1 2 3 Trends 4 OmniChannel IOT 5

목차 1. 데이터시대의 도래 2. 전통적인 빅데이터 분석 및 활용 3. 향후 빅데이터 분석

Big Data? Volume 데이터 크기, 다양성 및 속도에 데이터의 복잡성과 가치를 함께 포함하는 개념으로

Why Data ? From IT to Business Productivity Internet of Things u데이터 폭증의 시대

Internet of Things(Io. T) World Ø Any Thing Ø Any Device Ø Any One

스마트 시대에서 강자? 산업혁명시기에는 증기가 세상을 바꾸는 동력이었고, 기술의 시기에는 IT가 삶을 바꿨고, 스마트

목차 1. 데이터시대의 도래 2. 전통적인 빅데이터 분석 및 활용 3. 빅데이터 분석 Trends

Google 독감 예측 사례 Google 검색어 분석 © 2016 Data. Streams Corp. All Rights

Harrah’s Entertainment 사례 © 2016 Data. Streams Corp. All Rights Reserved. 11

Volvo Motor Company 사례 기존 50만대 판매시에 알 수 있던 결함 정보를 1000대 판매시점에서

목차 1. 데이터시대의 도래 2. 전통적인 빅데이터 분석 및 활용 3. 빅데이터 분석 향후

IT Trends © 2016 Data. Streams Corp. All Rights Reserved. 19

Big Data Cloud Strategy © 2016 Data. Streams Corp. All Rights Reserved.

Personal Technology Market “By 2018, consumer spending on personal technology will reach $3. 1

Customer Behavior Trends 소비자는 온라인, 오프라인, 모바일 등 다양한 경로를 넘나들며 상품을 검색하고 구매

Real time Data analysis Needs 실시간 데이터분석의 Needs 새로운 빅데이터 솔루션 IT 담당자 비즈니스

Omni-Channel 다양한 고객의 접근 채널을 On-Line 과 Off-Line에 상관없이 비즈니스 주체인 고객을 중심으로 일원화

Smart Digital Marketing Data Collection Strategy Roll out real-time and future marketing plans and

Omni-Channel Marketing Sensing & Integration 1)Customer Data Platform Smart Foot Traffic Beacon Digital Signage

Big data analysis platform Big Data analysis Platform Store Collect Real-Time Collection (Delta. Stream)

Real Time Io. T Convergence Analyst’s Screen Connectors Benchmark results Criteria Task: Log data

Unified Big Data Platform Real Time Monitoring • Performance Balancing • Emergency Management Data

One stop Data Governance One Stop Solution for Data Governance Admin & Monitoring Integrate

Consent Management § Protecting privacy is a opposite concept of connecting and sharing. §

감사합니다. www. datastreams. co. kr 본 사 : (주)데이터스트림즈 서울시 서초구 사임당로 28 청호나이스빌딩

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Big Data? Volume 데이터 크기, 다양성 및 속도에 데이터의 복잡성과 가치를 함께 포함하는 개념으로 확대되고 있음 Volume amount of data Terabytes Records Transaction Tables, Files Velocity speed of data in and out Variety Structured Unstructured Semistructured All the above 3 V’s Of Big Data Velocity Value Batch Near-real time Real time Streaming range of data type 특징 and Value importance utility and worth Volume 기존 DBMS로 처리 불가한 대용량 데이터 증가 Variety 기술 발전에 따른 데이터의 다양화 Velocity 데이터 처리 및 분석 속도 중요성 증가 Value © 2016 Data. Streams Corp. All Rights Reserved. 설명 수집� 분석되는 데이터의 가치 고려 4

Why Data ? From IT to Business Productivity Internet of Things u데이터 폭증의 시대 Internet, Cloud, Bigdata, Mobile u 스마트폰이 지구촌의 풍속도 를 바꾼 것처럼 빅데이터가 삶의 구석구석까지 바꿔놓을 거라는 전망 u 빅데이터는 인간이 디지털에 남긴 흔적을 모으는 것 Big Data 3 st Platform Mobile / Internet Client / Server 2 st Platform Mainframe Terminal 1 st Platform © 2016 Data. Streams Corp. All Rights Reserved. 6

Internet of Things(Io. T) World Ø Any Thing Ø Any Device Ø Any One Ø Any Service Ø Any Business Ø Any Path Ø Any Network Ø Any Place Ø Any Where Ø Any Time Ø Any Context Source : Setting the Stage for the Internet of Things (2014. 3) / IEEE Standards Association (http: //theinstitute. ieee. org/benefits/standards/setting-the-stage-for-the-internet-of-things) … © 2016 Data. Streams Corp. All Rights Reserved. 7

스마트 시대에서 강자? 산업혁명시기에는 증기가 세상을 바꾸는 동력이었고, 기술의 시기에는 IT가 삶을 바꿨고, 스마트 시대에는 데이터를 가진 자가 강자 Industrialization Steam, Coal Electronic Age Semi. Conductors, Computers Information Age Smart Age © 2016 Data. Streams Corp. All Rights Reserved. IT, SW, Network Big Data 8 ? 8

Personal Technology Market “By 2018, consumer spending on personal technology will reach $3. 1 trillion. ” (Werner Goertz, Gartner) § e-Test § PC Software § Landline Voice Services Trillions 3 § Fixed Internet Access Services § Mobile Data Services § Entertainment Devices / Contents 2 § Wearable Devices § Mobile Phones / Apps / Services 1 § Connected Home Services § Pay TV Services § Computing Devices / Printers 2014 2018 § Health Aid Devices © 2016 Data. Streams Corp. All Rights Reserved. 22

Customer Behavior Trends 소비자는 온라인, 오프라인, 모바일 등 다양한 경로를 넘나들며 상품을 검색하고 구매 Source: Holiday Shopping Intensions Study, Google / Ipsos OTX, 2012 Today’s Customers reach Retail services Anytime & Anywhere and in Fact they are Omnipresent across all possible customer touchpoints. © 2016 Data. Streams Corp. All Rights Reserved. Source: Forbes/CMO Network Dec 4 , 2012 23

Real time Data analysis Needs 실시간 데이터분석의 Needs 새로운 빅데이터 솔루션 IT 담당자 비즈니스 담당자 다양한 수집 비즈니스 데이터 고속저장 장비 로그 센서 정보 실시간 분석 이벤트 Source : 2015 Cap Gemini Study “ The Deciding Factor : Big Data & Decision Making” © 2016 Data. Streams Corp. All Rights Reserved. 24

Omni-Channel 다양한 고객의 접근 채널을 On-Line 과 Off-Line에 상관없이 비즈니스 주체인 고객을 중심으로 일원화 (Consolidation) 하여 가장 효과적인 마켓팅을 할 수 있 도록 한다. 기지국 Web, App Wifi 고객 Multi-Channel © 2016 Data. Streams Corp. All Rights Reserved. Multi-Channel 캠페인 26

Smart Digital Marketing Data Collection Strategy Roll out real-time and future marketing plans and strategy to engage. Customer activity and behavior pattern data collection Io. T & Sensing & Cognition Detecting Customer presence and activity across channels. Decipher behavior using cognitive methods © 2016 Data. Streams Corp. All Rights Reserved. Omni Channel Event Strategy Real-Time Big Data Analytics Strategy Integration & Unified Commerce Intelligence Predict customer intent by deep analytics and generate expected sales figures Planning Offering Unified offer/scheme distribution to all channels in real-time 27

Omni-Channel Marketing Sensing & Integration 1)Customer Data Platform Smart Foot Traffic Beacon Digital Signage Market S. Insight CDR 채널 솔루션 ERP CRM WEB/SNS 고객사별 정보 2)Real-time Intelligence 산업군 특화 Meta © 2016 Data. Streams Corp. All Rights Reserved. Planning & Offering 3)Campaign Builder 마케팅 관리, 실행 채널 정보 수집 Data Asset Syrup Intelligence 채널 데이터 융합 정형, 비정형 분석 Campaign 설계 실시간 이벤트 설계 4)Channel Event Manager SMS/MMS 발송 마케팅 자동화 성과예측 및 결과분석 통합 컨텐츠 디자인 On-line 발송 / 게시 Off-line 알림 28

Big data analysis platform Big Data analysis Platform Store Collect Real-Time Collection (Delta. Stream) Real-Time Packet(Machin e) Data Interface Tera. Stream BASS(SQL on Distributed File system) Visualization Social Data Analysis (Social Cube) Data Store Tera. Stream BASS(In. Memory Processing) Tera Stream for Hadoop (HBASE) Tera Stream for Hadoop (Oozie) Tera. Stream for Hadoop (HIVE) Data. Streams’s Solutions © 2016 Data. Streams Corp. All Rights Reserved. SQL on Distributed File system Meta data Mgmt. (Meta. Stream) R Analytic Engine Tera. Stream for Hadoop (MAP/REDUCE) Tera. Stream Document for No. Sql Hadoop (Mongo) (NOSQL) SQL Tera. Stream for Hadoop (RDBMS) Visualization Analysis Unstructured Data Processing (Social Cube) Structured Data Extraction (FACT) Analytic Store Open Source/3 rd Solutions 30

Real Time Io. T Convergence Analyst’s Screen Connectors Benchmark results Criteria Task: Log data of security firewall (Records 150 bytes each) Data Collection speed Collecting speed from source data to storage server Display speed 1000 record’s display speed from storage server to GUI among 400 Million records Fast N/W !!! Blazing Fast !!! Real-time Searching and Processing Data Warehouse • Backbone Network/Internet • Packet/Flow • LOG/Sensor/Click stream Result 5 million 15 Gbps Search Analysis by Big Data Memory/Storage records per second 0. 1 sec Security Information and Event Management(SIEM) Regular Monitoring System Real-time • • Data Ingestion Data Storage Indexing Search • In-depth Analysis • Operational Analysis • Large Scale Structured Data Management Crime Information Tracking System Fraud / Illegal Transaction Monitoring Real-time Event Marketing Data Lineage Data Quality © 2016 Data. Streams Corp. All Rights Reserved. 31

Unified Big Data Platform Real Time Monitoring • Performance Balancing • Emergency Management Data Platform Data Source s (Structured) Augmented ETL Extract Transform Cleanse Validation Transport Enterprise Data Hub Analytics Framework Analytics Platform Reporting RDBMS Legacy (ERP, CRM) HIVE File file Hadoop / HDFS Big Data* (Web/Social/Io. T) (unstructured / Streaming Data) Data Virtualizatio n HBAS E BI/OLAP Advanced Analytics Visualization In-memory Data Governance Metadata Mgmt © 2016 Data. Streams Corp. All Rights Reserved. Quality Mgmt. Impact Analysis Master Data Mgmt Data Lineage Mgmt 32

One stop Data Governance One Stop Solution for Data Governance Admin & Monitoring Integrate Business Analytics Applications Analyze Insights Collect External Information Sources Master Data Cubes In-Memory Search Data Content Streaming Information Data Warehouses DATA Governance Meta. Data © 2016 Data. Streams Corp. All Rights Reserved. Lineage Data. Quality 34

Consent Management § Protecting privacy is a opposite concept of connecting and sharing. § Consent Management is a key solution to promote personalized realtime IOT digital marketing services ü Informed Consent ü Opt-in ü Opt-out Opt-In Opt-Out © 2016 Data. Streams Corp. All Rights Reserved. 35

감사합니다. www. datastreams. co. kr 본 사 : (주)데이터스트림즈 서울시 서초구 사임당로 28 청호나이스빌딩 6층 T 02 3473 9077 F 02 3473 9084 E sales@datastreams. co. kr 연 구 소 : (주)데이터스트림즈 R&D센터 경기도 성남시 분당구 대왕판교로 670 유스페이스몰 2 B동 601호 T 02 3473 9077 F 02 3473 9084 중국법인 : 100 -102 Pohang center 28 F, Wangjing technology business park, Chaoyang District, Beijing E ysjeong@datastreams. co. kr