 (2017 현재 ) 감사원혁신 발전위원회위원장 (2017 현재 )

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16 Steve Jobs: "Getting fired from Apple was the best thing that could have ever happened to me. The heaviness of being successful was replaced by the lightness of being a beginner again. It freed me to enter one of the most creative periods of my life."

17 I am enough of the artist to draw freely upon my imagination. Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world

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21 ,,,,,,,,,,,

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24 Domestic Industry Global Industry + many more.. + many more.. KU

25 Archive,, Community Realization,, As-is To-be

26 X-Garage π- Ville

27 SudangSamyang Faculty House New KU Medical Center Science π-park CJ Creatorlibrary,,

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29 4차산업혁명포럼 2018 Bridge to the 4 th Industrial Revolution SESSION 01 4 차산업혁명시대의국가정책혁신전략 - 사회 : 김명준 ( 소프트웨어정책연구소소장 ) 1. I-KOREA 4.0 소프트웨어정책추진방향 2. 4차산업혁명시대의전자정부혁신 3. 미래를대비하는 SW 교육, 현황과과제 4. 지능정보시스템적용사례 : 우정사업본부

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31 4차산업혁명포럼 2018 Bridge to the 4 th Industrial Revolution SESSION 01 4 차산업혁명시대의국가정책혁신전략 1 I-KOREA 4.0 소프트웨어정책추진방향 노경원 과학기술정보통신부 SW 정책관

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33 강사소개 I-KOREA 4.0 소프트웨어정책추진방향 abstract 4차산업혁명시대로의진입에서디지털변혁을이끄는동인인지능정보신기술의근간은 SW로데이터 (D), 네트워크 (N), 인공지능 (A) 이소프트웨어를통해초연결지능화혁명을실현가능하게해줍니다. 이제 SW 생산 유통 활용패러다임이전환되고, 각산업별로새로운 SW융합시장이가시화됨으로인해 4차산업혁명을대비한 SW인재 기술경쟁이가속화되고있습니다. 이에따른 4차산업혁명시대의 SW융합신시장창출을위한소프트웨어정책방향을조망합니다. 노경원과학기술정보통신부 SW 정책관 Bio 행정고등고시재경직에합격한후, 과학기술처 ( 과학기술부 ) 연구기획과에서공무원생활을시작해원자력안전과, 공공기술개발과, 생명환경기술과, 정책총괄과등에서근무했습니다. 그리고교육과학기술부전략기술개발관, 미래창조과학부창조경제기획국장, IAEA( 오스트리아빈소재 ) 파견등을거쳐현재는우리나라 SW 및디지털콘텐츠정책을총괄하는과학기술정보통신부소프트웨어정책관으로재직중입니다.

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49 4차산업혁명포럼 2018 th Bridge to the 4 Industrial Revolution SESSION 01 4 차산업혁명시대의국가정책혁신전략 2 4 차산업혁명시대의전자정부혁신 정윤기 행정안전부전자정부국국장

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51 강사소개 4 차산업혁명시대의전자정부혁신 abstract ICT기술을활용하여행정의효율성과정부투명성, 민주성을동시에증진시키는전자정부가한국에서추진된지올해로 50년을맞이하였습니다. 한국전자정부의그간역사와성과를살펴보고, 4차산업혁명시대를맞이하여온라인위주서비스를넘어국민들이언제어디서나체감할수있는지능형정부를소개합니다. 스마트네이션, 지능형정부대표사례들을중심으로한국전자정부의혁신방향을조망합니다. 정윤기행정안전부전자정부국국장 Bio 1990년총무처입직후인사, 조직, 전자정부, 지방행정등의책무를맡아추진하였습니다. 행정업무에정보화마인드를접목하여보다세련되고효율적이며국민이편리한정부로발전시키기위해노력하였고, 현재는우리나라전자정부를총괄하는행정안전부전자정부국장으로재직중입니다.

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59 4차산업혁명포럼 2018 Bridge to the 4 th Industrial Revolution SESSION 01 4 차산업혁명시대의국가정책혁신전략 3 미래를대비하는 SW 교육, 현황과과제 변태준 한국교육학술정보원교육정보본부본부장

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61 강사소개 미래를대비하는 SW 교육, 현황과과제 abstract 미래의지능정보사회는학습한내용을바탕으로다양한지식을통합하여문제를해결하고, 새로운지식과가치를생성할수있는창의융합형인재가필요합니다. 이를대비하기위하여전세계적으로 SW교육에관심을갖고있는데, 우리나라도 2015교육과정이적용되면서 SW교육을필수로이수하도록하고있습니다. 이에 SW교육의필요성과교육현장에서적용을위한준비현황, 그리고고민과과제에대하여함께살펴보고자합니다. 변태준한국교육학술정보원교육정보본부본부장 Bio 1993년현장교사로교육현장에입직하였고, 2000년부터한국교육학술정보원에서초중등학교의정보화관련업무를담당하여왔습니다. 주로교원역량개발지원, 교원능력개발평가, 이러닝콘텐츠개발및질관리, 교원 SW 개발지원, 정보연수과정개발등의업무를추진하여왔습니다. 현재는초중등학교의교육정보업무지원을전담하는교육정보본부본부장으로재직중이며, 디지털교과서, SW교육, 사이버학습, 교육정보서비스운영등의업무를총괄하고있습니다.

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77 4차산업혁명포럼 2018 Bridge to the 4 th Industrial Revolution SESSION 01 4 차산업혁명시대의국가정책혁신전략 4 지능정보시스템적용사례 : 우정사업본부 강성주 우정사업본부본부장

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79 강사소개 지능정보시스템적용사례 : 우정사업본부 abstract 4차산업혁명시대에대응하기위해한국우정도변화하고있습니다. 초소형전기차, 드론배송, 스마트우편함등우편업무에우정신기술을도입하고, 핀테크 블록체인기술등을활용하여금융업무도혁신하고있습니다. 또한, 빅데이터센터 ( 우체국경기지수 ), 틴틴우체국 ( 미래체험관 ), AI우표디자인공모대전등다양한분야에지능정보기술을활용하여국민과가까워지고있습니다. 미래를위해역동적으로변화하고있는우정사업의모습을소개합니다. 강성주우정사업본부본부장 Bio 1987년정보통신부에서공직생활을시작하여, 행정안전부정보기반정책관, 미래창조과학부연구성과정책관, 과기정통부정보통신산업정책관등을역임하시며정보화분야전문가로서의길을걸었습니다. 美 Syracuse University에서정보시스템행정학석사, 美 Pennsylvania State University에서공공관리박사과정을수료하였고, 가톨릭대학교행정대학원에서전자정부론강의를하였습니다. 현재는우정사업본부장으로재직하며대한민국우정사업을총괄하고있습니다.

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89 4차산업혁명포럼 2018 th Bridge to the 4 Industrial Revolution SESSION 02 ICBM 시대의기술혁신전략 - 사회 : 김문조 ( 강원대학교석좌교수 ) 1. Successful startups in computer vision 2. Deep learning-based video analytic strategies 3. 4 차산업시대, Data 를어떻게다룰것인가?

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91 4차산업혁명포럼 2018 Bridge to the 4 th Industrial Revolution SESSION 02 ICBM 시대의기술혁신전략 1 Successful startups in computer vision Shmuel Peleg (Hebrew Univ., Briefcam)

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93 강사소개 Successful Startups in Computer Vision abstract The areas of Computer Vision and Machine Learning, which wereconsidered impractical only several years ago, have recently matured- enabling their useful application in real products. The HebrewUniversity was fortunate to be a basis for several startupcompanies, two of which where recently acquired. Mobileye,developing autonomous driving, was acquired by Intel, and Briefcam,developing video summarization, was acquired by Canon. I will coversome background behind the successful startups, Shmuel Peleg Hebrew Univ., Briefcam and describe apossible technology for the next startup. Bio Shmuel Peleg received his Ph.D. in Computer Science from theuniversity of Maryland in 1979, and since 1981 he is a facultymember at the Hebrew University of Jerusalem. Shmuel published over 150 technical papers in computer vision andimage processing, and holds 23 US patents. He founded severalstartup companies, and his latest company, Briefcam, was acquired bycanon on Shmuel served as an editor and committee member of numerous venues,e.g. general co-chair of ICPR 1994, CVPR 2011, ICCP 2013, and CVPR2018.

94 Successful Startups in Computer Vision Shmuel Peleg The Hebrew University of Jerusalem The Hebrew University of Jerusalem Founded Campuses 23,000 Students 1,000 Faculty Members Extensive Technology Transfer Activity o Over 1/3 of all academic research In Israel o Over 1/3 of PhD students in Israel

95 The Hebrew University of Jerusalem School of Computer Science Companies in Computer Vision MobilEye (Shashua): Autonomous Driving. Acquired by Intel, BriefCam (Peleg): Video Summary. Acquired by Canon, Orcam (Shashua): Aid to the visually impaired HumanEyes (Peleg): Panoramic Stereo Imaging Autonomous Driving MobilEye Founded: 1999, 1000 employees (2017) Autonomous driving and vision based mapping : Founded by A. Shashua ($1M private investors) 2014: IPO $5.3 Billion (Largest Israeli IPO) 2017: Acquired by Intel, $15B

96 MobilEye: Why use Vision? Google uses lasers, others use radar Roads are built for drivers using eyesight Camera is the richest sensor at lowest cost Video Synopsis BriefCam founded 2008, 60 Employees (2018) Video Surveillance Summarization & Indexing 2008: Founded by Prof. Shmuel Peleg 2013: Help solve Boston Bombing 2018: Acquired by Canon

97 More Video than People can View London Tube Terrorists July 2005 Dubai Assassination January 2010 Millions of Cameras recording 365/7 It took weeks to find these events in video archives Video Synopsis Helped Solve Boston Bombing in 2 Days Massachusetts governor acknowledge use of Video Synopsis in the investigation Currently used by law enforcement agencies, and deployed worldwide NY: Statue of Liberty & Empire States Building Many safe cities, airports, and police departments KNPA

98 Visual Speech Enhancement Just filed as a patent Maybe Next Startup Most vision research has ignored sound for many years Vision can help improve the speech of a visible speaker Visual Speech Enhancement Use a camera to look at the speaker Noisy Speech Microphone & Camera Neural Network Clean Speech hello Speech Enhancement hello Noise

99 4차산업혁명포럼 2018 Bridge to the 4 th Industrial Revolution SESSION 02 ICBM 시대의기술혁신전략 2 Deep learning-based video analytic strategies Hanseok Ko (Korea Univ., ML&BigData)

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101 강사소개 Deep learning based video analytic strategies abstract A movie clip is intended to capture and present a meaningful (or significant) story in video to be recognized and understood by human audience. What if we substitute the task of human audience with that of an intelligent machine or robot capable of capturing and processing the semantic information in terms of audio and video cues contained in the video? By using both auditory and visual means, human brain processes the audio (sound, speech) and video (background image scene, moving video objects, written characters) modalities to extract the spatial and temporal semantic information, Hanseok Ko Korea Univ., ML&BigData that are contextually complementary and robust. Smart machines equipped with audiovisual multisensors (e.g. CCTV equipped with cameras and microphones) should be capable of achieving the same task. An appropriate fusion strategy combining the audio and visual information would be a key component in developing such artificial general intelligent (AGI) systems. This talk reviews the challenges of current video analytics schemes and explores various sensor fusion techniques to combine the audio-visual information cues for video content analytics task Bio Hanseok Ko is Professor of Electrical and Computer Engineering and Director of Machine Learning Institute at Korea University. He received a B.S. degree from Carnegie Mellon University in 1982, MS degree from the Johns Hopkins University, and Ph.D in ECE from the Catholic University of America in 1986 and 1992 respectively. He joined the faculty of ECE, Korea University, in He was a visiting professor at the Center for Language and Speech Processing, JHU in 2001 and CS Dept, University of Maryland, in He has been credited as the main developer of core audiospeech interface for Hyundae-Kia Motors. He served as Director of STW-KU Intelligent Signal Processing Research Center, sponsored by Samsung in 2008~2013, to engage in research on the CCTV multimodal technologies addressing image and video analytics. He served as General Organizing Chair of IEEE AVSS 2014, Program Chair of IEEE Multisensor Fusion and Integration in 2008 and 2017, and co-general Organizing Chair of IEEE ICASSP 2018 Calgary. He was a founding member of the JCN journal and Editor for SJW and E-Bridge Journals. He is currently serving as Guest-Editor for Sensors Journal on the special issue addressing multisensor fusion strategies. He was awarded Research Excellence Award by Maeil Business in He is a Fellow of IET with his research interest in audio-visual signal processing and machine learning for video analytics and human-machine interface.

102 4th Industrial Revolution Talk Deep Learning based Video Analytic Strategies Hanseok Ko School of Electrical Engineering Machine Intelligence & Big Data Institute Korea University, Seoul How human perceives *WOCP JCU PCVWTCN CDKNKV[ VQ RGTEGKXG VJG GPXKTQPOGPV HQT UEGPG WPFGTUVCPFKPI CPF UWOOCTK\CVKQP *WOCP JCU VGPFGPE[ VQ TGEQIPK\G KOCIGU GXGP KH VJG[ CTG RTGUGPVGF KP UNKIJVN[ FKHHGTGPV HQTO

103 How human perceives *WOCP JCU PCVWTCN CDKNKV[ VQ RGTEGKXG VJG GPXKTQPOGPV HQT UEGPG WPFGTUVCPFKPI CPF UWOOCTK\CVKQP *WOCP RGTEGRVKQP KU HCUV 0GWTQPU HKTG CV OQUV VKOGU C UGEQPF *WOCPU UQNXG RGTEGRVKQP KP UGEQPFU # DKI NC[GT PGWTCN PGVYQTM ECP FQ VQQ 3 Perception by human Cognitive Neuroscience: Human has natural ability to perceive the environment for scene understanding and summarization (memorization) Intention Prior knowledge Experience Sound Flashing light Presence of unexpected objects

104 What is Multimodal Fusion? Multimodality The way in which information is received or experienced by multiple sensory (audio, visual, etc). Fusion A means or instrumentality combining information from multisensory resources such as audio and visual means to: Achieving robustness to environmental and sensor noise. Facilitating natural human computer interaction. Exploiting complementary information across modalities. 5 Real world tasks by AV Fusion Affect recognition Emotion Persuasion Personality traits Media description Image captioning Video captioning Visual Question Answering Event recognition Action recognition Segmentation Multimedia information retrieval Content based/cross-media

105 Multimodal Representations Heterogonous data: ¾ Verbal modality We saw the yellow dog ¾ Vocal modality ¾ Visual modality Representation: Computer interpretable description of the multimodal data (e.g., vector, tensor) Challenges: I Sym mbo and signals mb mbo I. Symbols II. Different granularities III. Static and sequential IV. Different noise distribution V. Unbalanced proportions TRECVID: Video Analytics Challenge Training / Evaluation database (2016 s) HAVIC (NIST), YFCC100M (Yahoo Corp.) Heterogeneous Audio Visual Internet Collection (HAVIC) Default set User generated video collected from internet (Youtube) Testing set : 100,000 video clips (EvalFull) / 16,000 (EvalSub) Training set: 10 and 100 video clips for each video event (10EX / 100EX) Yahoo Flickr Creative Commons 100M User generated video collected from internet (Flickr) Testing set : 100,000 video clips (EvalFull) / 16,000 (EvalSub)

106 Examine AV fusion by four examples 9 Final Thoughts X Humans are the ultimate intelligent systems equipped with multimodal sensors and the capability to seamlessly process, analyze, learn and respond to multimodal cues. X Machine learning algorithms mimicking human brain can make machines closer to human for providing perception power by exploiting audio and visual processing. X For human activity recognition, multimodal analysis combining image and audio sources is better than image based analysis alone. X Higher level features can be developed by various approaches including Bag-ofWord descriptors and Deep Neural Net, and fusions for creating semantic structures. X TRECVID competition challenges how accurately perform scene understanding by generating descriptive metadata of the video clips. X Human Activities contain streaming video over time. Current work can do better with time-dependent mapping (e.g. trajectory) as part of the semantic structure. X Much more exciting technologies in audio-visual fusion technologies by using the advancement of machine learning

107 4차산업혁명포럼 2018 Bridge to the 4 th Industrial Revolution SESSION 02 ICBM 시대의기술혁신전략 3 4 차산업시대, Data 를어떻게다룰것인가? 이영상 ( 데이터스트림즈대표 )

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109 강사소개 3. 4차산업시대, Data를어떻게다룰것인가? abstract 4차산업혁명 데이터시대가도래함에따라, 폭발적으로늘어나고있는데이터를얼마나효율적으로잘관리하는지에따라조직의성패가좌우되기도합니다. 빅데이터, 클라우드서비스시대에이르러당면하게되는데이터관련과제중, 가장큰비율을차지하는것은 곳곳에산재된많은데이터를얼마나쉽게, 제때, 정확히활용할수있는가? 즉, 데이터거버넌스의성숙도문제입니다. 기업또는기관, 조직이다루는데이터가얼마나유효한지에따라그데이터자산의가치는 이영상데이터스트림즈대표 증가합니다. 이에따라본발표는데이터거버넌스의정의와필요요소, 구현방안, 구축효과등을살펴보고자합니다. Bio 이영상대표이사는미시건주립대학교전자공학석사, 한국과학기술원 (KAIST) 전자공학박사과정을수료하였으며, 2001년, 데이터관리전문회사 인 ( 주 ) 데이터스트림즈를창업해현재는국내데이터통합및관리분야에국내독보적인회사로성장시킨 IT전문가입니다. 뿐만아니라, KOTRA SW 수출자문위원, 한국빅데이터협회부회장, 한국상용SW협회및한국 PMO협회회장등을역임한바있습니다.

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111 Introduction CEO Introduction Young-sang Lee CEO - CEO/President of DataStreams, Corp. - Vice Chairman of Korea Big Data Society (2013~Present) - Board Member of KINTEX (Korea INternational Exhibition & convention center) (2017 ~ ) - Honoury Chairman of Korea PMO Association (2013~2016) - Honoury Chairman of KOSEA (Korea Software Enterprise Association) (2012~2014) - Advisory Committee of KOTRA SW Export (2012) 3 Introduction

112 Introduction 5 Big Data Mainframe Clients/Server WEB Cloud - Social IOT Kilobytes Megabytes Gigabytes Tera Peta Exa Zetta Digital Banking based on Fintech/Big Data/AI Stronger Regulation Data as Asset BUSINESS TECHNOLOGY USERS VALUE TECHNOLOGIES SOURCES 1960s-1970s Few Employees Back Office Automation OS/360 MAINFRAME 1980s Many Employees Front Office Productivity CLIENT-SERVER 1990s Customers/ Consumers E-Commer ce 2007 Business Ecosystems 2011 Communities & Society Social Line-of-Business Engagement Self-Service SOCIAL 2014 Devices & Machines Real-Time Optimization INTERNET OF THINGS

113 Big Data Get Business insight from Data Analysis Data Analysis with legacy data, ERP data, CRM data Digital Data Banking Analysis based with on large Fintech/Big volume, velocity, Data/AI variety and valuable data Stronger Regulation Data as Asset Data Analysis 7 Big Data Financial achievements of the enterprises using data (Study of EIU) Digital Banking based on Fintech/Big Data/AI Stronger r Regulation Data as Asset EIU (Economist Intelligence Unit) : Expert group of analyzing economic environment of global 60 countries

114 Big Data Digital Banking based on Fintech/Big Data/AI Stronger Regulation Data as Asset <Source : Gartner Research, 2015> 9 Cloud Computing

115 Cloud Computing 65% 42% 53% 73% Is data safely protected while in motion, in use or stored in the cl oud? How is the availability of data in the cloud assured? How are assurance levels effectively managed by the cloud provi der? Can I get a snapshot of the cloud provider s security managemen t capabilities at any point? Can the cloud provider demonstrate that regulatory controls are implemented effectively and sustainably? Who owns/accesses/edits/modifies my data in the cloud? Data does not equal a one-size fits all model How do you measure policy enforcement? How do you enforce policy? Cloud Computing

116 Cloud Computing Cloud Computing

117 Data Governance Data Governance?? Invisible Internal?.? Incorrect Blueprints? Lack of Control System,

118 Data Governance? Executives Data Integration & Consolidation Digital Banking based on Fintech/Big Data/AI Stronger Regulation Data as Asset RM BI a/ EIS ODM DSS KM Risk AUDIT Data Analyst Marketer Data Warehouse (Data Integration Hub) ODM(Operational Decision Management) DSS(Decision Support System) EIS(Executive Information System) DBA 17 Data Governance Through Data Governance Policy and Guideline Role and Process Method and Tool Digital Banking n based WHO on Fintech/Big WHAT Data/AI WHEN Stronger Regulation Data as Asset WHERE HOW WHY.. Who, when, where How and why to make Where to transfer Who is utilizing The goal is to manage well

119 Data Governance Data Management can gain advantages through Data Governance. 1) (,, ). 2) Data Glossary,,.. 3) ETL. 4). 5). Data Management Data Governance Metadata / Policy SSO (Admin) Location Path Ownership Lifecycle Structure Rationali -zation Integration (Standard Dictionary, Model, DBMS) Single View Utilization Utilization Meta (Description, Relation,Connection) Application- Based (CRUD) Role Authority Work Flow Statistical Meta (Definition, System, Computation), ETL-based When Who Where What How Real-Time Integration Batch Integration Real-Time Analysis Model Visualization Information Why 19 Data Governance Data Govern Data View & Direction WHO WHAT WHEN WHERE HOW WHY Digital Data Banking based on Fintech/Big Data/AI Management Stronger Solution Regulation Data as Asset Data Classification Data Authority Data Standard Data Structure Data Quality Master Data Data Integration Data Lineage Data Utilization Data Security Application & Biz Data Management Asset Finance Sales Production Process Material Investment Economics Currency,,,,

120 Implementation List of potential questions for business people Sample industryspecific questions Sample documentation Examples from other projects Diagnostic Aid Descriptions of each DG capability Descriptions of typical DG roles and responsibilities Key questions by DG capability DG Design Guidelines & Best Practices by Capability Diagnostics by capability Optional way to score capabilities Sample architectures, blueprints & roadmaps Sample organization charts Software tools guidelines & recommendations Sample recommendations Sample implementation work plans Estimating Guidelines Sample Reports Library of completed projects Implementation Word & Terms Domain & Codes Model Entity / Attribute DBMS Tables / Column Development Test Metastream Operational Extract Interfacing Type-in Standard Dictionary Meta-Data Repository Model & DB Catalog Interface Report/OLAP ETL UpLoad Words/TermsC ode/domain Messages Descriptions Table/Column/ Partition/Function /View Report - Data Jobs Data Standard-Model MDM DQM Data Linage

121 Implementation To secure enterprise information assets, we need Data standard & quality assurance management which ensures data consistency Improving accessibility and impact discovery Audit & update data structure under work-flow management 23 Implementation Data quality management system to ensure high-quality data, enhances business productivity, prevent the error data, and provide the reliable IT services

122 Implementation OLTP System ODS DW / DM Management Report Customer Ledger Sales per Customer Daily New product Sales Results Contracts Ledger Period/Product/Customer Performance Analysis Credit Ledger Daily Sales Ledger Indivisual Credit Ledger Customer Commodity Contracts Ledger Cooperation Daily Sales Ledger Customer????? Data Standardization Data Administration Data Quality Data Governance Master Data Management Data Integration Data Lineage Data Security Metadata Management System Law / Regulation / Rule / Process Note: Data Program Path 25 Implementation Cost of Quality and 3 Rules of Quality in <Quality Story> To repair a defective product immediately 1 dollar is spent, To hide the problem and try to repair the defective product after completion 10 dollar is spent, If the defective product is sent to customer and the customer complains, 100 dollar is spent If a problem is found during planning 1 dollar is lost, If a problem is found during execution 10 dollar is lost, If a problem is found after work is done, 100 dollar is lost The 1:10:100 Rule can be explained by the safety issue of Toyota cars, which has became a global issue. When the problem occurred, if it was solved immediately Toyota would not lose an astronomical amount of money. Thus this issue dramatically show the 1:10:100 Rule

123 DataStreams HQ Chungho-nais B/D 6F, 28 Saimdang-ro, Seocho-gu, Seoul, Korea (DataStreams R&D U-Space mall #2 B-601, 670 Daewangpangyo-ro, Bundang-gu, Seongnam, Korea DataStreams China Pohang center 28F, Wangjing technology business park, Chaoyang District, Beijing T E ysjeong@datastreams.co.kr DataStreams Vietnam 1806, CMC Building, Duy Tan St., Cau Giay Dist, Hanoi T , E tuan.ta@datastreams.co.kr DataStreams Japan 18F, Shinkasumigaseki Bldg., Kasumigaseki Chiyoda-ku, Tokyo, T E kaneday@ais-info.co.jp DataStreams USA Contact nd Avenue, San Francisco, CA T E jylee@datastreamsglobal.com

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