Disruptive Analytics - Dinsmore, Thomas W.; Vagner, Oliver
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Learn all you need to know about seven key innovations disrupting business analytics today. These innovations-the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics-are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities. Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business…mehr

Produktbeschreibung
Learn all you need to know about seven key innovations disrupting business analytics today. These innovations-the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics-are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities. Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization. Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today. What You'll Learn Discover how the open source business model works and how to make it work for you See how cloud computing completely changes the economics of analytics Harness the power of Hadoop and its ecosystem Find out why Apache Spark is everywhere Discover the potential of streaming and real-time analytics Learn what Deep Learning can do and why it matters See how self-service analytics can change the way organizations do business Who This Book Is For Corporate actors at all levels of responsibility for analytics: analysts, CIOs, CTOs, strategic decision makers, managers, systems architects, technical marketers, product developers, IT personnel, and consultants.
  • Produktdetails
  • Verlag: Springer, Berlin; Apress
  • Artikelnr. des Verlages: 978-1-4842-1312-4
  • 1st ed.
  • Erscheinungstermin: 28. August 2016
  • Englisch
  • Abmessung: 236mm x 154mm x 20mm
  • Gewicht: 454g
  • ISBN-13: 9781484213124
  • ISBN-10: 1484213122
  • Artikelnr.: 43380106
Autorenporträt
Thomas W. Dinsmore is Knowledge Expert in Customer Analytics at The Boston Consulting Group. He previously served as Director of Product Management for Revolution Analytics; Analytics Solution Architect for IBM Big Data Solutions; and Principal Consultant for SAS Professional Services. Dinsmore has more than twenty-five years of experience in predictive analytics. He led or contributed to analytic solutions for more than five hundred clients across vertical markets-including AT&T, Banco Santander, Citibank, Dell, J. C. Penney, Monsanto, Morgan Stanley, Office Depot, Sony, Staples, United Health Group, UBS, and Vodafone-and around the world-including the United States, Puerto Rico, Canada, Mexico, Venezuela, Brazil, Chile, the United Kingdom, Belgium, Spain, Italy, Turkey, Israel, Malaysia, and Singapore. Although his roots are in hands-on customer analytics, in the past fifteen years Dinsmore has expanded the scope of his experience to include analytic software applications and broader solutions including database integration and web applications. As a project lead, he has worked with DB2, Oracle, Netezza, SQL Server, and Teradata. Dinsmore is certified in SAS 9 and has working experience with the Hadoop ecosystem and the leading analytic tools in the market today, including SAS, R, SPSS, and Oracle Data Mining. Dinsmore is the author of Modern Analytics Methodologies (FT Press, 2014) and Advanced Analytics Methodologies (FT Press, 2014) and runs The Big Analytics Blog. He holds his MBA from the Wharton School, The University of Pennsylvania, and his bachelor's from Boston University.
Inhaltsangabe
Chapter 1. The Engines of Disruptiono Business Models§ Open Source§ Cloudo Technology§ Cheap Hardware: Memory, Storage, and Chips§ Distributed Computing§ MapReduce/HDFS§ Resilient Distributed Datasets§ VizQLPART I. Innovations in Business AnalyticsChapter 2. The Analytic Database Matureso MPP Databaseso Columnar Databaseso In-Memory Databaseso The Data Warehouse ApplianceChapter 3. The Hadoop Ecosystemo Hadoop 1.0o Hadoop 2.0o NoSQLo SQL on HadoopChapter 4. Open Source Analyticso Analytic Languages§ R§ Julia§ Python§ Scalao Analytic Platforms§ Mahout§ MADLib§ H2O§ SparkChapter 5. Data in the Cloudo Google for the Common Mano Evolution of Data in the Cloudo Cloud Platforms§ AWS§ Microsoft Azure§ Qubole§ Google Cloud§ IBM Cloudo Analytics in the Cloud§ AWS Marketplace§ Amazon Machine Learning§ Azure Machine Learning§ Google BigQuery§ IBM Watson§ StartupsChapter 6. Real-Time and Streaming Engineso Preparing for IoTo Stream vs. Batcho Stream Pipelineso Open Source Projects§ Kafka§ Storm§ Spark Streaming§ Flume§ Millwheelo Commercial Products§ IBM InfoSphere Streams§ AWS KinesisChapter 7. The New Machine Learningo Ensemble Modelso Neural Networks and Deep Learningo Bayesian Techniqueso Graph Analyticso Topic Modelingo Topological Data Analysiso Automated LearningChapter 8. Disrupting the Last Mileo Tableau and Qliko BI on Hadoopo Prediction for the Business UserPART II. Harnessing the New TechnologiesChapter 9. Beyond GIGO: Developing a Data Strategyo What Is a Data Strategy?o Data and Metadata Managemento Keeping It Clean UpstreamChapter 10. Data Lakes, Reservoirs, and Swampso The Hybrid/Extended Data Warehouse in Practiceo Avoiding the Swampo Evolving into the Application CloudChapter 11. Architecting for the Cloudo When the Cloud Makes Senseo Locality, Elasticity, Agility, and Scaleo Data SecurityChapter 12. Lambda Architectureo Putting It All Together: Batch + Stream = Real-Time Analyticso Design Patternso Pros, Cons, and AlternativesChapter 13. Agile Analytics: Data to Insighto Accelerating Time to Valueo Minimum Viable Modelo Leveraging Kaizen Continual Improvement ProcessesChapter 14. Disruptive Analytics at Work: Case StudiesChapter 15. Predictions for Predictive Analytics