Ibm big data-platform
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The document discusses IBM's Big Data and analytics solutions, including Watson Explorer which provides a single interface to access both structured and unstructured data. It also outlines several common use cases for big data such as customer analytics, security intelligence, and operations analysis. The final section provides contact information for an IBM sales manager to discuss these big data solutions.
![© 2014 IBM Corporation5
We have for the first time an economy based on a key resource
[Information] that is not only renewable, but self-generating.
Running out of it is not a problem, but drowning in it is.
– John Naisbitt
Source, Megatrends, Naisbitt, John, Grand Central Publishing 1988
We are not suffering from Information Overload. We
are suffering from Filter Failure.
– Clay Shirky
Sourcehttp://www.ted.com/talks/view/lang/en//id/575](https://image.slidesharecdn.com/ibm-bigdata-platform-140513093600-phpapp02/85/Ibm-big-data-platform-4-320.jpg)
Ibm big data-platform
- 1. © 2014 IBM Corporation Big Data Platform Arild Kristensen Nordic Sales Manager, Big Data Analytics Tlf.: +47 90532591 Email: [email protected]
- 2. © 2014 IBM Corporation3
- 3. © 2014 IBM Corporation4 Welcome to the Big Data Opportunity “The list of life's certainties has gotten longer. Along with death and taxes we can now include information overload.”
- 4. © 2014 IBM Corporation5 We have for the first time an economy based on a key resource [Information] that is not only renewable, but self-generating. Running out of it is not a problem, but drowning in it is. – John Naisbitt Source, Megatrends, Naisbitt, John, Grand Central Publishing 1988 We are not suffering from Information Overload. We are suffering from Filter Failure. – Clay Shirky Sourcehttp://www.ted.com/talks/view/lang/en//id/575
- 5. © 2014 IBM Corporation6 Welcome to the Big Data Opportunity Research firm IDC expects Big Data to grow from $3.2 billion in 2010 to $16.9 billion in 2015 by 2015 we'll see 4.4 million jobs devoted to the global support of Big Data each IT job created by Big Data will generate three more positions outside of IT.
- 6. © 2014 IBM Corporation11 Big Data Analytics And Natural Language Cognitive: The Next Wave of Disruptive Technology
- 7. © 2014 IBM Corporation14 Understands natural language and human style communication Adapts and learns from training, interaction, and outcomes Generates and evaluates evidence- based hypothesis 1 2 3 • Understands me • Engages me • Learns and improves over time • Helps me discover • Establishes trust • Has endless capacity for insight • Operates in a timely fashion Watson combines transformational capabilities to deliver a new world experience using cognitive computing Watson:
- 8. © 2014 IBM Corporation15 IBM Watson family IBM Watson Solutions IBM Watson Transformation IBM Watson Foundations IBM Watson Innovations Provides the big data and analytics capabilities that fuel Watson Products based on Watson’s core attributes and capabilities APIs, tools, methodologies, SDKs, and infrastructure that fuels Watson Bespoke solutions designed to meet some of industries most demanding needs leveraging cognitive capabilities IBM Watson Ecosystems The Watson Developer Cloud, Watson Content Store and Watson Talent Hub driving innovation from partners Introducing the IBM Watson family
- 9. © 2014 IBM Corporation16 How is Big Data transforming the way organizations analyze information and generate actionable insights?
- 10. © 2014 IBM Corporation17 Paradigm shifts enabled by big data Leverage more of the data being captured TRADITIONAL APPROACH BIG DATA APPROACH Analyze small subsets of information Analyze all information Analyzed information All available information All available information analyzed
- 11. © 2014 IBM Corporation18 Paradigm shifts enabled by big data Reduce effort required to leverage data TRADITIONAL APPROACH BIG DATA APPROACH Carefully cleanse information before any analysis Analyze information as is, cleanse as needed Small amount of carefully organized information Large amount of messy information
- 12. © 2014 IBM Corporation19 Paradigm shifts enabled by big data Data leads the way—and sometimes correlations are good enough TRADITIONAL APPROACH BIG DATA APPROACH Start with hypothesis and test against selected data Explore all data and identify correlations Hypothesis Question DataAnswer Data Exploration CorrelationInsight
- 13. © 2014 IBM Corporation20 Paradigm shifts enabled by big data Leverage data as it is captured TRADITIONAL APPROACH BIG DATA APPROACH Analyze data after it’s been processed and landed in a warehouse or mart Analyze data in motion as it’s generated, in real-time Repository InsightAnalysisData Data Insight Analysis
- 14. © 2014 IBM Corporation21 Hadoop & Streaming Data New Sources Unstructured Exploratory Iterative Structured Repeatable Linear Data Warehouse Traditional Sources Traditional Approach Structured, analytical, logical New Approach Creative, holistic thought, intuition Enterprise Integration Customer Data Transaction Data 3rd Party Data Core System Data Web Logs, URLs Social Data Text Data: emails, chats Log data Analytics is expanding from enterprise data to big data, creating new opportunities for competitive advantage Contact Center notes Geolocation data
- 15. © 2014 IBM Corporation22 Addressing Client Challenges through Big Data Platform
- 16. © 2014 IBM Corporation23 A New Architectural Approach is Required Information Integration & Governance Systems Security On premise, Cloud, As a service Storage New/Enhanced Applications All Data What action should I take? Decision management Landing, Exploration and Archive data zone EDW and data mart zone Operational data zone Real-time Data Processing & Analytics What is happening? Discovery and exploration Why did it happen? Reporting and analysis What could happen? Predictive analytics and modeling Deep Analytics data zone What did I learn, what’s best? Cognitive
- 17. © 2014 IBM Corporation24 Information Integration & Governance Actionable insight Exploration, landing and archive Trusted data Reporting & interactive analysis Deep analytics & modeling Data types Real-time processing & analytics Transaction and application data Machine and sensor data Enterprise content Social data Image and video Third-party data Decision management Predictive analytics and modeling Reporting, analysis, content analytics Discovery and exploration Operational systems Information Integration Data Matching & MDM Security & Privacy Lifecycle Management Metadata & Lineage IBM Big Data Analytics (Watson Foundations) - One architecture that fits together BigInsights Streams PureData for Analytics DB2 Blu Watson Explorer Cognos Cognos SPSSPureData for Analytics PureData Operational Analytics
- 18. © 2014 IBM Corporation25 InfoSphere DataStage Automatically push transformational processing close to where the data resides, both SQL for DBMS and MapReduce for Hadoop, leveraging the same simple data flow design process and coordinate workflow across all platforms “Big Data Expert”
- 19. © 2014 IBM Corporation IBM InfoSphere Streams: Get real-time insights from data in-motion
- 20. © 2014 IBM Corporation27 27 Current fact finding Analyze data in motion – before it is stored Low latency paradigm, push model Data driven – bring data to the analytics Historical fact finding Find and analyze information stored on disk Batch paradigm, pull model Query-driven: submits queries to static data Traditional Computing Stream Computing Stream Computing Represents a Paradigm Shift Real-time Analytics
- 21. © 2014 IBM Corporation28 28 Modify Filter / Sample Classify Fuse Annotate Big Data in Real Time with InfoSphere Streams Score Windowed Aggregates Analyze
- 22. © 2014 IBM Corporation29 29 Streams Analyzes All Variety of Data Mining in Microseconds (included with Streams) Image & Video (Open Source) Simple & Advanced Text (included with Streams) Text (listen, verb), (radio, noun) Acoustic (IBM Research) (Open Source) Geospatial (Included with Streams) Predictive (Included with Streams) Advanced Mathematical Models (Included with Streams) Statistics (included with Streams) ∑population tt asR ),( Blue = included with the product Red = built for Streams and used in projects but not yet part of the product
- 23. © 2014 IBM Corporation30 30 How is Streams Being Used? Stock market Impact of weather on securities prices Analyze market data at ultra-low latencies Momentum Calculator Fraud prevention Detecting multi-party fraud Real time fraud prevention e-Science Space weather prediction Detection of transient events Synchrotron atomic research Genomic Research Transportation Intelligent traffic management Automotive Telematics Energy & Utilities Transactive control Phasor Monitoring Unit Down hole sensor monitoring Natural Systems Wildfire management Water management Other Manufacturing Text Analysis ERP for Commodities Real-time multimodal surveillance Situational awareness Cyber security detection Law Enforcement, Defense & Cyber Security Health & Life SciencesICU monitoring Epidemic early warning system Remote healthcare monitoring Telephony CDR processing Social analysis Churn prediction Geomapping
- 24. © 2014 IBM Corporation Watson (Data) Explorer IBM Software Group Information Management Big Data
- 25. © 2014 IBM Corporation32 Watson Explorer solves #1 challenge customers face in Big Data: Unlocking the value of information through a single interface Create unified view of ALL information for real-time monitoring Identify areas of information risk & ensure data compliance Analyze customer analytics & data to unlock true customer value Increase productivity & leverage past work increasing speed to market Improve customer service & reduce call times InfoSphere Data Explorer • Analyzes structured & unstructured data—in place • Unique positional indexing • Unlimited scalability • Advanced data asset navigation • Pattern clustering • Virtual documents Contextual intelligence • Text analytics • Secure data integration • Query transformation • Easy-to-deploy big data applications • User-friendly customisable interface Providing unified, real-time access and fusion of big data unlocks greater insight and ROI Zoom in Zoom out 12/05/201432
- 26. © 2014 IBM Corporation33 Watson Explorer Application Architecture User Profiles 360O View Applications Information Discovery Applications Big Data Applications Discovery & navigation applications Web Results FeedsSubscriptions Federated Query Routing Application Framework Authentication/Authorization Query transformation Personalization Display Meta-Data User Profiles Application layer managing user interactions, apps, creating context, routing queries Thesauri Clustering Ontology Support Semantic Processing Entity Extraction Relevancy Text Analytics Search Engine Metadata Extraction Faceting BI Tagging Taxonomy Collaboration Processing layer for indexing, analysis & conversion CM, RM, DM RDBMS Feeds Web 2.0 Email Web CRM, ERP File Systems Connector Framework Framework for accessing data sources 12/05/201433
- 27. © 2014 IBM Corporation34 Highly relevant, secure & personalized results Access all sources or individual source Refinements based on metadata Dynamic categorization Narrow down results set Information Navigation, Discovery & Insight Through One Interface Live link here Setup alert to notify change Identify topical experts Tag results Rate results Comment results Store & share results
- 28. © 2014 IBM Corporation35 Big Data Use cases
- 29. © 2014 IBM Corporation36 Top sources of information used as part of initial big data efforts – typically start with data already being captured Source: The real world use of Big Data, IBM & University of Oxford Big data sources Respondents with active big data efforts were asked which data sources are currently being collected and analyzed as part of active big data efforts within their organization. 88% 73% 59% 57% 43% 42% 42% 41% 41% 40% 38% 34% 92% 81% 70% 65% 27% 19% 36% 47% 32% 0% 21% 22% Transactions LogData Events Emails Social Media Sensors External Feeds RFID Scans or POS Data Free-formText Geospatial Audio Still Images / Videos Banking & Fin Mgmt respondents Global respondents 3 6
- 30. © 2014 IBM Corporation37 Big Data Exploration Find, visualize, and understand all big data for improved decision making Enhanced 360o View of the Customer View all internal and external information sources to know everything about your customers Operations Analysis Analyze a variety of machine data for improved business results Data Warehouse Modernization Modernize the data warehouse with new technology: in-memory, stream computing, Hadoop, appliances, while building confidence in all data Security Intelligence Extension Lower risk, detect fraud and monitor cyber security in real-time Big Data Use Cases
- 31. © 2014 IBM Corporation38 Arild Kristensen IBM Norway Nordic Sales Manager Forusbeen 10 Big Data Analytics 4033 Stavanger IBM Software Group Mobile: +47 90 53 25 91 Information Management [email protected] linkedin.com/pub/arild- kristensen/34/96b/184 twitter.com/ArildWK www.ibmbigdatahub.com www.analyticszone.com