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[Webinar] Getting to Insights Faster: A Framework for Agile Big Data

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In this slidedeck, Infochimps Director of Product, Tim Gasper, discusses how Infochimps tackles business problems for customers by deploying a comprehensive Big Data infrastructure in days; sometimes in just hours. Tim unlocks how Infochimps is now taking that same aggressive approach to deliver faster time to value by helping customers develop analytic applications with impeccable speed.

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[Webinar] Getting to Insights Faster: A Framework for Agile Big Data

  1. 1. Getting to Insights Faster: A Framework for Agile Big Data @TimGasper Director of Product Infochimps, a CSC Big Data Business
  2. 2. Agenda (1) IT’S ALL ABOUT THE APP (2) WHAT IS A BIG DATA APP (3) TRADITIONAL VS AGILE APPROACH (4) ENABLERS OF AGILE BIG DATA (5) DEMONSTRATION
  3. 3. What problem are you trying to solve?
  4. 4. It’s all about the apps.
  5. 5. Poll Question 1
  6. 6. What is a Big Data app? ? + Critical Business Problems = Impactful Analytic Applications
  7. 7. Smart Meter Monitoring for Customer Value Add Predictive Inventory Levels to Minimize Warehousing Costs Personalized Medicine Treatment Programs Trade Options and Futures Pricing Platform Source: PARC Customer Churn Analysis for Increased Customer Lifetime Value
  8. 8. Poll Question 2
  9. 9. It’s all about the apps.
  10. 10. Source: Tableau
  11. 11. Predictive Manufacturing + Smart Manufacturing & Energy Ad Publisher Campaign Analytics 360 Customer Experience Management Social Media Monitoring & Analytics
  12. 12. The Traditional Way Business Discovery Info Discovery Logical Data Model Physical Data Model System Staging Data Ingestion, Transformation, ETL Application Development Analytics Data Warehouse Project 12-24 Months to Reach Production Production Staging
  13. 13. Big Data: A New Hope Business Discovery Info Discovery Logical Data Model Physical Data Model System Staging Data Ingestion, Transformation, ETL Application Development Production Staging Analytics Data Warehouse Project 12-24 Months to Reach Production App Dev Business Discovery Info Discovery Sys. Stag. Initial Data Ingest Analytics Schema on Read App Dev Prod. Stag. App Dev App Dev App Dev Analytics Analytics Analytics Analytics Schema on Read Schema on Read Schema on Read Schema on Read Big Data Project 3-6 Months to Reach Production
  14. 14. Application Development Timelines 6 2 Developers Months 5 2 Developers Months 3 1 Developer Months 4 2 Developers Months
  15. 15. Speed to Value: A Case Study HGST, a Western Digital company, is improving customer support and product quality by collecting, analyzing, and acting on massive quantities of machine and sensor data.  Greatly diminished operational burden with ability to focus on analysis and driving business action  Fast project delivery and success  Expertise with Big Data technologies like Hadoop KEY STATS Industry Storage Technology Solution Machine Data Analysis Engine Channel B2B Cloud Services Cloud::Queries Cloud::Hadoop Users Application Developers, Data Scientists, Analysts Deployment Amazon Web Services
  16. 16. Poll Question 3
  17. 17. Enablers of Agile Big Data 1. ​Managed infrastructure means focusing on Big Data apps 2. The community tech itself and what it enables 3. ​Our customer engagement framework for choosing use cases that have impact and designing successful solutions 1. ​Agile, iterative analytics app dev lifecycle 1. ​Our application reference design framework for kick starting application development
  18. 18. A Managed Platform
  19. 19. Technologies Under the Hood PART 1 HADOOP ​• Java ​MapReduce ​• Streaming MapReduce ​• SQL on Hadoop, Pig, Hive ​NOSQL DATABASES ​• ​ HBase/Accumulo ​• ​ Elasticsearch ​• ​ Cassandra, MongoDB ​STREAM PROCESSING, MESSAGE QUEUES ​• Storm ​• Kafka
  20. 20. Technologies Under the Hood PART 2 HADOOP INTERFACES ​• Hue ​• Command Line ​STATISTICAL TOOLS • R, SAS, SPSS ​BUSINESS INTELLIGENCE AND DATA VIZ • Legacy: Cognos, Biz Objects, OBIEE, Microsoft BI • New Gen: Tableau, Qlikview, SiSense, Kibana
  21. 21. Our Unique Toolset Addition SaaS Develop & Test Locally with App/Analytics Scripting & “Deploy Pack” Orchestration PaaS Real-time Analytics With Cloud::Streams Interactive Analytics With Cloud::Queries Batch Analytics With Cloud::Hadoop Abstract to any cloud with Orchestration DSL IaaS Public Cloud Virtual Private Cloud Private Cloud
  22. 22. Customer Engagement Framework Service Requirements Week 1-2 Discovery Design & Build Week 3-4 Technical Design Production Ongoing Iterative App Development Week 5-8+ Platform Rollout Build Data Flows Interview Key Business Stakeholders Define Business Benefits Design Data Flows Interview Key Technical Stakeholders Define Target Use Case Define Architecture Define Objectives & Challenges Develop HighLevel Approach & Costs Identify Data Sources Agree to Project Plan/Rollout Real-Time Data Flow Architecture Validation Standup / Connect Environment Tuning Solution Historical Data MAJOR ACTIVITIES • Run 2-4 hour Design Thinking Workshop • Review current state metrics • Review business pain points & opportunities • Review application & infrastructure environment • Define target use case • Identify data sources for target use case • Develop high level tech approach and costs • Define high level benefits • Develop initial case for action • Develop go forward plan • Develop Data Model • Technical architecture & integration design • Stand up environment • Dashboard design workshops • Data mapping • Build prototype dashboard • Configure prototype application • Data load • Run solution iterations • Analytical modeling
  23. 23. Agile Iteration for App Dev ::
  24. 24. App Reference Design Framework • A use-case-driven reference design • A code repository with: o o o o Domain-specific sample data sets/sources Sample data flows Sample data processors/analytics Simple data visualization
  25. 25. App Reference Designs Predictive Manufacturing + Smart Manufacturing & Energy Ad Publisher Campaign Analytics 360 Customer Experience Management Social Media Monitoring & Analytics
  26. 26. Social Media App Reference Design
  27. 27. Demonstration
  28. 28. Big Data Benefits ENABLED BY • ​Unstructured data and semi-structured data allow for faster path to data integration • ​Real-time analysis and batch analysis with scripting tools • ​Schema on read for app-driven data models and data structures • ​Local to cloud, small data to big data… tools can talk to each other​ New Use Cases New Analytics and Analytical Techniques More Data Time to Value Faster Iteration Faster Data Increased Flexibility
  29. 29. What is Your First Big Data App?
  30. 30. Learn More » sales@infochimps.com 1-855-328-2386 Request a Demo: http://infochimps.com/demo Q&A

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