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Introduction: Real-Time Analytics on Data in Motion

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Stream Computing is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of real-time sources. The solution can handle very high data throughput rates, up to millions of events or messages per second.

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Introduction: Real-Time Analytics on Data in Motion

  1. 1. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less © 2014 IBM Corporation Introduction: Real-Time Analytics on Data in Motion Analyze More, Speed Actions, Store Less
  2. 2. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 2 The state of big data is changing What is context-aware stream computing? Streaming data is challenging Stream Computing –Experience the power of now: secure, continuous, dynamic Industry leaders Learn More Agenda
  3. 3. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 3 The state of big data is changing Can you quickly spot the opportunity in your data? Market Trend Business Impact 1. Movement from batch to real-time analytics Faster decisions required to keep pace with competition, 66% increase in streaming analytics 2. Organizations can’t keep up with fast data The value of data decreases over time, 2 weeks to analyze social data on average 3. Missed opportunities/risks despite analytics Organizations waste $1.3 million/year on false positives, 21,000 hours wasted time 4. More data (sensors, social, mobile) but the ability to make sense of it is declining Organizations can make sense of less than 2% of their data 5. The rise of machine data Organizations unable to analyze machine data, 40% of machines connected by 2020
  4. 4. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 4 1st Platform 2nd Platform 3rd Platform Streaming data the new normal, interactions/events need to be analyzed in real-time NOT ONLY transactions
  5. 5. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less The value drivers for big data have shifted to velocity and veracity 55 Data in many formsVariety Data at speed Velocity Data at scaleVolume Data as trustworthy Veracity 4 Vs of big data 2012 differentiators 2014 differentiators Scalable / extensible infrastructure Scalable storage infrastructures enable larger workloads High-capacity warehouses support the variety of data Data integration topped the data priorities of most organizations Agile and flexible infrastructure Big data landing platform expands the structured and unstructured data available for usage Real-time analysis processing enables ‘in the moment’ actions Trustworthiness is now the top data priority across majority of organizations Source: http://www-935.ibm.com/services/us/gbs/thoughtleadership/2014analytics/ IBM Institute for Business Value, Analytics: The Speed Advantage
  6. 6. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 6 Streaming data is challenging 2xs Sometimes 1 minute is too late. How to quickly process, analyze and act on data? What opportunity are you missing? Data volumes double every year. Too much to store and then analyze. How to analyze now before insight is lost or forgotten? Dashboard overload. Too much history and not enough forward thinking. How to get ahead, plan and predict vs react? Soon there will be 1 trillion connect things. Are you restricting your analytics? Too much noise. Too much low value data. How to pre-process all data on the fly. Keep only what is valuable. Minute 1Trillion Business Need Connect the right data to the right people in the right context for the right decisions at the right time
  7. 7. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 7 Missed Opportunities, Limited Observation Space Typical approaches to stream computing miss the mark Time Consuming, Requires Deep Skills Typical Approaches Fall Short Resource Intensive, Slow Risky, Very Expensive, Skills Gap Build More Business Rules Expand Warehouse, Add Data Build in House Solution Deploy Another Analytic Silo
  8. 8. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 8 IBM InfoSphere Streams for Context-Aware Stream Computing Experience the power of now: secure, continuous, dynamic Real-Time Action Context- Aware AnalyticsData Acquire Broadest range of data types Analyze Continuous multimodal analytics Act Right time, right method
  9. 9. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 9 Integration with existing architectures Privacy built in IBM services and support Top Performance Real-Time Analytics Enterprise Ready Context Awareness IBM context-aware stream computing is a “must” for business Text Predictive Geospatial Acoustic Image/Video Statistical Time series Statistics/Mathematics Natural language processing No training data or rules required. Self learning, self correcting, cognitive system. All data, all cultures, all languages More Efficient: 14.2x less hardware resources Faster: 12.3x more throughput Scalable: Advantage increases as scale increases
  10. 10. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 10 Market leading development environment Intelligent optimization and centralized management Speed time to market. 45% faster delivery Reduce operational cost and complexity. 1.5 people manage lar ge gover nment application Faster results with a smaller hardware footprint InfoSphere Streams delivers superior performance and lowers TCO Performance advantage increases as scale increases Run the benchmark to see for yourself https://github.com/IBMStreams/benchmarks Read Benchmark Results Read TCO Analysis Do more with less. 14.2x less hardware resources 12.3x more throughput
  11. 11. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 11 IBM recognized as a leader The Forrester Wave™: Big Data Streaming Analytics Platforms, Q3 ‘14 The Forrester Wave is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of Forrester Research, Inc. The Forrester Wave is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. “InfoSphere Streams is industrial strength.” “IBM scored highest on performance and scalability optimization, and also has comprehensive stream processing operators and development tools that can satisfy the gnarliest of use-cases.” Earned the highest possible score in data sources, ability to execute, and implementation support. “InfoSphere Streams includes customers in healthcare, financial services, telecommunications, government, energy and utilities, manufacturing, & transportation.” “Open source is hyped, but commercial vendors got the goods.”
  12. 12. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 12 EDC Survey of Big Data Developers  “IBM, which is credited with inventing the stream computing concept, recently extended its InfoSphere Streams …”  “IBM InfoSphere Streams came out well ahead of most every other major player, with 41% …” SOURCE: Big Data and Advanced Analytics Survey Volume II, 2014, Evans Data Corporation Which stream processing runtimes are you using? Count Percent of Responses Percent of Cases InfoSphere Streams 161 19.9 41.0 Apache Storm 130 16.0 33.1 Software AG Apama 104 12.8 26.5 Amazon Kinesis 93 11.5 23.7 Tibco Streambase 82 10.1 20.9 Apache Spark Streaming 76 9.4 19.3 LinkedIn Samza 63 7.8 16.0 Yahoo S4 34 4.2 8.7 Other 67 8.3 17.0 ------- ------- ------- Total 810 100 206.1
  13. 13. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less Vendor Big Data Revenue IBM $1,252 HP $664 Teradata $435 Dell $425 Oracle $415 SAP $368 EMC $336 IBM recognized as industry leader in big data 13 Number #1 in Wikibon’s Big Data Vendor Revenue and Market Forecast 2012-2017
  14. 14. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 1414 Make sense of big data in the business moment Real-time Actionable Insight Better Focused Human Attention Detection of New & Emerging Patterns What’s the business value of context-aware stream computing?
  15. 15. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 15 Market response to InfoSphere Streams for context-awareness Streaming data is emerging as the best source for real-time insights Decision-making is moving from the elite few to the empowered many As the value of streaming data continues to grow – open source systems won’t keep pace Consolidated Communications Holdings, Inc. uses real-time analytics to avoid business disruptions and eliminate manual thresholds resulting in a yearly cost avoidance of $300,000 CenterPoint Energy empowers customer service reps to resolve problems electronically, thus saving 700,000 gallons of fuel and lowering customer costs by $24M •Open source is hyped, but commercial vendors got the goods •Exploiting perishable insights is a huge, untapped opportunity for firms Consolidated Communications Case Study CenterPoint Energy Case Study Forrester Wave: Streaming Analytics Platforms
  16. 16. © 2014 IBM Corporation Analyze More, Speed Actions, Store Less 16 Learn more on Stream Computing  InfoSphere Streams product website  IBM Context-Aware Stream Computing webpage  IBM Context-Aware Stream Computing on Big Data Hub  InfoSphere Streams developerWorks community  InfoSphere Streams Developer Community  InfoSphere Streams data sheet  InfoSphere Streams for industry alignment webpage Kimberly Madia @madiakc Avadhoot (Avi) Patwardhan @avi_patwardhan

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