Casos de Uso de Big Data

Transcripción

1 Casos de Uso de Big Data 13 Febrero IBM Corporation

2 Big Data y Watson 2013 IBM Corporation

3 Los datos son el nuevo recurso natural Social. Móvil. Big Data Juntas están enriqueciendo a la sociedad con conocimiento, a través de redes que generan expectativas de valor a cambio de su información 3

4 Big Data ofrece nuevos casos de negocio estratégicos Hexa Peta Hasta x más volumen Mexcla (no)estructurados Volumen Data Scale Tera Giga Datos en reposo instrumentado interconectado Analítica Compleja 4 Mega Kilo Data Warehouse traditional e Inteligencia de Negocio Hasta x más rápido año mes sem día hora min seg ms ocasionalmente frecuentemente tiempo real Velocidad Datos en movimiento Tiempo esperado de decisión inteligente

5 The World of Big Data & Analytics Is Rapidly Expanding Untapped Resource 5 Empower Everyone Increased Value

6 We have entered a new era of computing enabling new opportunities and outcomes 6

7 Announcing The Watson family IBM Watson Solutions Bespoke solutions designed to meet some of industries most demanding needs leveraging cognitive capabilities IBM Watson Advisors Products based on Watson s core attributes and capabilities IBM Watson Cognitive Fabric APIs, tools, methodologies, SDKs, and infrastructure that fuels Watson IBM Watson family IBM Watson Ecosystems The Watson Developer Cloud, Watson Content Store and Watson Talent Hub driving innovation from partners IBM Watson Foundations Provides the big data and analytics capabilities that fuel Watson 7

8 Watson combines transformational capabilities to deliver a new world experience 1 2 Understands natural language and human style communication Generates and evaluates evidencebased hypothesis Watson: Understands me Engages me Learns and improves over time Helps me discover Establishes trust 3 Adapts and learns from training, interaction, and outcomes Has endless capacity for insight Operates in a timely fashion 8

9 Introducing the IBM Watson family 9 Cross-industry Watson Engagement Advisor Watson Discovery Advisor Watson Analytics (NEO) Watson Explorer Industry-specific Watson Health Cloud Watson Travel Cloud Watson Retail Cloud Etc. Ecosystem Watson Products and Services Business solutions and services Watson Developer Cloud Watson Content Store Watson Talent Hub Watson Foundations New era big data and analytics capabilities IBM Decision Management IBM Content Analytics IBM Planning & Forecasting IBM Discovery & Exploration IBM BI & Predictive Analytics IBM Content Management IBM Hadoop System IBM Stream Computing IBM Data Management & Data Warehouse IBM Information Integration & Governance Watson-enabled Solutions (IBM & Client) IBM and client solutions that incorporate Watson services Powered by: Ex. Interactive Care Guide & Reviewer Powered by Watson (Wellpoint) API branding: A modern approach to ingredient branding design the service to be distinctive to IBM. Systems & Storage Systems & storage optimized for big data & analytics

10 A new foundation for leveraging all analytics and harnessing all data All Data IBM Watson Foundations New/Enhanced Applications Information ingestion and operational information zone Real-time analytics zone Exploration, landing and archive zone Information governance zone Enterprise warehouse data mart and analytic appliances zone What action should I take? Decision management What is happening? Discovery and exploration Cognitive Fabric What could happen? Predictive analytics and modeling Why did it happen? Reporting, analysis, content analytics Systems Security Storage On premise, Cloud, As a service 10 IBM Big Data & Analytics Infrastructure

11 Watson Foundations the cornerstone of our IBM Big Data & Analytics Portfolio CONSULTING AND IMPLEMENTATION SERVICES SOLUTIONS Sales Marketing Finance Risk Operations Fraud IT HR IBM Watson and Industry Solutions WATSON FOUNDATIONS Decision Content Planning & Discovery & Management Analytics Forecasting Exploration Business Intelligence & Predictive Analytics Data Mgmt & Hadoop Stream Content Warehouse System Computing Management Information Integration & Governance Unique fuels journey to Cognitive Innovative easy to consume Complete enterprise-ready Fast start anywhere and grow BIG DATA & ANALYTICS INFRASTRUCTURE 11

12 Watson Foundations uniquely Helps me discover fresh insights Predictive and content analytics to uncover patterns not yet known Interactive exploration across all data WATSON FOUNDATIONS Decision Management Content Analytics Planning & Forecasting Discovery & Exploration Business Intelligence & Predictive Analytics Data Mgmt & Warehouse Hadoop System Stream Computing Content Management Information Integration & Governance Operates in a timely fashion Real-time analytics as data flows through an organization Enterprise-class Hadoop that runs 4x faster In-memory computing for speed of thought analytics 12 Establishes trust so I can act with confidence Governance across complete data lifecycle including Hadoop Security and privacy with compliance Transparency and context to decision-making process

13 Arquitectura de Nueva Generación 2013 IBM Corporation

14 Nueva Generación de Arquitectura y Capacidades Fuentes de Datos Analítica en Tiempo Real Area de Entendimiento Aplicaciones Mejoradas Datos de máquinas y sensores Cognitive Customer experience Imagen y video Contenido Empresarial Datos de aplicaciones y transformaciones Informacion Operaciona + + Enterprise warehouse Data Data mart mart Decision management Predictive analytics and modeling Nuevos Modelos de Negocio Mejoras económico financieras Gestión del Riesgo Datos de Redes Sociales Datos de Terceros Exploracion, landing y archivo Analytic appliances Analytic appliances Reporting, analysis, content analytics Gestión de Operaciones y del Fraude Economía IT Gobierno de la Información Discovery and exploration SYSTEMS SECURITY STORAGE 14

15 Oferta Big Data & Analytics Fuentes de Datos INFOSPHERE STREAMS Real-time analytics Actionable insight Enhanced applications Datos de máquina y sensores Imagen y video Contenido Empresarial Datos de aplicaciones y transformaciones Datos de Redes Sociales Datos de Terceros DB2, INFORMIX Information ingestion and operational information INFOSPHERE BIGINSIGHTS Exploration, landing and archive PUREDATA OPERATIONAL ANALYTICS Enterprise warehouse DB2 BLU PUREDATA ANALYTICS Data mart PUREDATA ANALYTICS Analytic appliances WATSON Cognitive SPSS MODELER GOLD Decision management SPSS MODELER Predictive analytics and modeling COGNOS BI COGNOS TM1 Reporting, analysis, content analytics WATSON EXPLORER Customer experience Nuevos Modelos de Negocio Mejoras económico financieras Gestión del Riesgo Gestión de Operaciones y del Fraude Economía IT INFORMATION SERVER, MDM, G2, GUARDIUM, OPTIM Information governance Discovery and exploration SYSTEMS SECURITY STORAGE 15

16 Casos de Uso de Big Data 2013 IBM Corporation

17 Los 5 Categorías de Casos de Uso Exploración Encontrar, visualizar, comprender los Big Data para mejorar el proceso de decisión Vista 360º cliente mejorada Extender las vistas de clientes (MDM, CRM, etc) incorporando fuentes de información internas y externas Seguridad/Inteligencia Aumentada Reducir riesgo, detectar fraude y monitorizar seguridad en tiempo real Inteligencia Operacional Analizar una variedad de datos máquina para mejorar la eficiencia operacional Aumento del Data Warehouse Integrar las capacidades de big data y de data warehousing para mejora de resultados de negocio 17

19 Caso de Uso: Exploración y Descubrimiento Enterprise Data Discovery Application User Profiles 360 O View Information Application Big Data Optimization Application User Profiles Application Framework Authentication/Authorization Query transformation Personalization Display Entity Model Text and Metadata processing Thesauri Semantic Clustering Ontology Support Entity Extraction Relevancy Faceting Collaboration Tagging Search Engine Query-Time Connectors Subscriptions Feeds Web Results Content Connectors CM, RM, DM RDBMS Feeds Web 2.0 Web CRM, ERP File Systems 19 19

20 Ejemplos Index Product Catalog, Returns by Product, Availability Sales revenues, Retailers Sales Competitor News, Industry News, Web sites FAQ, MKTG Campaign docs Tweets 20 20

21 Access across many sources Dynamic categorization Expertise location Highly relevant, personalized results Refinements based on structured information Enhanced by social collaboration Leveraging Structured and unstructured content Organize content into virtual folders

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23 Global Investment Banking Firm Need Reduce risk and improve compliance Too many silos to monitor across multiple LOBs needed visibility to all from a single point Improve knowledge sharing and research Deliver 360º view of customers, products and assets Solution/Status Watson Explorer deployed in: POC deployed in Consumer Client Banking Risk and compliance solution Asset management group PoC Call center knowledge man 23 23

25 Una plataforma de BIG DATA es la llave para mejorar diferentes procesos de negocio en base a un mayor conocimiento de clientes El reto de Big data no es cuánta información procesar si no como ser capaces de analizar esa información y llevarla a los procesos de negocio 25 25

26 Qué información de cliente podemos obtener de las redes sociales para construir perfiles 360 Qué información podemos extraer de las redes sociales para empezar a formar la visión 360? Qué sería mas prioritario? 26

27 Cómo lo hacemos? Mediante análisis del lenguaje natural la plataforma de Big Data es capaz de analizar diariamente miles de comentarios volcados en las redes sociales e identificar nuevos atributos con los que enriquecer el perfil del cliente Me gusta la novela de aventura, lo último que he leído ha sido la saga crepúsculo. Te la recomiendo, cuando empiezas a leerla no puedes dejarlo. Un abrazo. Mercedes Mañana comida con cliente. Alguien me recomienda un buen sitio para tomar sushi?. Mercedes Qué suerte Ana!! a mi también me encantaría poder hacer un crucero algún día. Mercedes 27 27

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29 Major Italian Bank leverages unstructured client data to improve customer retention Need Drive customer retention activities based on behaviors instead of only transactions Link to the case study /common/ssi/ecm/en/imc 14573usen/IMC14573U SEN.PDF Leverage branch teller notes, call center notes and client s to identify changing client behaviors Track social media sentiment analysis to measure impact of targeted campaigns Targeted Benefits Reduce attrition from 6% to 3% Optimize offers and cross sell to increase average products per customer from 1.4 to 2.2 Improve client advocacy (NPS) 29 29

30 Large Asian credit card company uses Big Data & Analytics to speed customer offers & treatments based on customer behavior Need Required a capability to analyze massive amounts of customer data to more quickly target and deliver rewards programs and offers to more granular customer segments Benefits Significant improvement in offer response rate Increased net margin 13% Allows faster processing of massive amounts of customer data including transactions, contact history, and customer behavior faster than ever before Able to launch more timely marketing activities and change ongoing campaigns on the fly, as a result of real-time campaign analysis IBM Corporation

31 Major Dutch Bank analyzes rapidly client behavior to optimize offers and treatments Need: Rapidly analyze client behaviors to offer personalized marketing offers across all channels and significantly improve offer acceptance rate Solution: IBM PureData for Analytics IBM Unica Benefits: A 98 percent increase in key client interaction performance metrics such as click stream data, analytical reporting and queries. The marketing division is currently exploring adding additional applications on the existing platform given the success of the solution 31 31

32 Casos de Uso en Banca de 360 extendido Contact Center optimization Optimización del Contact Center Interactive contextual campaigns Campañas interactivas contextuales Online channel monitor Monitor de canales on-line en tiempo real Affinity-based assignment of Agents to Customers. Relevant event detection by analyzing conversations with clients. Decision-taking based on this information. Interactive Marketing improvement, based on the usage of information obtained via Big Data: geolocalization, contextual client information, multichannel behavior analysis. Near real time dashboards that show the Internet channel activity, with different aggregation levels and details, from the number of customers connected at any time, down to the transaction that a specific client is executing. Gestor Client complaints de quejas de clientes manager Advanced Spending Manager Spending Manager avanzado 360º client profile with social media analytics Perfil 360º de Clientes con analítica de Social Media 32 Automatic client complaint classification based on unstructured text analytics. Classification of client expenses through the analysis of nonstructured text involved in transactions. Automatic advice engines for clients and merchants. Usage of Advanced analytics on Social Media and development of automatic client matching on those media to complement the profile available at the Bank.

33 Caso de Uso: GeoMarketing 2.0 / Geofencing Pisadas Patrones de movimiento de personas y patrones de estancia Análisis por hora y día Inferencia para métricas de estancias dentro de edificios Sociodemográficos y Conducta Perfil sociodemográfico combinado con Movimiento, como edad, género, país, dirección vivienda, dirección trabajo, tipo dispositivo (datos, status), patrón de actividad Tránsito Inferencia de rutas de transporte público incluyendo carreteras, trenes, autobuses 33

34 Ufone reduced churn and kept subscribers happy, helping ensure that campaigns are highly effective and timely Need To ensure that its marketing campaigns targeted the right customers, before they left the network To keep its higher usage customers happy with campaigns offering services and plans that were right for them Benefits Predictive analytics is expected to improve the campaign response rate from about 25% to at least 50% CDRs can be analyzed within 30 seconds, instead of requiring at least a day Expected to reduce churn by approximately 15-20% IBM Corporation

35 Sprint improves business insight with IBM Big Data Platform Need A solution to perform efficient, near-real-time analytics in order to help drive more meaningful decision making. Sprint wanted to gain strategic insight into the needs of its customers. Benefits Increased transaction capacity by 90 percent Enabled informed, near-real-time strategic decision making Created cost savings through consolidation of legacy solutions Home 2013 IBM Corporation

36 The Next Generation Audience Insights Platform enable use cases which realize new capabilities and business value Use Case 1: Multi-Platform Viewing Quickly measure a TV show's performance across linear and digital platforms Use Case 2: Audience Composition and Indexing Identify specific audience segments or audience attributes across different TV shows Use Case 3: Audience Engagement and Targeting Identify existing fans, target new prospects, analyze engagement across TV shows Use Case 4: Social Sentiment Analysis Analyze social conversations to understand audience sentiment across TV shows Use Case 5: Social Media Indexing and Visualization Explore (browse, search) extracted social media postings about TV shows Overall Architecture 36

37 Use Case 1: Multi-Platform Viewing Quickly measure a TV show's performance across linear and digital platforms Viewership TV Show 37 TV Show XYZ Week # (Episode Aired) Week 6 Viewership ILLUSTRATIVE % of Viewership Channel % of Digital/Mobile of Total Channel Capabilities Integration of disparate linear and non-linear data sources Discovery and analysis of multi-platform audience viewing patterns Benefits Common view of show performance for different business functions Ability to identify impact of digital trends across network portfolio of shows Viewership is defined as Nielsen s Total Reach (How many people watch at least 1 minute of the program)

38 Use Case 1: Multi-Platform Viewing Quickly measure a TV show's performance across linear and digital platforms Linear Viewing Data Data Query: Platform Shows Audience Time Horizon Rules Engine Data Visualization Non-Linear Viewing Data Big Data Advanced Analytics Warehouse Multiplatform View Numbers Structured 1 Merge linear and UI Portal nonlinear viewing data across different platforms into a single coherent view Display Widgets 38 Ad Sales / Research

39 Use Case 2: Audience Composition and Indexing Identify specific audience segments or audience attributes across different TV shows Male Viewership by Gender (TV Shows ABC, JKL, XYZ) Viewership by Age (TV Shows ABC, JKL, XYZ) Top Category Spend (ABC, JKL, XYZ) ILLUSTRATIVE Female Top TV Shows for Restaurant Category Spend Capabilities Data integration of digital and 3rd party spend/affinity data Discovery and analysis of audience attributes across different views TV shows Benefits Ability to identify desired audience attributes for advertisers Identify trends or measure impact of changes that drive desired audience composition 39

40 Use Case 2: Audience Composition and Indexing Identify specific audience segments or audience attributes across different TV shows 40 Linear Viewing Data Non-Linear Viewing Data Demographic Data Affinity & Spend Data Social Media Data 1 Extract viewer affinity and demographics from social data Text Analytics Big Data Advanced Analytics Warehouse Segment Profiles Structured Unstructured Streams Processing 2 Data Query: Platform Shows Audience Time Horizon Demographics Income Affinity Create viewer micro-segments based on affinity, income, & demographics from all data sources Rules Engine Data Visualization UI Portal Display Widgets Ad Sales / Research

41 Use Case 3: Fan Engagement and Targeting Identify existing fans, target new prospects, and analyze overall engagement across TV shows Personal Attributes Sophia Campbell 32 years old, birthday on 7/17 Lives near Raleigh, NC College grad; Income of k In a Relationship Activity Summary Retweets BF s comments Interest in BBC shows: Downton Abbey, Sherlock, Fringe, (P&P?) Watches Modern Family, Vampire Diaries, Sherlock Hunger Games, Katniss/J. Lawrence TV Show Engagement Scores (1-10) ILLUSTRATIVE Social Media Presence sophiex0x0@gmail.com Twitter: Soph <3 Facebook: Blog: iamsophie.blogspot.com LinkedIn: Interests/Behavior Watch movies, tv shows Romance plots, hero types, strong women Uses ipad 3, Redbox, Hulu Shopping, interest in sales/deals Duke/ UNC basktball Recommended TV Shows (% Affinity) Capabilities "Entity" integration (audience member) from multiple social, CRM, and other 3rd party sources (Acxiom, etc) Correlation and propensity modeling to build a target audience prospect list Benefits Measure and compare engagement levels of audiences across TV shows Build an actionable list of new prospects to improve marketing efforts 41

42 Use Case 3: Fan Engagement and Targeting Identify existing fans, target new prospects, and analyze overall engagement across TV shows Fan Engagement Level for TV Show XYZ Fan Engagement Level Comparison Capabilities "Entity" integration (audience member) from multiple social, CRM, and other 3rd party sources (Acxiom, etc) Correlation and propensity modeling to build a target audience prospect list Fan Engagement Level for TV Show XYZ Benefits Measure and compare engagement levels of audiences across TV shows Build an actionable list of new prospects to improve marketing efforts 42

43 Use Case 3: Fan Engagement and Targeting Identify existing fans, target new prospects, and analyze overall engagement across TV shows 43 Linear Viewing Data Non-Linear Viewing Data Subscription Data Affinity & Spend Data Demographic Data Social Media Data 1 Influence Extract entity profile info and influence from social + non-social data & perform Entity Integration to create individual profiles Text Analytics Fan Prospects List Big Data Advanced Analytics Warehouse 360 o Individual Profile Structured Unstructured Streams Processing 3 Data Query: Shows Fan type Engagement Churn 2 Create prospects list based on predictive models & produce targeted marketing campaigns Data Visualization Propensity Predictive Analytics Rules Engine Fan Type Engagement Churn Develop models to predict buying propensity, influence, fan engagement, fan type & churn rate Multi-Channel Marketing Campaign Management UI Portal Display Widgets Marketing/ Research

44 Use Case 4: Social Sentiment Analysis Analyze social conversations to understand audience sentiment across different TV shows Positive Tweets Theme: Comedy 44 Social Buzz Trending Topics over Time Social Sentiment ( /+) over Time Capabilities Extraction of Buzz, sentiment, and intent to watch from social media text and other social data fields Development of microsegment profiles that include audience product affinity attributes "It's rare that I laugh out loud when I'm watching a TV show but this show has the zaniest of antics that I can't help but laugh out loud." Theme: Attraction to cast Most Representative Tweets Negative Tweets Theme: Disagreement with storyline ILLUSTRATIVE "When I see Greys Anatomy, when Mcdreamy and McSteamy argue I'm going to stand and shout it's okay boys you can both marry me "The way this is going, Glee is gonna crash and burn because everyone s dying or dating each other and I hate that" Theme: Casting decisions Killing off Sybil has ruined Downton for me, I can't watch anymore I never really got on the Charlie train. As long as Adam doesn't leave I'll still watch Girls Benefits Measure social ratings and performance across a set of TV shows Understand trending topics that are being discussed about TV Shows Analyze differences between difference social audience micro-segments

45 Use Case 4: Social Sentiment Analysis Analyze social conversations to understand audience sentiment across different TV shows Linear Viewing Data Non-Linear Viewing Data Subscription Data Affinity & Spend Data Demographic Data 1 Extract Sentiment & Intent to View from social data Big Data Advanced Analytics Warehouse Structured Unstructured Sentiment Intent 2 Emerging Topics Data Query: Platform Shows Intent Sentiment Affinity Derive emerging sentiment topics of interest by analyzing social media trends Data Visualization Rules Engine UI Portal Display Widgets Product Affinity Social Media Data Text Analytics Streams Processing 3 Descriptive Analytics Relate user social data attributes to product affinity Ad Sales / Research 45

46 Use Case 5: Social Media Indexing and Visualization Explore (browse, search) extracted social media postings about TV shows Filters Results Social Sentiment - Positive Sentiment - Negative Sentiment - Intent to Watch Trends / Topics - Actors - Ed O Neill - Sofa Vergara - Julie Bowen + Cast + Plot Audience Attributes + Location (NY, FL, CA, etc) + Gender + Occupation + Income Lifestyle Affinity +Cars +Travel +Technology Date Range +Episode Aired Week +Custom ILLUSTRATIVE Capabilities Semantics based indexing of social media (unstructured data) Faceted search/browse of social data Visualization of topic clusters (e.g. phrase clouds, etc) Benefits Discover trending topics driving positive or negative sentiment about a particular TV Quickly drill down on topics and/or audience facets to find specific subset of social conversations to review 46

47 Use Case 5: Social Media Indexing and Visualization Explore (browse, search) extracted social media postings about TV shows Discovery Social Media content Rules Engine + Text Analytics Big Data Advanced Analytics Warehouse Structured Unstructured Extracted Social Media 1 Data Visualization Data exploration tool that creates a single repository for multiple BigData sources, where unstructured and structured data is accessible in a single simple web interface. This enables ease of searching and browsing through social media content. UI Portal Display Widgets Text Analytics Social Media Data Streams Processing 47 Ad Sales / Research

49 IBM Client Examples of Solutions in production Client Problem Benefit Sanctions Screening Credit Risk Scoring Reduced watch list checks from 8-12 hours to less than 15 minutes, increased names checked from 2,500 to more than 40,000, reduced false positives by 75%, realized ROI in 3 months Reduced bad debt by 15+% and credit score every customer daily AML Transaction Monitoring Improved efficiency by 60% reducing administrative costs, reduced alerts by 90%, increased accuracy by 60% Online Fraud Detection Lowered fraudulent transfers by 50% 70% false positive reduction by eliminating unnecessary customer validation calls Link Analysis The system helped prevent more than 1,000 customers from losing funds to fraud in the first 50 days of its use. reduced fraud by 30 percent during that same period while improving AML compliance requirements. 49

50 Case management at Canadian Bank uses big data to prevent and contain mortgage fraud in its banking book Need Fraud investigation suffers from inefficient and slow case processing. Analysts work with unstructured data from disparate mortgage data sources, manually entered into the case management system. Very large data volumes make the investigation very complex and time consuming. Benefits Faster decision making, reduced mortgage provisions, and lower investigations costs IBM Corporation

51 BPM and Identity Insight at Insurance Bureau of Canada (IBC) outsmarting fraudsters with fraud analytics Need Link to the Case Study ommon/ssi/ecm/en/imc147 75usen/IMC14775USEN.P DF Automate processes to detect potential claim fraud and identify possible fraud rings. Fraud is said to account for 10 to 15 percent of Insurance losses to drive up claims costs Benefits Automates identity insight solution replaced the manual ad hoc approach to detect and investigate potential fraud Analytics increases opportunities to identify previously unidentified fraud rings Increases confidence in gathering information against suspected fraudsters Home

52 MoneyGram International detects and prevents transfer fund fraud before it impacts customers Need With 230,000 locations in 190 countries MoneyGram International faced pressure to keep up with fraud and compliance regulations affecting the international business landscape Benefits Increased ability to identify and interrupt potentially fraudulent transactions by 40 percent Prevented thousands of customers from losing funds to fraud and realized a 72 percent reduction in consumer fraud complaints in one year Stopped more than US $37.7 million in fraud Home

53 Global credit card provider high volume Internet card fraud French Credit Card Company foils Fraudsters Need French financial cyber crime police alerted to test payment authorizations for 25,548 stolen credit card numbers over a 72-hour period with 13,274 authorizations granted. Fraud began several days later banks alerted when victims disputed the charges IP addresses, domains and bank information were loaded into the IBM centralized analysis solution years of legacy data involving 25,000,000 records Benefits Organized criminality was established with one site in the ring responsible for almost $300,000 in fraud High volume / small amounts of transactions were low risk but high yield One principal group of 1,233 members was identified and 27 sub-groups which were then geospatially mapped Leveraged against other data, individuals were connected with the transactions and arrests made 53 53

55 Big Data Accelerator Types Analytic Accelerators Address specific data types or operations (e.g. Text Analytics, Geo Spatial, etc.) Application Accelerators Address specific applications (example: Log Analysis) Address specific industry functions (Example: Cyber Security, Health care, etc) Address use cases or specific business processes (Customer insights from Social Media) Can be industry-specific or crossindustry. 55

56 Machine Data Analytics is a Business Imperative Cost of system down-time 49 percent of Fortune 500 companies experience more than 80 hours of system down time annually 1 Cost of down-time varies from $90,000/hour in the media sector to $6.48 million / hour for large online brokerages 80 hours * $6.48M = approx $500M per year System downtown costs North American businesses $26.5 billion a year in lost revenue 2 When systems go down Sales and other processes stop Work in progress may be destroyed Failure to meet SLA s and contractual obligations can result in damages, fees, adverse publicity and damage to reputation Customers are lost to competitors, some permanently Productivity suffers and remediation costs additional $$$ s Chart from

57 Machine Data Analytics Vision App. Server Logs System Logs Smart Meters Sensor Data Utility Systems Data Ingestion Data at Rest Data in Motion Out of Box Extractors Processing Real-time Text Analytics Adaptive Algorithms Insights Root Cause Pattern Analysis Anomaly Detection Outcomes Gain deep insights into operations, customer experience, transactions and behavior Proactive planning to increase operational efficiency Facility Systems Message Queues Clickstream Data Configuration Files Web Access Logs Structured Unstructured Unstructured Indexing Searching Forwarding Correlation Predictive Analysis Dynamic Queries Visualization Troubleshoot problems and investigate security incidents Monitor end-to-end infrastructure to avoid service degradation or outages Database Audit Logs and Tables Web Proxy Logs HDFS Logs Extendable Solution 57 57

58 Casos de Uso en Utilities Gestión Comercial más informada e inteligente Smart metering Central de Operación y Mantenimiento de Molinos Fraude técnico Smart Grid Click en imágenes para ver detalles 58 58

59 Ayudar a reducir los costes de energía y la fiabilidad y rendimiento de la red Necesidad Validar la viabilidad de una técnica de la smart grid llamada control transactivo Smart Grid Beneficios Une a consumidores con activos mediante un sistema de señal-respuesta optimiza el sistema e integrar mejor los recursos renovables Permite analizar y ganar conocimiento de 10Pb en minutos Aumenta la eficiencia y la fiabilidad de la red mediante la auto-monitorización y feedback Se capacita a la ciudad para evitar una potencial caída de tensión 59 59

60 Fabricante de aerogeneradores optimiza inversiones de capital Necesidad Definir modelos climáticos para optimizar la localización de las turbinas, maximizando la generación de energía y la vida residual del equipamiento Beneficios Reduce el tiempo requerido para identificar la localización de la turbina de semanas a horas Reduce los costes de IT y disminuye el consumod e energía en un 40% Operation Analytics Localización de Aerogeneradores Incorpora 2,5Pb de flujos de información estructurada y no estructurada. Se espera que el volumen de datos crezca hasta 6Pb

61 Una gran organización no lucrativa de I+D conduce una proyecto de demostración regional para validar las tecnologías de smart grid Necesidad Reunir y analizar datos del rendimiento de la smart grid de 15 sitios de prueba que representan el terreno, meteorología, y demografía de la región para derivar conocimiento y validar las tecnologías smart grid Beneficios 50% de caídas a corto plazo en horas punta 15% de caidas en picos generales 10% reducción de las facturas de electricidad (recomendaciones de consumo) 70 mil M$ de ahorros proyectados en 20 años Poder analizar 10 Pb en minutos Smart Meters 61

62 How can IBM help create a Proactive Call Center? Customer Knowledge Hub Product Knowledge Hub Sentiment Analytics Segmentation Marketing Campaigns Next Best Action Analytics Subscriber Analysis OTT usage Offer & Product Dev Marketing Optimisation IBM Corporation

64 Data Warehouse Augmentation Use Case Integrate big data and data warehouse capabilities to increase operational efficiency Qualifying Questions 64 Are you drowning in very large data sets (TBs to PBs)? Do you use your warehouse environment as a repository for ALL data? Do you have a lot of cold, or inactive data in your database? Are you having to throw data away because you re unable to store or process it? Are you interested in using your data for traditional and new types of analytics? Extend warehouse infrastructure Optimize storage, maintenance and licensing costs by migrating rarely used data to Hadoop Query-able access to data Governance and Policy-driven archiving 2013 IBM Corporation

65 Archive data into BigInsights using Optim Manage data growth, Lower TCO & Meet data retention compliance Archive Cold Data IMS VSAM More Database Archive files InfoSphere Optim Apply Retention // Hold Policies Capture complete business object Preserve Data Integrity Preserve Schema Metadata Load data into Hadoop as needed InfoSphere BigInsights Archive & Purge Data Compressed, immutable, auditable & restorable archives Query-able data store using Hive in Hadoop IBM Corporation

66 Archive Data Into Hadoop With Optim Database Complete object Optim Big Data Integration Roadmap: : Optim Hadoop Loader to convert Optim archive file into CSV & load into HDFS (next release): Hive External table, via DDL creation in HCatalog. Future: HBase support (planned) CSV Files Hadoop Cluster Archive files Hive warehouse Hcatalog metadata Optim Archive Optim Hadoop Loader HBase database BigSQL.. query processing Application IBM INTERNAL 2013 IBM Corporation

67 Archiving cold data in data warehouses Reduce Costs Improve Performance Minimize Risk Reduce TCO by leveraging an archiving strategy. Reclaim space & defer expensive hardware upgrades. Increase data warehouse performance by archiving out dormant data. Support data retention Archive or legal hold requirement. Archive Capture the precise sets of data required based on your audit or e-discovery IBM Corporation

68 Casos de uso por Industria Banca Optimización de ofertas Servicio a clientes y eficiencia Detección e investigación de fraude Riesgo de crédito y de contrapartida Seguros Vista 360 del sujeto Modelización catástrofes Fraude y Abuso Analítica de rendimiento producto Telecom. Call Center proactivo Analítica de Red Servicios basados en localización Energía y Utilities Analítica de Smart Meter Predicción/ planificación de carga Mantenimiento basado en condición Ofertas a clientes (TOU) Medios y Entretenimiento Transformación de procesos de negocio Optimización de audiencias y marketing Campañas multicanal Optimización de comercio digital Retail Analítica de Cliente y Fidelización Optimización de Mercancías Precios óptimos Transporte y Turismo Analítica de Cliente y Fidelización Analítica de Mantenimiento Predictivo Optimización de Capacidad y Precios Gran Consumo Disponibilidad Estante Optimización de Trade Funds Cumplimiento de mercancías Alertas/Excepciones de promociones Gobierno Servicios públicos Defensa e Inteligencia Servicios fiscales y tributarios Salud Medir y actuar sobre los resultados de salud Involucrar a los consumidores en su salud Automoción Monitorización avanzada de condición Optimización Data Warehouse Analítica de Cliente y Fidelización Química y Petróleo Vigilancia, Análisis, Optimización Operacional Optimización Data Warehouse Investigación Upstream Aviación y Defensa Plataforma de Acceso Información Uniforme Optimización Data Warehouse Plataforma de Certificación Aerolínea Monitorización avanzada de condición Electrónica de Consumo Analítica de clientes/ canal Monitorización avanzada de condición Farmacia Aumentar la visibilidad de la seguridad y efectividad de los medicamentos 68

69 Siguientes Pasos? 2013 IBM Corporation