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Watson and Analytics
- 1. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION IBM Watson and Analytics: Welcome to the Cognitive Era A new era in technology, a new era in business. Jerry Carroll, Executive Architect – Big Data & Analytics jbcarrol@us.ibm.com
- 2. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION Where code goes, where data flows, cognition will follow. 2 Watson is ushering in a new era of computing Tabulating Systems Programmable Era Cognitive Era 1900 1950 2011
- 3. 3 Here’s what theworld is saying about the impact of Cognitive systems on the future of how we work: “IBM Crafts a Role for Artificial Intelligence in Medicine.” “IBM Watson represents a bold technological and visionary step a future in which every aspect of our lives will be enhanced by the utility of cognitive computing as it is harnessed into myriad new applications.” “What is distinctive about IBM is the breadth of its effort to create Watson tools and services as plug-in offerings for a wide range of developers.” “At Wayin, former Sun CEO Scott McNealy is using Watson's image recognition capabilities to trawl photos on social media and make them searchable, even when they don't have tags describing their content. ‘You can't do this without Watson,’ he said.” “IDC predicts the worldwide cognitive software platforms market will grow to $3.7 billion in 2019, at a CAGR of 35% over 5 years.” IDC: Worldwide Cognitive Software Platforms Forecast, 2015-2019: The Emergence of a New Market (#258781, September 2015, David Schubmehl) “IBM is the only company marketing a cognitive computing platform that’s specifically designed to support the development of a broad range of enterprise solutions.” “No doubt, Watson has the means to radically change the industry. In fact, its potential as an ‘innovation lake/incubator’ should be highly valued.” IDC: IBM’s Go-to-Market Transformation – Deeper, Wider, Newer (#AP257527, April 2015, Chris Zhang, Sabharinath Balasubramanian, Mayur Sahni) “IBM’s famous cognitive computer can help banks with complex financial operations and attack important health care problems. Now you can add seeing to its skill set.” “These days, it’s not just AI algorithms themselves that have improved, but the ability to deliver them…that has made so many new applications possible.”
- 4. There are threecapabilities that differentiate cognitive systems from traditional programmed computing systems. Reasoning They reason. They can understand information but also the underlying ideas and concepts. This reasoning ability can become more advanced over time. It’s the difference between the reasoning strategies we used as children to solve mathematical problems, and then the strategies we developed when we got into advanced math like geometry, algebra and calculus. Learning They never stop learning. As a technology, this means the system actually gets more valuable with time. They develop “expertise”. Think about what it means to be an expert- - it’s not about executing a mathematical model. We don’t consider our doctors to be experts in their fields because they answer every question correctly. We expect them to be able to reason and be transparent about their reasoning, and expose the rationale for why they came to a conclusion. Understanding Cognitive systems understand like humans do, whether that’s through natural language or the written word; vocal or visual.
- 5. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION Data is transforming industries and professions. 5 HOW, AND WHY NOW?
- 6. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION 6 CONSIDER: Data flows from every device, replacing guessing and approximations with precise information. Yet 80% of this data is unstructured; therefore, invisible to computers and of limited use to business. HEALTHCARE DATA GOVERNMENT & EDUCATION DATA 99% 88% 94% 84% Healthcare data comes from sources such as: Government & education data comes from sources such as: Patient Sensors Electronic Medical Records Test Results Vehicle Fleet Sensors Traffic Sensors Student Evaluations UTILITIES DATA MEDIA DATA 93% 84% 97% 82% Utilties data comes from sources such as: Media data comes from sources such as: Utility Sensors Employee Sensors Location Data Video and Film Images Audio By 2020, of new information will be created every minute for every human being on the planet. growth by 2017 unstructured growth by 2017 unstructured 1.7 MB growth by 2017 unstructured growth by 2017 unstructured
- 7. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION The world is being reinvented in code. 7 HOW, AND WHY NOW?
- 8. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION 8 CONSIDER: The world is being rewritten in software code, and cloud is the platform on which the new digital builders—from developers to business professionals—are reimagining everything from banking to retail to healthcare. Smart TVs represented 27% of all TV sales in 2012; by 2018, they will represent 82%. Smart LED lighting will grow from 6M units in 2015 to 570M units in 2020, used for safety communication, health, pollution and personalized services. By 2017, there will be 1B connected things in smart homes, including appliances, smoke detectors and cameras. 100,000,000 lines of code in a new car 5,000,000 lines of code in smart appliances 1,200,000 lines of code in a smartphone 80,000 lines of code in a pacemaker of B2B collaboration will take place through web APIs next year. 50% Sensors for industrial asset monitoring and management will grow from just over 15M units in 2014 to over 40M units in 2018 Smart traffic sensors and other devices installed in smart cities will grow from 237M units in 2015 to 371M in 2017. Revenues for smart grid sensors will grow ten-fold from 2014 to 2021. By 2020, there will be 925M smart meters installed worldwide, more than double the 400M in 2014. Code Tools Analytics Data APIs
- 9. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION Computing is entering a new cognitive era. 9 HOW, AND WHY NOW?
- 10. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION 10 CONSIDER: Cognitive systems can understand the world through sensing and interaction, reason using hypotheses and arguments and learn from experts and through data. Watson is the most advanced such system. Today, businesses in countries across. There are Watson ecosystem partner companies, with 78% of business and IT executives believe that successful business will manage employees alongside intelligent machines. On average there are Among C-Suite executives familiar with cognitive computing: 96% 84% 94% 89% in insurance intend to invest in cognitive capabilities. in healthcare believe it will play a disruptive role in the industry, and 60% believe they lack the skilled professionals and technical experience to achieve it. in retail intend to invest in cognitive capabilities. in telecommunications believe it will have a critical impact on the future of their business. 36 17industries are applying cognitive technologies. 350+ 100 of those have taken their product to market. 1.3B Watson API calls a month and growing.
- 11. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION • Deeper human engagement • Elevated expertise • Cognitive products and services • Cognitive processes and operations • Intelligent exploration and discovery ADVANTAGES OF COGNITIVE BUSINESS: 11
- 12. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION Relationship Extraction Questions & Answers Language Detection Personality Insights Keyword Extraction Image Link Extraction Feed Detection Visual Recognition Concept Expansion Concept Insights Dialog Sentimen t Analysis Text to Speech Tradeoff Analytics Natural Language Classifier Author Extraction Speech to Text Retrieve & Rank Watson News Language Translation Entity Extraction Tone Analyzer Concept Tagging Taxonomy Text Extraction Message Resonance Image Tagging Face Detection Answer Generation Usage Insights Fusion Q&A Video Augmentation Decision Optimization Knowledge Graph Risk Stratification Policy Identification Emotion Analysis Decision Support Criteria Classification Knowledge Canvas Easy Adaptation Knowledge Studio Service Statistical Dialog Q&A Qualification Factoid Pipeline Case Evaluation 12 IBM WATSON The Waston that competed on Jeopardy! in 2011 comprised what is now a single API—Q&A—built on five underlying technologies. Since then, Watson has grown to a family of 28 APIs. By the end of 2016, there will be nearly 50 Watson APIs— with more added every year. Natural Language Processing Machine Learning Question Analysis Feature Engineering Ontology Analysis
- 13. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION 13 IBM WATSON Personality Insights
- 14. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION 14 IBM WATSON These APIs are underpinned by 50 technologies: Anaphoric Co-referencing Colloquialism Processing Content Management -- Versioning Convolutional Neural Networks Curation Deep Learning Dialog Framing Ellipses Embedded Table Processing Ensembles and Fusion Entity Resolution Factoid Answering Feature Engineering Feature Normalization Focus and Spurious Phrase Resolution HTML Page Analysis Image Management Information Retrieval Knowledge (Property) Graphs Knowledge Answering Knowledge Extraction Annotators Knowledge Validation and Extrapolation Language Modeling Latent Semantic Analysis Learn To Rank Linguistic Analysis Logical Reasoning Analysis Logistical Regression Machine Learning Multi-Dimensional Clustering Multilingual training n-Gram Analysis (word combinations and distance) Ontology Analysis Pareto Analysis Passage Answering PDF Conversion Phoneme Aggregation Question Analysis Question-answering Reasoning Strategies Recursive Neural Networks Rules Processing Scalable Search Similarity Analytics Statistical Language Parsing Support Vector Machines Syllable Analysis Table Answering Visual Analysis Visual Rendering Voice Synthesis
- 15. © 2015 INTERNATIONALBUSINESS MACHINES CORPORATION 15 BECOMING A COGNITIVE BUSINESS 1. A cognitive strategy Determine what data you need, which experts will train the system; where you must build more human engagement; which products, services, processes and operations should be infused with cognition, and which parts of the unstructured 80% of data you most need to focus on to make discoveries for the future. 2. A foundation of data and analytics Collect and curate the right data—data you own, data from others, data available to all; both structured and unstructured. Apply cognitive technologies to this data in order to sense, learn and adapt, thereby creating competitive advantage. 3. Cloud services optimized for industry, data and cognitive APIs The building blocks for products and services are code, APIs and diverse data sets. The platform you choose to develop on, and the agile development culture and methods you embrace, will be critical to your success. 4. IT infrastructure tuned for cognitive workloads Architect a new kind of IT core—a heterogeneous infrastructure that serves as the backbone of your enterprise. Do this rapidly and affordably by harmonizing technologies from public, private and hybrid cloud with distributed devices, IoT instrumentation and your existing systems. 5. Security for a Cognitive Era As cognition makes its way into cars, buildings, roadways, business processes, fleets, supply chains—securing every transaction, piece of data, and interaction becomes essential to ensure trust in the entire system—and in your brand and reputation.
- 16. Power Systems –Designed for Big Data and Analytics solutions 16 Analytics 1.0
- 17. Power Systems –Designed for Big Data and Analytics solutions 17 Analytics 1.5
- 18. Power Systems –Designed for Big Data and Analytics solutions 18 Analytics 2.0 - support a variety of data and a range of analytics
- 19. Classic hadoop infrastructurescan be inefficient and inflexible leading to server and cluster sprawl, unnecessary software licenses, and infrastructure management challenges IBM Data Engine for Hadoop and Spark IBM Data Engine for Analytics
- 20. Avoid Server Sprawlas Big Data and Analytics environments grow Intel server growth estimated as a sample configuration with 500 TB of user space grows 5x and 10x to 2.5 PB and 5 PB: 5x 10x POWER8 server growth using IBM Data Engine for Analytics estimated as a sample configuration grows 5x and 10x to 2.5 PB and 5 PB of user space: 5x 10x Sizing is based on assumptions regarding general configurations and use cases for BigInsights and BigSQL with actual client. The comparison reflects the number of servers required to deliver relative performance and equivalent user space on Intel reference architecture using Hadoop triple replication with 4 TB drives versus POWER8 IDEA reference architecture with 4 TB drives using the Elastic Storage Server. 17 Servers 28 Servers 75 Servers 143 Servers
- 21. Many new solutionworkloads in addition to existing apps Leads to costly, complex, siloed, under-utilized infrastructure and replicated data Development Test Distributed ETL, Sensitivity Analysis Hadoop based Sentiment Analysis Low Utilization = Higher cost Low Utilization = Higher cost Infrastructure Silos aka Cluster Sprawl is Inefficient
- 22. IBM Data Enginefor Analytics (IDEA) – Client Example Client: Multinational Telecomm. Company A multinational telecommunication company with over 6M subscribers.. Challenges Expectations of a Real Time Marketing (RTM) based solution to run event-based campaigns Enable event-based marketing, analyzing various sources of input data containing information regarding subscribers actions Dispatch the triggered events to downstream applications such as campaign management, for associated campaign execution. Architecture- Solution Components • BigInsights, Streams, SPSS Modeller, SPSS Analytics Server • IBM Data Engine for Analytics: 20 X S822L, 2 X ESS GL4, Spectrum Scale, PCM Solution Approach Solution provided a Hadoop-based Big Data platform, integrated to the RTM decision engine, to enable data monetization opportunities, including location based analytics Customer was not comfortable with the huge number of x86 Data Nodes approach of typical Hadoop Architecture The IBM team designed the Power solution and conducted a technical workshop on newly redefined Hadoop architecture based on IDEA. Key Client Benefits Optimized Big Data deployment architecture with IDEA Architecture with Linux on Power, Elastic Storage Server and Spectrum Scale Lower TCO with 4 Racks on Power vs 12 racks on x86 More IO bandwidth with 40GbE Power network against 10GbE on x86 based solution 3x less racks for 2 PB Big Data solution 3x less racks for 2 PB Big Data solution 4 vs. 12
- 23. IBM Data Enginefor Analytics – Solution Highlights Actionable Insights with IBM BigInsights preloaded + Increase business value by consolidating multiple analytic capabilities and data as needed Up to 2.5x* faster insights Smart Infrastructure Services with IBM Platform Computing + Designed to handle multiple analytic workloads in a multi-tenant environment with dynamic resources Designed for Data with IBM POWER8 Systems (S822L) + Outstanding memory and IO bandwidth design for the demands of Big Data 2x* better performance Scalable Networking with IBM and partner networks + High bandwidth, low latency networking Ethernet RoCE, (10 Gbit or 40 Gbit), InfiniBand RDMA (FDR) 10x to 100x network performance growth since Hadoop inception Flexible Storage with IBM Elastic Storage Server + Combines Servers, Storage Enclosures, Disks and Elastic Storage Software Over 2x** reduction in storage disk count *Based on internal testing and cost analysis **Based on client example vs a triple replica Hadoop configuration. Big Data & Analytics Software Infrastructure Services POWER8 Servers Scalable Networking Scale Out Cluster File System Elastic Storage Server Appliance-Like but much more Versatile!
- 24. Compute Plane =Power8 Systems, Designed for Big Data 4X Threads per core* 4X Memory Bandwidth* 5X More cache* multaneous Multi-Threading On-Line Transaction Processing gh Performance Computing These design decisions result in best performance for all types of workloads such as: Java, OLTP, Analytics, Big Data, HPC * POWER8 compared to Intel Haswell EX Sources: Haswell EX: http://ark.intel.com/products/84685/Intel-Xeon-Processor-E7-8890-v3-45M-Cache-2_50-GHz POWER8: http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?subtype=BR&infotype=PM&appname=STGE_PO_PO_USEN& POWER8 SMT8 x86 SMT2 POWER8 pipe Data flow x86 pipe POWER8 x86 x86POWER8 1.4 – 2.3X Clock Frequency Components of IDEA
- 25. Data Plane =Elastic Storage Server, A Data Lake for many applications Cinder SwiftGPFS NFS Linear capacity & performance scale out POSIX Enterprise storage on standard hardware Technical Computing Big Data & Analytics Cloud File 77.5 Percent of organizations are already investing in a Data Lake* Elastic Storage Server Single Name Space 25 Hadoop Block Object Data Lake Structured Data Unstructured Data Traditional Analytics BigSQL Components of IDEA
- 26. Production - IBMData Engine for Analytics 26 Without HMC/TFT/SMN/MN With HMC/TFT/SMN Available Filesystem Capacity 0PB 1PB Number of Data Nodes 14 4 Hadoop Mgmt LPARs 0 6 System Mgmt Node 0 1 Data Node – S822L 24x 3.02GHz cores 256GB DRAM 12x 1.8TB 10K SAS HDD 2x40GbE (2 port)(data+mgmt) 2x 4-port 1GbE NIC (mgmt) Hadoop Management Node – S822L 2 LPARs with split backplane 24x 3.02GHz cores 256GB DRAM 8x 1.8TB 10K SAS HDD (OS + data) 2x40GbE (2 port)(data+mgmt) 2x 4-port 1GbE NIC (mgmt) System Management Node – S812L 10x 3.425GHz cores 32GB DRAM 2x 300GB 10K SAS HDD (OS) 1x40GbE (2 port)(data+mgmt) 1x 4-port 1GbE NIC (mgmt) Initial RackScale out Racks
- 27. Classic hadoop infrastructurescan be inefficient and inflexible leading to server and cluster sprawl, unnecessary software licenses, and infrastructure management challenges IBM Data Engine for Hadoop and Spark IBM Data Engine for Analytics
- 28.
- 29. Single vendor support Upto 2x better price performance for Spark workloads* Delivered as a fully integrated cluster ready to run OpenPOWER innovation with IBM S812LC servers Optimized configurations for Hadoop or Spark workloads Based on S812LC servers with up to 14*6TB disk drives per server Optionally preloaded with IBM BigInsights and IBM Open Platform Simplify operations – easy to deploy and manage Adapt and scale to your changing analytics needs IBM Data Engine for Hadoop and Spark OpenPOWER innovation with IBM Open Platform with Apache Hadoop for a high performance, storage dense and fully integrated cluster offering. • All results are based on IBM Internal Testing of 3 SparkBench benchmarks consisting of SQL RDD Relation, Logistic Regression, SVM Announce: Feb 9, 2016 GA: Mar 18, 2016
- 30. IBM Data Enginefor Spark and Hadoop (IDE-HS) Cluster Performance Designed for the Cognitive Era to Make Better Decisions even Faster IBM Data Engine for Hadoop and Spark infrastructure delivers Spark workload scaling to minimize execution times and reduce batch windows -2.1X more performance per dollar spent for Spark Logistic Regression based Machine Learning used in model training by wide variety of lines of business -1.4X more performance per dollar spent for Support Vector Machine (SVM) – a Machine Learning algorithm used in product Recommender Systems -1.7X more performance per dollar spent for Spark SQL query processing used widely in Big Data clusters • All results are based on IBM Internal Testing of 3 SparkBench benchmarks consisting of SQL RDD Relation, Logistic Regression, SVM • 6 Data Nodes and 1 Management Node. Each node is IBM Power System S812LC 10 cores / 80 threads, POWER8; 2.92GHz, 256 GB memory, RedHat 7.2, Spark 1.5.1, OpenJDK 1.8 • 6 Data Nodes and 1 Management Node. Each node is x86 E5-2620V3 12 cores / 24 threads, E5-2620 V3; 2.4GHz, 256 GB memory, RedHat 7.1, Spark 1.5.1, OpenJDK 1.8 • Pricing is based on web prices of HP DL380 and list prices of IBM Power S812LC SVMLogres SQL SVMLogres SQL 6
- 31. • Apache Sparkis an open-source in-memory distributed compute engine – It speeds iterative analysis on large-scale data up to 100x faster than current technologies – Enables more people to collaborate together to access data, apply analytics and deploy deep intelligence into every application including IoT, web, mobile, social, business process and more – IBM/Spark commitment: 3500 employees working on Spark • Included in the IBM Open Platform (IOP) that runs on Linux on Power • Power Systems - key contributor to Spark • Offering over 2x the performance per core for Spark workloads compared to x86 Haswell * (SQL, ML, Graph, Streaming) Open Platform with Apache Hadoop Open innovation to put data to work across the enterprise * Based on Sparkbench on POWER8 P822L vs x86 E5-2690 V3; each 24 core and 256 GB RAM Spark on Power
Editor's Notes
- #2 1) What is the relationship between Cognitive Business, Watson and outthink? “Cognitive Business” is a branded POV for the entire IBM Company, while “Watson” is the lead brand for IBM’s cognitive offerings. “Outthink” is our marketing campaign that supports Cognitive Business and features Watson. ANALYTICS 2.0
- #3 We have the most sophisticated cognitive technology.
- #4 Cognitive systems are making us rethink the way business gets done. They’re becoming integral to the way we work and make decisions – and the market is validating this.
- #5 Understand Understands data–structured and unstructured, text-based or sensory–in context and meaning, at astonishing speeds and volumes. Reason Has the ability to form hypotheses, make considered arguments and prioritize recommendations to help humans make better decisions. Learn Ingests and accumulates data and insight from every interaction continuously. Is trained, not programmed, by experts who enhance, scale and accelerate their expertise. Therefore, it gets better over time.
- #6 What are the forces driving the idea of cognitive business? It’s information, it’s data – Cognitive systems are knowledge systems; fueled by the magnitudes of information available to us. For the first time in history, the volume of data and information we’re producing has outpaced our ability to make use of it. And the sources and types of data that inform the work we do and the decisions we make are broader and more diverse than ever before. This isn’t news, because most of us spend a lot of time figuring out how to surface actionable insights from massive amounts of data and information. Since 2014, the number of businesses that have implemented data-driven projects has increased by 125%, with executives citing improved speed and quality of decision making as their top priority. Even with these advanced analytics solutions, businesses estimate that they’re only reaching 12% of the data they have, leaving 88% of it to waste. That’s because this 88% of data is “invisible” to computers. It’s the type of data that humanity encodes in language and unstructured information, in the form of text – books, emails, journals, blogs, articles, tweets, as well as images, sound and motion. We need better way to take command of the knowledge and information that matters most to us. We need to be able to discover new connections, patterns, and insights from within it. And a new way to think about expertise – in order to draw new conclusions and make decisions with more confidence and speed than ever before.
- #11 Today, businesses and organizations in 36 countries, across 29 industries and 5 languages (Arabic, English, Japanese, Brazilian Portuguese, Spanish) are using Watson to build cognitive abilities into their products, applications, processes, and offerings: The 50,000 students at Deakin University in Australia using Watson as a student advisor to answer their questions as they arrive on campus; The 1.1 million patients in Bumrungrad Hospital’s network who now have access to personalized cancer treatment recommendations with help from a system trained by the doctors at the worlds leading cancer centers; The 5.5 million citizens in Singapore who have access to government services with help from Watson; 80,00 developers, VCs, and start-ups using Watson APIs. More than 350 Watson ecosystem partner companies, with 100 of their applications already in market. And countless chief marketing officers, analysts, researchers, and many more who are making connections and discoveries with apps powered by Watson. Let’s take a closer look at a few examples:
- #12 1. 96% of unhappy customers don’t complain, but 91% never come back 2. Cognitive learning makes expertise accessible on a new scale by making it easy for any professional to keep pace with knowledge from the entire field and learn from the best in the world. Scaling the greatest mind to every mind. 3. Cognitive products and services can sense, reason and learn so they can adapt and develop new capabilities not previously imaginable. Apps with advanced and predictive analytics are growing 65% than apps without this functionality.. 4. Cognitive systems bring more certainty to business by extracting real-time information from workflows, context and environment to enhance forecasting and decision-making. 5. Cognitive discovery changes the odds for high-stakes research by enabling companies to mine insights from vast amounts of data, and uncover patterns and opportunities that would be virtually impossible to find through traditional methods.
- #13 Watson is a cloud-based, open platform of expanding cognitive capabilities. With Watson, you can build cognition into digital applications, products and operations. Next, you can leverage Watson APIs – cognitive building blocks - to apply Watson’s capabilities. Watson APIs are delivered on a cloud-based, open platform, and with Watson, you can build cognition into your digital applications, products, and operations, using any one or combination of 28 available APIs. For example, Natural Language Classifier API enables developers without a background in machine learning or statistical algorithms to create machine-learning, natural language interfaces for their applications. Tone analyzer helps individuals understand the linguistic tone of their writing. This API uses linguistic analysis to detect and interpret emotional, social, and writing cues that are located within the text, and also offers rhetorical suggestions for an author to improve the intended tone. Retrieve and rank helps users find the most relevant information for their query by using a combination of search and machine learning algorithms to detect “signals” in the data. – cognitive building blocks – to leverage capabilities including relationship extraction, personality analysis, tone analysis, concept expansion, and trade-off analytics, among others. Each API is capable of performing a different task, and in combination, they can be adapted to solve any number of business problems or create deeply engaging experiences. And we continue to add new and expanded cognitive capabilities to the platform.
- #16 Becoming a Cognitive Business is a journey. Leaders can capitalize on all the foundational work they’ve done to deploy cloud, analytics, mobile, social, security. P8 for Jeopardy
- #19 Build a platform that is fluent in all forms of data and analytics. It’s really about realizing it; it’s about investing in this big data and analytics platform, to build out against a master plan that will eventually accommodate all types of data, any type of analytics and really drive towards a full range of business outcomes. After all data requires analytics to make sense of it and analytics requires data in order to fuel it, so these things are really quite tightly tied together. When we look at IBM’s strategy around big data and analytics, they want to make sure that we have a portfolio of hardware, software, storage, services, everything to address the customer’s needs. When we talk to a client they just don’t want to talk about the storage, they don’t want to just talk about servers, they want to talk about a business challenge that they have and they want to find a way of solving the business challenge in many times with a single company that understands the entire end-to-end solution and can put it in a way that helps them execute within their cost restraints and that’s what our goal is. If you read this chart left right, we talk about all of this data, the structured and unstructured data. When bringing it in, some of it is going to be stored in unstructured format, it can be either data in motion which is Streaming the board data at rest which would be Hadoop, or we could put it into a structured database and that’s where IBM DB2 with BLU Acceleration plays. Then there is a whole line of innovative analytics solutions, everything from our cognitive solutions around Watson, to our Cognos and SPSS and our industry solutions. We want to be able to take these solutions and drive them into key business processes. The really nice thing is IBM can offer all of this on a single platform, IBM Power Systems. Deliver insights quickly: Access all data and make better decisions with a unified view of information across all sources Optimized for the unique demands of Big Data applications built with Spark and Hadoop Deploy big data technologies with confidence: An economical entry point Superior price/performance, with 2.3X BETTER performance per dollar spent A platform that can scale with your needs UNSTRUCTURED: Hadoop, CAPI-enabled Flash, NoSQL - Derive actionable insight using industry cost-efficient solutions. Solutions such as IBM Data Engine for Analytics, IBM Solution for Hadoop, IBM Data Engine for NoSQL, InfoSphere Up to 3X lower TCA IN-MEMORY: Perform faster in-memory performance with leading database providers. Two of three support Linux on Power Little Endian (not Oracle) DB2 BLU Acceleration, Oracle Database 12, SAP HANA* 56% more query results per hour STRUCTURED: Compute tremendous amounts of data rapidly and support multiple databases DB2, Oracle, EnterpriseDB, MariaDB and other industry databases, Cognos, SPSS 82X faster insights
- #21 Many are now discovering that there are advantages in taking a shared storage approach for Hadoop, and leading companies like IBM are challenging the common assumptions of how best to house data in the big data space. Key Message: The IDEA solution has all the key elements needed to support BDA deployments with outstanding performance all in a pre-intregated package and it can grow as clients needs change. 1. Innovative design 2. SDI – Spectrum Scale and PS. the traditional databases and grid computing technologies they had in-house would not scale. Hadoop stores everything in a schema-less structure centralized Hadoop implementation spans every system across the entire company to break down data silos and provide a single, comprehensive view of all its data.
- #22 Many are now discovering that there are advantages in taking a shared storage approach for Hadoop, and leading companies like IBM are challenging the common assumptions of how best to house data in the big data space. Key Message: The IDEA solution has all the key elements needed to support BDA deployments with outstanding performance all in a pre-intregated package and it can grow as clients needs change. 1. Innovative design 2. SDI – Spectrum Scale and PS. the traditional databases and grid computing technologies they had in-house would not scale. Hadoop stores everything in a schema-less structure centralized Hadoop implementation spans every system across the entire company to break down data silos and provide a single, comprehensive view of all its data.
- #24 Many are now discovering that there are advantages in taking a shared storage approach for Hadoop, and leading companies like IBM are challenging the common assumptions of how best to house data in the big data space. Key Message: The IDEA solution has all the key elements needed to support BDA deployments with outstanding performance all in a pre-intregated package and it can grow as clients needs change. 1. Innovative design 2. SDI – Spectrum Scale and PS. the traditional databases and grid computing technologies they had in-house would not scale. Hadoop stores everything in a schema-less structure centralized Hadoop implementation spans every system across the entire company to break down data silos and provide a single, comprehensive view of all its data.
- #25 4x Threads = 8 threads /core vs 2 threads / core = 4x 4X Memory Bandwidth = 410 GB/s vs 102 GB/s = 4x 5X Cache = For POWER8: L1 = 96 KB, L2 = 512 KB, L3 = 96 MB and L4 = 128 MB For Haswell-EX: L1 = 64 KB, L2 = 256 KB, L3 = 45 MB Total in MB: For POWER8 = .096 + .512 + 96 + 128 = 224.608 MB For Haswell-EX = 0.064 + 0.256 + 45 = 45.32 MB Ratio = 4.96 Clock Rates (09/29/15) Haswell EX slowest rate = 1.9 GHz Haswell EX fastest rate = 3.2 GHz POWER8 (E880) fastest rate = 4.35GHz
- #26 Key Message: The Elastic Storage Server provide a consolidated storage solution that can store a wide variety of data types supporting a range of applications with standard access methods. One shared Data Lake allows for the same data to be shared across different application domains and global locations while reducing the need for data movement and copies thus saving significant costs for storage, floor space and administration. Data Lake: A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed.
- #30 Extremely competitive $/TB with starter configurations over 200TB raw for around $100K
- #32 Key Message: Leading performance! Enables faster insights on less infrastructure.