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What Is a Case Study?

When you’re performing research as part of your job or for a school assignment, you’ll probably come across case studies that help you to learn more about the topic at hand. But what is a case study and why are they helpful? Read on to learn all about case studies.

Deep Dive into a Topic

At face value, a case study is a deep dive into a topic. Case studies can be found in many fields, particularly across the social sciences and medicine. When you conduct a case study, you create a body of research based on an inquiry and related data from analysis of a group, individual or controlled research environment.

As a researcher, you can benefit from the analysis of case studies similar to inquiries you’re currently studying. Researchers often rely on case studies to answer questions that basic information and standard diagnostics cannot address.

Study a Pattern

One of the main objectives of a case study is to find a pattern that answers whatever the initial inquiry seeks to find. This might be a question about why college students are prone to certain eating habits or what mental health problems afflict house fire survivors. The researcher then collects data, either through observation or data research, and starts connecting the dots to find underlying behaviors or impacts of the sample group’s behavior.

Gather Evidence

During the study period, the researcher gathers evidence to back the observed patterns and future claims that’ll be derived from the data. Since case studies are usually presented in the professional environment, it’s not enough to simply have a theory and observational notes to back up a claim. Instead, the researcher must provide evidence to support the body of study and the resulting conclusions.

Present Findings

As the study progresses, the researcher develops a solid case to present to peers or a governing body. Case study presentation is important because it legitimizes the body of research and opens the findings to a broader analysis that may end up drawing a conclusion that’s more true to the data than what one or two researchers might establish. The presentation might be formal or casual, depending on the case study itself.

Draw Conclusions

Once the body of research is established, it’s time to draw conclusions from the case study. As with all social sciences studies, conclusions from one researcher shouldn’t necessarily be taken as gospel, but they’re helpful for advancing the body of knowledge in a given field. For that purpose, they’re an invaluable way of gathering new material and presenting ideas that others in the field can learn from and expand upon.

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How IBM Watson is Powering Big Data & Analytics

Darryl Worsham | January 18, 2018

We’ve covered   big data and analytics  in some of our previous posts, and in this post, we focus solely on   IBM Watson big data. Where it started, its evolution and growth, and how IBM Watson is powering big data and analytics today. We also explore some industry use cases. You’ll have a greater understanding and appreciation for IBM Watson and big data.

Download our summary sheet to learn more about the future of IBM Watson.

Cognitive Computing & IBM Watson

Before IBM Watson, an IBM Research team worked on a project called Deep Blue. Deep Blue was programmed to sift and sort through up to 200 million possible chess positions per second. In 1997, Deep Blue made international headlines by using high-powered computing to win a chess match. Later, the architecture behind Deep Blue gave developers insight into ways they could use this type of processing for predictive modeling in the world of finance, healthcare, and business practices.

Deep Blue placed IBM at the forefront of the cognitive computing technology era, leading to IBM Watson.

Watson, The First Iteration

In early 2011, IBM caught international attention again as IBM Watson beat two of the all-time most successful human players on Jeopardy! without an internet connection. It only knew what it had amassed through years of persistent interaction and learning from a large set of unstructured knowledge. Using machine learning, statistical analysis, and natural language processing to find and understand the clues in the questions, IBM Watson then compared possible answers, by ranking its confidence in their accuracy, and responded – all in about three seconds.

Source:   IBM

Applications of IBM Big Data Analytics

Advancements in the Watson ecosystem have evolved at a rapid pace since this first iteration. What follows are some examples and case studies of how IBM Watson is powering big data and predictive analytics with long-term value.

The healthcare sector generates petabytes of data, information from Electronic Patient Healthcare Records (EPHR), research, trials and medical journals is often unstructured and due to the sheer volume of it, can’t be processed manually by humans.

Because of this, potentially valuable insights are being lost which could have lifesaving implications such as helping to diagnose medical conditions or identify patients that will respond better to some treatments more than others.

Cancer patients

According to IBM, Watson is adding value to Oncology and is being used to suggest tailored treatment plans for patients based on their unique medical records.  Not everyone benefits or responds to treatment in the same way, but with Watson, healthcare professionals can crunch large volumes of data to identify treatments that have the highest probability of success.

387 million people in the world live with diabetes, and according to Hooman Hakami, Executive Vice President and Group President of Medtronic Diabetes:

“The number of people isn’t slowing down, the cost isn’t slowing down. We need to do something different.”

To address this, IBM, in conjunction with   Medtronic , worked together to produce a Cognitive Application that can help with the prevention, identification, and management of diabetes.

The Medtronic and Watson application dubbed Watson Health was designed to help patients manage their condition daily. One of the ways in which it’s able to do this is be performing a retrospective analysis of a patient’s insulin levels, continuous glucose monitors, and nutritional data.

The application can help patients understand how their everyday behavior is affecting fluctuations in glucose levels in real-time. The hope is that patients will be able to pinpoint lifestyle choices and their effects and adjust or make healthier decisions.

In 2012, Citi partnered with IBM to explore the use of Watson technology with consumer banking solutions. After developing cognitive technologies for customer insight, IBM went on to develop technology to help finance firms manage regulatory and compliance controls. Today,   IBM Watson Financial Services  powers services in the wealth management industry, insurance, and financial risk management solutions.

Source   ZDNet

With a history of data analysis and predictive modeling, it’s no surprise that the marketing industry is a growing marketplace for IBM Watson. In 2016,   SugarCRM partnered with IBM to help businesses across the automotive, insurance, hospitality, retail, industrial, government, and banking industry solve CRM problems.  In 2017, Salesforce announced a global strategic partnership to power Salesforce Einstein.

Wimbledon Case Study

Back in 2016, powered by IBM Watson big data, the Wimbledon editorial could monitor social channels and break new stories and mold the narrative to encourage new fans from other sports such as the F1 to visit its tennis-related platforms.

The results were impressive, a 24% increase in social media audience, an increase of 25% in terms of video views from 2015’s event (110 million video views).

View Habits and Attention

Internet user habits have changed dramatically in recent years, attention spans are shorter, and from a marketing perspective, it is often said that we live in an “attention economy.”

Watson allowed the Wimbledon team to capture the audience’s attention by allowing the team to continuously publish the right content in the right places at the right times.

Twitter Data

Twitter, the global social media platform is the platform of choice if you want to find out what’s happening now .

For the Wimbledon Team to create compelling content for social media audiences, it paid to keep an eye on the hot topics of the day.  A task like this is impossible to achieve manually, but by leveraging Watson’s Natural Language Classifier, the team could “surface” key topics and phrases of the day.

Instead of using simple keyword filtering such as searching for a hashtag such as “#wimbledon,” Watson was trained using historical datasets to highlight messages that were relevant to the tournament.

To further enrich this data and gain additional insights, Watson’s  Alchemy Language  service was used to identify metadata from the text of relevant social media posts. Using this service allowed the team to identify things such as the players and matches being mentioned or the sentiment of the post (the underlying emotion of the message).

Throughout the tournament, over 17 million pieces of social media content were processed and aggregated.  This information was displayed in real-time dashboards in the Cognitive-Social Command Centre interface.

[bctt tweet=”By identifying signals in the “noise,” the editorial team could then make better-informed decisions about how best to connect with the audience. #Wimbledon #CaseStudy #IBMWatson ” username=”GAPapps”]

You might think that 17 million posts sound like far too much content to process, but Watson was able to “surface” the most important topics and messages that were relevant to the Editorial Team. This gave the team insights as to which topics and stories had viewers and fans excited.

We’ve explored AI, machine learning and its effects on employment in detail on another blog post . While a platform like IBM Watson allows healthcare professionals to quickly analyze vast quantities of medical data whether it be in text, video or web format at incredible speeds, we don’t see clinicians and other healthcare professionals being replaced anytime soon. The same can be said for many other industries.

Platforms like IBM Watson will extend existing human capabilities by allowing professionals to derive actionable insights from data that was invisible prior to using AI and machine learning technology.

Here at Growth Acceleration Partners, we have extensive expertise in many verticals.  Our Centers of Engineering Excellence (COEs) in Latin America focus on combining business acumen with top-notch expertise to help your business.

We can provide analytics services for your organization with resources in the following areas:

• data analytics
• data science
• information systems
• machine learning
• predictive modeling
• software development
• …and much more!

If you’d like to find out more, then visit our website  here .  Or if you’d prefer, why not arrange a call with us or send us an email ?

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IBM Big Data Analytics Concepts and Use Cases

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• 1 . © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. What Is Big Data? Architectures and Practical Use Cases Tony Pearson Master Inventor and Senior IT Specialist IBM Corporation
• 2 . 2 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Abstract Do you understand the storage implications of big data analytics? This session will explain what big data is, provide some practical use cases, then explain the IBM products that support big data
• 3 . 3 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. This week with Tony Pearson Day Time Topic Monday 10:15am Opening Session – Storage 01:45pm IBM's Cloud Storage Options Tuesday 11:30am Software Defined Storage -- Why? What? How? (repeats Friday) 03:15pm The Pendulum Swings Back – Understanding Converged and Hyperconverged Environments 04:30pm New Generation of Storage Tiering: Less Management Lower Cost and Increased Performance Wednesday 09:00am What Is Big Data? Architectures and Practical Use Cases 01:45pm Data Footprint Reduction – Understanding IBM Storage Efficiency Options 03:15pm IBM Spectrum Virtualize – SVC, Storwize and FlashSystem V9000 (repeats Friday) Thursday 10:15am IBM Spectrum Scale and Elastic Storage Offerings 01:45pm IBM Spectrum Scale for File and Object storage 03:15pm IBM Storage Integration with OpenStack 05:45pm Meet the Experts Friday 09:00am Software Defined Storage -- Why? What? How? 10:15am IBM Spectrum Virtualize – SVC, Storwize and FlashSystem V9000
• 4 . What is Big Data? Big Data Use Cases IBM Analytics Platform IBM Spectrum Scale Agenda
• 5 . 5 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. What is Big Data? Data sets so large and complex that it becomes difficult to process using relational databases The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization Analysis of a single large set of related data allows correlations to be found Can be used to identify trends, patterns and insights to make better decisions Source: Wikipedia
• 6 . 6 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. OLAP cube Extract Transform Load (ETL) Strategic planning based on historical analysis and speculation Day-to-day operations based on reports, news, intuition Business Executives Make decisions 3 Traditional Decision Making Process Reports Batch Processing Transaction and Application data Database Administrators System of Record Gather data 1 Business Analysts Analyze 2
• 7 . 7 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. What has Changed in the Last Few Decades? 1986 2015 6% 99% Analog data Digital data Transaction and Application data Machine data Social media, email Enterprise content 20% Structured data 80% Unstructured data
• 8 . 8 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. New Sources of Data to Analyze – the Four V’s of big data Volume – Scale of data has grown beyond relational database capabilities Variety – Machine data, enterprise content, and social media and email Velocity – Computing has advanced to receive and analyze real-time data streams Veracity – How much can you trust the data is right and accurate? Transaction and Application data Database Administrators System of Record System of Engagement System of Insight Machine Data, log data Social media, photos, audio, video, email Enterprise content Storage Administrators Gather and Identify sources of data 1
• 9 . 9 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Data is the New Oil DATA is the new OIL In its raw form, oil has little value… Once processed and refined, it helps to power the world!
• 10 . 10 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Structured, Repeatable, Linear OLAP cube Unstructured, Exploratory, Iterative New Capabilities to Analyze the Data Reports Visualization and Discovery Hadoop Data warehousing Stream Computing Integration and Governance Text Analytics Business Analyst Data Scientist Analyze data2
• 11 . 11 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. What does a Data Scientist do? “It’s no longer hard to find the answer to a given question; the hard part is finding the right question. And as questions evolve, we gain better insight into our ecosystem and our business.” -- Kevin Weil, Lead Analyst at Twitter A data scientist must have… – Strong business acumen – Modeling, statistics, analytics and math skills – Ability to communicate findings, tell a story from the data, to both business and IT leaders Inquisitive: exploring, doing “what if?” analyses, questioning existing assumptions and processes to spot trends, patterns and hidden insight. Computers are useless. They can only give you answers. – Pablo Picasso Source: http://www-01.ibm.com/software/data/infosphere/data-scientist/ http://blog.cloudera.com/blog/2010/09/twitter-analytics-lead-kevin-weil-and-a-presenter-at-hadoop-world-interviewed/
• 12 . 12 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Data Information Knowledge Wisdom (DIKW) Wisdom Applied I better stop the car! Knowledge Context The traffic light I am driving towards has turned red Information Meaning South-facing light at corner of Pitt and George streets has turn red Data Raw červený 685 nm, 421 THz, #FF0000 http://legoviews.com/2013/04/06/put-knowledge-into-action-and-enhance-organisational-wisdom-lsp-and-dikw/
• 13 . 13 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Better Decisions for New Business Outcomes Day-to-day operations based on real-time analytics Strategic planning based on science, trends, patterns and insight Know Everything about your Customers Innovate new products at Speed and Scale Instant Awareness of Fraud and Risk Exploit Instrumented Assets Run Zero-latency Operations Business Executive Make Decisions and Take Action 3 Empowered Employees
• 14 . 14 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. statistical models Decision Making Process in the Era of big data Real-time Analytics Database Administrators System of Insight Strategic planning based on science, trends, patterns and insight Dashboard Storage Administrators Gather and Identify sources of data 1 Day-to-day operations based on real-time analytics Business Executives Empowered Employees Make Decisions and Take Action 3Data Scientists Business Analysts Analyze data2
• 15 . What is Big Data? Big Data Use Cases IBM Analytics Platform IBM Spectrum Scale Agenda
• 16 . 16 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Practical Use Cases – The Analytics Landscape Degree of Complexity CompetitiveAdvantage Standard Reporting Ad hoc reporting Query/drill down Alerts Simulation Forecasting Predictive modeling Optimization What exactly is the problem? What will happen next if ? What if these trends continue? What could happen…. ? What actions are needed? How many, how often, where? What happened? Stochastic Optimization Based on: Competing on Analytics, Davenport and Harris, 2007 Descriptive Prescriptive Predictive How can we achieve the best outcome? How can we achieve the best outcome including the effects of variability?
• 17 . 17 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Innovate New Products and Services at Speed and Scale Vestas, the world’s largest wind energy company, was able to use big data and IBM technology to increase wind power generation through optimal turbine placement. Reducing the time to analyze petabytes of data with IBM Big Insights software and IBM Spectrum Scale “Before, it could take us three weeks to get a response to some of our questions simply because we had to process a lot of data. We expect that we can get answers for the same questions now in 15 minutes.” – Lars Christian Christensen
• 18 . 18 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. If You are Not Paying for it… Then you are not the Customer, … You are the Product Being Sold! How much is each user worth to Social Media companies? Sources: Geek & Poke comic, “Let’s Talk about Data” by Neha Mehta
• 19 . 19 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Social Network Public Database How valuable is Amy to my retail sales? Who does she influence? What do they spend? Retailer Amy Bearn 32, Married, mother of 3, Accountant Telco Score: 91 CPG Score: 76 Fashion Score: 88 Telco company How valuable is Amy to my mobile phone network? How likely is she to switch carriers? How many other customers will follow Merged Network Calling Network 360 Degree View of the Customer – A Demographic of One
• 20 . 20 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Deep Individual Customer Insight • Preferences • Interests • Likes Run Zero-Latency Operations Direct Channel Workflow Enrich Initiate Direct Response Initiate Channel Response Initiate Process or Workflow Enrich Customer Profile Real-time Decision
• 21 . 21 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. How Target® Figured Out a Teen Girl Was Pregnant Before Her Father Did Every time you go shopping, you share intimate details about your consumption patterns with retailers. Target has figured out how to data-mine whether you have a baby on the way Looked at historical buying data for all the ladies who had signed up for Target baby registries – Unscented soaps and lotions – Calcium, magnesium and zinc supplements About 25 products help generate “pregnancy prediction” score and her “baby due date” Target sends coupons timed to very specific stages of her pregnancy Source: http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/ “My daughter got this in the mail. She’s still in high school, and you’re sending her coupons for baby clothes and cribs?” -- Angry father of teen girl “I had a talk with my daughter,…She’s due in August. I owe you an apology.” -- Same father, 3 days later
• 22 . 22 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Exploit Instrumented Assets Doctors from University of Ontario apply big data to neonatal infant monitoring to predict infection Detect Neonatal Patient Symptoms Up to 24 Hours sooner Continuously correlate data Thousands of events each second Signal Processing and Data Cleansing Heart Rate Variability
• 23 . What is Big Data? Big Data Use Cases IBM Analytics Platform IBM Spectrum Scale Agenda
• 24 . 24 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. The IBM big data platform advantage BI / Reporting BI / Reporting Exploration / Visualization Functional App Industry App Predictive Analytics Content Analytics Analytic Applications IBM big data platform Systems Management Application Development Visualization & Discovery Accelerators Information Integration & Governance Hadoop System Stream Computing Data Warehouse • The platform provides benefit as you move from an entry point to a second and third project • Shared components and integration between systems lowers deployment costs • Key points of leverage • Reuse text analytics across streams and BigInsights • Hadoop connectors between Streams and Information Integration • Common integration, metadata and governance across all engines • Accelerators built across multiple engines – common analytics, models, and visualization
• 25 . 25 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Simplify your data warehouse Customer Need – Business users are hampered by the poor performance of analytics of a general-purpose enterprise warehouse – queries take hours to run – Enterprise data warehouse is encumbered by too much data for too many purposes – Need to ingest huge volumes of structured data and run multiple concurrent deep analytic queries against it – IT needs to reduce the cost of maintaining the data warehouse Value Statement – Speed and Simplicity for deep analytics – 100s to 1000s users/second for operation analytics Customer examples – Catalina Marketing – executing 10x the amount of predictive workloads with the same staff System for Transactions System for Analytics System for Operational Analytics Get started with IBM PureData Systems!
• 26 . 26 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Ad-Hoc versus Operational Analytics
• 27 . 27 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Analyze streaming data in Real time Customer Need – Harness and process streaming data sources – Select valuable data and insights to be stored for further processing – Quickly process and analyze perishable data, and take timely action Value Statement – Significantly reduced processing time and cost – process and then store what’s valuable – React in real-time to capture opportunities before they expire Customer examples – Ufone – Telco Call Detail Record (CDR) analytics for customer churn prevention Get started with IBM Streams! Visualization Streams Runtime Deployments Sync Adapters Analytic Operators Source Adapters Automated and Optimized Deployment Streaming Data Sources Streams Studio IDE
• 28 . 28 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Dominant Players vs. Contender platforms OS Tape Cloud Management Big Data & Analytics Dominant Player Microsoft Windows Quantum DLT Amazon Web Services Cloudera Contender platform Linux Linear Tape Open (LTO) OpenStack Open Data Platform Supporters of Contender platform IBM, RedHat, SUSE, Oracle and others IBM, HP, Certance and others IBM, HP, Rackspace, RedHat, Dell, Cisco, VMware and others IBM, Pivotal, Hortonworks and others
• 29 . 29 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. IBM InfoSphere BigInsights is a 100% standard Hadoop distribution By default, open source components are always deployed Elect to use proprietary capabilities depending on your needs In some cases, proprietary capabilities offer significant benefits Open standards first, but with freedom of choice HDFS YARN HIVE MapReduce PIG Spectrum Scale Platform Symphony Big SQL Adaptive MapReduce BigSheets Share data with non-Hadoop applications and simplify data management Re-use existing tools and expertise, Avoid additional development costs Boost performance, support time-critical workloads, do more with less True multi-tenancy to boost service levels and avoid duplication on infrastructure Simplify access for end-users, minimize software development
• 30 . 30 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Text Analytics Spectrum Scale Platform Symphony IBM BigInsights Enterprise Management System ML on Big R Distributed R IBM Open Platform with Apache Hadoop IBM BigInsights Data Scientist IBM BigInsights Analyst Big SQL Big Sheets Big SQL BigSheets IBM BigInsights for Apache Hadoop IBM BigInsights for Apache Hadoop Three new user-centric modules founded on an Open Data Platform IBM Open Platform with Apache Hadoop is IBM’s own 100% open source Apache Hadoop distribution. IBM will include the ODP common kernel when available. Business Analyst Data Scientist Administrator
• 31 . 31 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Platform Symphony Integrates with Hadoop YARN uses a pluggable architecture for schedulers. – FIFO, Fair, and Capacity Schedulers implemented this way – Symphony EGO is also implemented this way. Therefore, scheduler is completely transparent to YARN Applications. ISV Certification for Platform Symphony is not required. YARN (open source) Fair Capacity Symphony EGO FIFO Like other schedulers, queues and policies are defined in Platform Symphony EGO. App1 App2 App3
• 32 . 32 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Spark, a Complement to Hadoop 32 • Spark - complement Hadoop, not replace • Provides distributed memory abstractions for clusters to support applications that repeatedly use a working set of data, • Iterative algorithms (machine learning), • Interactive data mining tools (R, Python, ..) • Spark Programming Model – Resilient Distributed Datasets (RDDs) • Immutable collections partitioned across cluster that can be rebuilt if a partition is lost • Created by transforming data in stable storage using data flow operators (map, filter, group-by, …) • Can be cached across parallel operations • Spark uses HDFS or IBM Spectrum Scale • Can use any Hadoop data source • Use Hadoop InputFormats and OutputFormats • Spark runs on YARN • Can run on the same cluster with MapReduce • Spark works with Hadoop ecosystem • Flume, Sqoop, HBase • Spark architectural considerations • Keep dataset in memory • Spark programs can be bottlenecked by any resource in cluster: CPU, network bandwidth, memory. Most often, if data fits in memory, the bottleneck is network bandwidth. HDFS or IBM Spectrum Scale YARN
• 33 . 33 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. IBM InfoSphere BigInsights – Big SQL Native Hadoop Data Sources CSV SEQ Parquet RC AVRO ORC JSON Custom Optimized SQL MPP Run-time Big SQL SQL based Application IBM’s SQL for Hadoop • Makes Hadoop data accessible to a wider audience • Familiar, widely known syntax • Leverage native Hadoop data sources Complements the Data Warehouse • Exploratory analytics • Sandbox, Data Lake Included in IBM BigInsights Use familiar SQL tools • Cognos, SPSS, Tableau, MicroStrategy
• 34 . 34 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Information Ingestion and Operational Information Decision Management BI and Predictive Analytics Navigation and Discovery Intelligence Analysis Landing Area, Analytics Zone and Archive Raw Data Structured Data Text Analytics Data Mining Entity Analytics Machine Learning Real-time Analytics Video/Audio Network/Sensor Entity Analytics Predictive Exploration, Integrated Warehouse, and Mart Zones Discovery Deep Reflection Operational Predictive Stream Processing Data Integration Master Data Streams Information Governance, Security and Business Continuity Architecture Pattern for big data Implementation Application Transaction Machine data Social media, email Enterprise content Data at Rest
• 35 . What is Big Data? Big Data Use Cases IBM Analytics Platform IBM Spectrum Scale Agenda
• 36 . 36 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Why use IBM Spectrum Scale™ Extreme Scalability Add or Remove nodes and storage, without disruption or performance impact to applications Universal Access to Data All servers and clients have access to data through a variety of file and object protocols High Performance Parallel access with no hot spots Proven Reliability Used by over 200 of the top 500 Supercomputers Survive any node or storage failure with Distributed RAID and redundant components
• 37 . 37 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Hadoop Analytics – HDFS vs IBM Spectrum Scale™ HDFS Save Results Discard Rest IBM Hadoop Connector allows Map/Reduce programs to process data without application changes IBM Spectrum Scale Application data stored on IBM Spectrum Scale is readily available for analytics Save Results JFS2 NTFS EXT4 Data Sources mashup of structured and unstructured data from a variety of sources Actionable Insights Provides answers to the Who, What, Where, When, Why and How Business Intelligence & Predictive Analytics > Competitive Advantages > New Threats and Fraud > Changing Needs and Forecasting > And More!
• 38 . 38 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Hadoop HDFS HDFS NameNode HA added in version 2.0. NameNode HA in active/passive configuration Difficulty to ingest data – special tools required Lacking enterprise readiness No single point of failure, distributed metadata in active/active configuration since 1998 Ingest data using policies for data placement Versatile, Multi-purpose, Hybrid Storage (locality and shared) Enterprise ready with support for advanced storage features (Encryption, DR, replication, SW RAID etc) Large block-sizes – poor support for small files Variable block sizes – suited to multiple types of data and metadata access pattern Scale compute and storage independently (Policy based ILM) Compute and Storage tightly coupled – leading to very low CPU utilization Single-purpose, Hadoop MapReduce only POSIX file system – easy to use and manage Non-POSIX file system – obscure commands. Does not support in-place updates. IBM Spectrum Scale HDFS versus IBM Spectrum Scale™
• 39 . 39 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. HDFS Namenode Secondary Namenode IBM Spectrum Scale™ – File Placement Optimization SAN Internal, Direct-Attach TCP/IP or RDMA Network • Spectrum Scale avoids the need for a central namenode, a common failure point in HDFS • Avoid long recovery times in the event of namenode failure • Spectrum Scale can intermix FPO with standard NSD server and client nodes in the same cluster • POSIX compliance which is key to avoid data islands. • Robustness and performance at massive scale and maturity File Placement Optimization (FPO) Creates a “shared nothing” cluster similar to HDFS in Hadoop environments
• 40 . 40 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Share-Nothing versus Shared-Disk Deployments Data Data Data Parity Data Data Data Copy Copy Copy Copy Copy Copy TCP/IP or RDMA Need more compute? Add another node! Spectrum Scale and Elastic Storage Server reduce storage to one RAID-protected copy of the data Scale compute and storage capacity separately Spectrum Scale FPO can keep 1,2 or 3 replicas of the data Need more storage capacity? Add another node! 3x versus 1.3x TCP/IP or RDMA
• 41 . 41 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. IBM Spectrum Scale™ – Software, Systems or Cloud Services Software • Install software on your own choice of Industry standard x86 or POWER servers Pre-built Systems • Elastic Storage Server with distributed RAID • Storwize V7000 Unified Cloud Services • Spectrum Scale can be deployed on any Cloud Scale
• 42 . 42 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Session summary Big data is being generated by everything around us – Every digital process and social media exchange produces it – Systems, sensors and mobile devices transmit it Big data is arriving from multiple sources at amazing velocities, volumes and varieties To extract meaningful value from big data, you need optimal processing power, storage, analytics capabilities, and skills Sources: The Economist, and special thanks to Dr. Bob Sutor, IBM VP, Business Solutions & Mathematical Sciences
• 43 . 43 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Session Evaluations YOUR OPINION MATTERS! Submit four or more session evaluations by 5:30pm Wednesday to be eligible for drawings! *Winners will be notified Thursday morning. Prizes must be picked up at registration desk, during operating hours, by the conclusion of the event. 1 2 3 4
• 44 . 44 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM.
• 45 . 45 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Big Data & Analytics Building Big Data and Analytics Solutions in the Cloud http://www.redbooks.ibm.com/abstracts/redp5085.html?Open o IBM BigInsights o IBM PureData System for Hadoop o IBM PureData System for Analytics o IBM PureData System for Operational Analytics o IBM InfoSphere Warehouse o IBM Streams o IBM InfoSphere Data Explorer (Watson Explorer) o IBM InfoSphere Data Architect o IBM InfoSphere Information Analyzer o IBM InfoSphere Information Server o IBM InfoSphere Information Server for Data Quality o IBM InfoSphere Master Data Management Family o IBM InfoSphere Optim Family o IBM InfoSphere Guardium Family “Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models.”
• 46 . 46 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Research Paper “In this paper, we revisit the debate on the need of a new non-POSIX storage stack for cloud analytics and argue, based on an initial evaluation, that it can be built on traditional POSIX-based cluster filesystems.“
• 47 . 47 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Hadoop for the Enterprise http://www.ibm.com/software/data/infosphere/hadoop/enterprise.html IBM BigInsights for Apache Hadoop provides a 100% open source platform and offers analytic and enterprise capabilities for Hadoop.
• 48 . 48 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. IBM Tucson Executive Briefing Center Tucson, Arizona is home for storage hardware and software design and development IBM Tucson Executive Briefing Center offers: –Technology briefings –Product demonstrations –Solution workshops Take a video tour! – http://youtu.be/CXrpoCZAazg
• 49 . 49 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. About the Speaker Tony Pearson is a Master Inventor and Senior managing consultant for the IBM System Storage™ product line. Tony joined IBM Corporation in 1986 in Tucson, Arizona, USA, and has lived there ever since. In his current role, Tony presents briefings on storage topics covering the entire System Storage product line, Tivoli storage software products, and topics related to Cloud Computing. He interacts with clients, speaks at conferences and events, and leads client workshops to help clients with strategic planning for IBM’s integrated set of storage management software, hardware, and virtualization products. Tony writes the “Inside System Storage” blog, which is read by hundreds of clients, IBM sales reps and IBM Business Partners every week. This blog was rated one of the top 10 blogs for the IT storage industry by “Networking World” magazine, and #1 most read IBM blog on IBM’s developerWorks. The blog has been published in series of books, Inside System Storage: Volume I through V. Over the past years, Tony has worked in development, marketing and customer care positions for various storage hardware and software products. Tony has a Bachelor of Science degree in Software Engineering, and a Master of Science degree in Electrical Engineering, both from the University of Arizona. Tony holds 19 IBM patents for inventions on storage hardware and software products. 9000 S. Rita Road Bldg 9032 Floor 1 Tucson, AZ 85744 +1 520-799-4309 (Office) [email protected] Tony Pearson Master Inventor, Senior IT Specialist IBM System Storage™
• 50 . 50 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Email: [email protected] Twitter: twitter.com/az99Øtony Blog: ibm.co/Pearson Books: www.lulu.com/spotlight/99Ø_tony IBM Expert Network on Slideshare: www.slideshare.net/az99Øtony Facebook: www.facebook.com/tony.pearson.16121 Linkedin: www.linkedin.com/profile/view?id=103718598 Additional Resources from Tony Pearson
• 51 . 51 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Continue growing your IBM skills ibm.com/training provides a comprehensive portfolio of skills and career accelerators that are designed to meet all your training needs. If you can’t find the training that is right for you with our Global Training Providers, we can help. Contact IBM Training at [email protected] Global Skills Initiative
• 52 . 52 IBM Systems Technical University, October 5-9 | Hilton Orlando © Copyright IBM Corporation 2015. Technical University/Symposia materials may not be reproduced in whole or in part without the prior written permission of IBM. Trademarks and Disclaimers Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. IT Infrastructure Library is a registered trademark of the Central Computer and Telecommunications Agency which is now part of the Office of Government Commerce. Intel, Intel logo, Intel Inside, Intel Inside logo, Intel Centrino, Intel Centrino logo, Celeron, Intel Xeon, Intel SpeedStep, Itanium, and Pentium are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries. Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both. Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both. ITIL is a registered trademark, and a registered community trademark of the Office of Government Commerce, and is registered in the U.S. Patent and Trademark Office. UNIX is a registered trademark of The Open Group in the United States and other countries. Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates. Cell Broadband Engine is a trademark of Sony Computer Entertainment, Inc. in the United States, other countries, or both and is used under license therefrom. Linear Tape-Open, LTO, the LTO Logo, Ultrium, and the Ultrium logo are trademarks of HP, IBM Corp. and Quantum in the U.S. and other countries. Other product and service names might be trademarks of IBM or other companies. Information is provided "AS IS" without warranty of any kind. The customer examples described are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual environmental costs and performance characteristics may vary by customer. Information concerning non-IBM products was obtained from a supplier of these products, published announcement material, or other publicly available sources and does not constitute an endorsement of such products by IBM. Sources for non-IBM list prices and performance numbers are taken from publicly available information, including vendor announcements and vendor worldwide homepages. IBM has not tested these products and cannot confirm the accuracy of performance, capability, or any other claims related to non-IBM products. Questions on the capability of non-IBM products should be addressed to the supplier of those products. All statements regarding IBM future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only. Some information addresses anticipated future capabilities. Such information is not intended as a definitive statement of a commitment to specific levels of performance, function or delivery schedules with respect to any future products. Such commitments are only made in IBM product announcements. The information is presented here to communicate IBM's current investment and development activities as a good faith effort to help with our customers' future planning. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon considerations such as the amount of multiprogramming in the user's job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve throughput or performance improvements equivalent to the ratios stated here. Prices are suggested U.S. list prices and are subject to change without notice. Starting price may not include a hard drive, operating system or other features. Contact your IBM representative or Business Partner for the most current pricing in your geography. Photographs shown may be engineering prototypes. Changes may be incorporated in production models. © IBM Corporation 2015. All rights reserved. References in this document to IBM products or services do not imply that IBM intends to make them available in every country. Trademarks of International Business Machines Corporation in the United States, other countries, or both can be found on the World Wide Web at http://www.ibm.com/legal/copytrade.shtml. ZSP03490-USEN-00

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IBM SPSS Data Analytics, Case Studies in C#, Java and Electric Cars

Harvard University, Cranfield University, Thunderbird University and many other MBA programs continue to employ Case Studies in research. It is also a good practice to use Case Studies for undergraduate degree programs. For PhD candidates, it is mandatory that they do quantitative or qualitative research using real world Case Studies. Big Data, C#, Project Risk Management courses were offered for professionals at IEEE. Java Programming was given at New Jersey Institute Technology, with students pursuing a Master Degree in Computer Science. At Dominican College, Global Marketing course was taught to undergraduate students. They did the final projects on Electric Cars. At University of Phoenix (UOPX), this author mentors 16 online Doctoral Candidates. They learn and use the IBM SPSS software for in-depth quantitative data analysis. Doing Case Studies, for undergraduates, Master degrees, PhDs, provided a sound foundation for critical thinking, leadership, and team building skills. Student reviews were good to excellent. This paper gives the summary.

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The profession as an electrical engineer has undergone many changes in the last 20 years. Many of the design jobs have been outsourced, downsized or eliminated. While there is an oversupply of one type of engineer, there is a significant shortage of another type of engineer. Jobs in New York area represent the pulses of the engineering profession. Defense contractors have moved out of the area but microwave engineers are still in demand. Programmers in Java and C# .NET are especially in acute shortages. In the banking, construction, finance and healthcare industry, Project Manager is the hottest job title. For the displaced electrical engineers, several options remain. First one is to continue sending resume to the design manufacturing firm and trying to get work. Second one is to start his/her own design engineering firm. Then the problem is marketing. Most engineers dreaded marketing. This new design company is normally short-lived. The third choice is to take courses to get new skills that the modern society needs. To take a course in Java, C# or Project Management, the cost is $1,200 at a two-year college or $2,500 at a professional training organization. Since 1993, IEEE North Jersey Section provided 16 low-cost courses (specifically on C Programming, C++ Programming, Java Programming, Advanced Java Programming, Project Management, Marketing Research, and C# .NET Programming), training a total 220 engineers/professionals by this author. The collaboration between academia, industry and private firms make it possible to achieve the retraining goals. It generated an inventory of the effective activities, developed the action plan and delivered the end results. The bottom line: successfully retrained engineers to work as Java Programmer, C# Programmer, Project Manager or related title. Some of them obtained jobs as Math Teachers in high schools or teaching programming courses in colleges/universities. IEEE North Jersey Section, a volunteer organization, also benefited from the financial gains $50,000 running these courses. The money was used to cover many IEEE North Jersey Section activities. The same program may apply to German engineers, since many of the German firms are also outsourcing and downsizing their engineering workforce. Therefore this paper provides a roadmap for international professional societies to retrain their engineers to fit the needs of the modern world.

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