2012.05.15 Saltlux, Inc. Tony LEE

1. The 8thKorea Communication Conference 2012 Big Data Analytics in Practice 2012.05.15 Saltlux, Inc.Tony LEE

2. 9 Myths of Big Data Big Data ‘KUMIHO’, nine-tailed fox

3. Myth #1 “Big Data means Big Volume of data set” Truth #1 “Big Data means Big Difficulties to process it” “4V := Volume, Velocity, Variety + Value”

4. Myth #2 “Bigger Insight comes with Bigger Data” Truth #2 “Bigger insight comes with meaningful Data Selection” “No Garbage-in, Gold-out” - At least goldstone-in

5. Myth #3 “Big Data Analytics Means Social Media Analytics” Truth #3 “Social data is one of big data sources” “It is not enough to understand the World”

6. Myth #4 “The goal of Big Data Analytics is Prediction” Truth #4 “Right understanding and optimization Of current status” “The best way to predict the future is to create it”

7. Myth #5 “New technology drives the success of Big Data Analytics” Truth #5 “Clear Goal and Human centric task drive the success” “People PeoplePeople under the Clear Vision"

8. Myth #6 “Hadoop is essentialtech. for big data analytics” Truth #6 “Hadoop is one of good tech. tools for big data anal.” “Do not use a hammer to crack a nut"

9. Myth #7 “Big data tech. will lead Huge IT Market” Truth #7 “A few companies will create values from big data” “It’s not Buzz, but be careful“ - you wouldn’t be.

10. Myth #8 “Big data business is an extension of old BI” Truth #8 “BI is one of applications, using big data” “It’s Not an Old Wine in New Bottle!“

11. Myth #9 “Big data analysis is for decision maker like CEO” Truth #9 “Common people will enjoy the value of big data” “Invisible and Calm Big Data Analytics“

12. Understanding Big Data Analytics

13. What kinds ofdata we have? King of Data : Linking Open Data gov. and pub. open data + data from social media + enterprise and private + closed government data

14. Almost of them areunstructured 90% is unstructured However, we don’t utilize it enough Unified analysis combining structured and unstructured data will be key success factor Enterprise Strategy Group, 2010

15. Stream DataWorld, we never seen sensor networks, social networks, web of data, IOT, M2M ...

16. Big dataAnalytics Platform? Big Data Analytics Platform Advanced Analytics Advantages Visualization and Application Real-time Marketing Optimization Social Data Trend Analysis Analysis Workflow System Enterprise Data Competitive Strategy Optimization Voice of Customer Analy. Service Component Classification., Clustering, Trends, Sentiment Financial Data Dynamic Cost Optimization Reputation Analysis Telcom. Data Analytic Tech. Infra NLP, ML, Statistics, Semantic, AI Competitive Analysis New business or Policy Development Security Data Data Collection, indexing and Mngt. Infra Risk Detection/Mngt Risk Management and Optimization Health Data Fraud Detection Parallel/Dist Comp Infra Hadoop, NoSQL(HBase, mongoDB, …) Manufact. Data Production System Optimization Manufac. system anal. Cloud Computing Infra

17. Quality comes from Process • Analysis is sophisticate process : Workflow system is key • Dynamic collaboration between analysts and machine

18. Big data analyticsTechnologies Visual- ization Semantics Statistics (R) Machine Learning In-memory Analytics Text Mining Cloud, NoSQL NLP IR (Search) Crawling

19. Value Creation

20. Is Big data a Future Growth Engine?? Feature of Future Expected Role and Values of Big Data Competition Counterplan Creativity Insight • Understanding real world pattern, forecasting • Understanding big picture under complexity • Understanding social signal and simulation Uncertainty Risk • Anomaly detection from (social) big data • Issue detection and decision supporting • transparency improvement and cost reduction Smart • Competitive analysis from social big data • Context awareness & AI based smart services • Next generation BM based on personalization Conversions • New value creation from heterogeneity • Reducing ‘trial & errors’ by connected patterns • Fusion market creation from conversions source : NIA, 2011.12.30

21. 3 + 1Big Values  take two! Quality Sustain- ability • Personalized Services • VOC, Customer Understan. • Medical, Healthcare • Business & Policy Develop. • Competitive Analysis • City Surveillance • Risk Management • e-Discovery • Security and Defense • Environment Conservation Speed Cost • Financial Fraud Detection • Run-time Marketing Optimization • Real-time Production Optimization

22. Big Data 5-Step Business Models Step 5 Big Data based End-user services (B2C, B2B) Step 4 Big Data & Analyzed Data Service (DaaS) Step 3 Analytic System Integration Step 2 S/W tool Provisioning Step 1 Consulting, Education

23. McKinsey Said the Value of Big Data $300 billion potential annual value to US health care more than double the total annual health care spending in Spain €250 billion potential annual value to Europe’s public sector administration more than GDP of Greece $600 billion potential annual consumer surplus from using personal location data globally 60% potential increase in retailers’ operating margins possible with big data 1.5 million more data-savvy managers needed to take full advantage of big data in the United States

24. IDC also said about Big Data Big Data Phenomenon is REAL. The market for Big Data technology and services will reach almost 17 billion dollars by 2015, up from a mere 3 billion dollars in 2010 This is a 40 % a-year growth-rate, and about seven times the estimated growth rate of the overall ICT market Server 27.3%, Software34.2%, Storage61.4% growth Appliance and Cloud based service will be important (IDC, March 7, 2012)

25. Applications Working on Big Data

26. Applications of Big Data Analytics time Financial, Telecom fraud detection 1s 1m 1h 1d 1w Mobile Service Personalization City Surveillance, Disaster Management Medical, Healthcare Services Social Media Analytics (Trends, Sentiment, Issue detection and etc.) Voice of Customer /Citizen Analysis Defense, Security, eDiscovery Policy, Business Development Tech. / Research DataAnalysis un-structurality structuredsemi-structuredun-structured

27. Applications of Big Data Analytics City Surveillance Public Data Disease Protection Defense, Security Crime Protection Gov. Policy Optimization Welfare Optimization Medical Service Opt. Fraud Detection VoC Analysis Service Personalization Risk Management Social Issue Detection e-Discovery Marketing Optimization Biz Strategy Optimization Reputation Analysis Social Data Enterprise Data

28. TelecomBig Data: Personalization

29. TelecomBig Data: Hybrid Reasoning

30. CompanyBig Data: Search + Analytics 30

31. CompanyBig Data: e-Discovery & Compliance

32. CustomerBig Data: VOC Analysis

33. TechnicalBig Data: Trend Sensing

34. ResearchBig Data: experts recommend.

35. SocialBig Data: Trends, Reputation TrueStory.co.kr

36. Leakage Detection Discover Leakage Area Sensor Monitoring Infer Leakage Pipe Link Automatic Alert Recom. Detour Path Smart CityBig Data: Pipeline mgmt. 36

37. Smart CityBig Data: Traffic Optimization Traffic prediction for Milano based on traffic sensor data Hybrid (ontology and ML) reasoning for stream data set Milano City Sensor Map • Traffic data from Milano (Italy) • Data ranging from Mar. 07 to July 09 • 5 min. sampling rate for flow & speed • Traffic flow & speed from • 209 sensors that are able to classify vehicles, and • 757 non classifying sensors • Weather data provided fromhttp://www.ilmeteo.it • 1 hour sampling rate for weather data Sensors – Crossroads – Street Categories (multi-colored)

38. - CLOSING - The best way to predict the future is to create it. Let’s manage in a times of Grate Changes!