Top 5 Challenges to Overcome for Big Data Success
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Top 5 Challenges to Overcome for Big Data Success

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This presentation is about the why and the how of Big Data inititiatives ; it presents customer case studies of Big Data journeys from Sandbox to Analytics, and then to actionable and real time ...

This presentation is about the why and the how of Big Data inititiatives ; it presents customer case studies of Big Data journeys from Sandbox to Analytics, and then to actionable and real time operations.
It goes accross the 5 execution challenges: obtaining skills, sourcing the data, setting the infrastructure, governing the data and funding the projects.

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Top 5 Challenges to Overcome for Big Data Success Presentation Transcript

  • 1. © Talend 2014 1 Top 5 Challenges to Overcome for Big Data Success Webinar | June 2014
  • 2. © Talend 2014 2 ONLINE REPLAY http://www.talend.com/resources/webinars/overcome-the-top-5-challenges-to-big- data-success
  • 3. © Talend 2014 3 Welcome A few logistical points.. • All participants are muted • You may ask questions using the Q&A panel located on bottom or GoToWebinar applet • Answers will be provided after the presentation • If time is too short to address all questions, answers will be provided via email • To receive a replay of our webinar today, please send us an email to webinar@talend.com • If you are experiencing connection problems, please use the Q&A panel to communicate
  • 4. © Talend 2014 4 Top 5 Challenges to Overcome for Big Data Success Webinar | June 2014
  • 5. © Talend 2014 5 Your presenters Jean-Michel Franco Director, Product Marketing Philip Crocker Director, Partner Marketing
  • 6. © Talend 2014 6 The Talend Platform
  • 7. © Talend 2014 7 Meeting Customer Big Data Needs Top Ranked Exponential Growth 500+ Customers Cloud Leaders 3X bookings Q1 ‘13 – Q1 ‘14 80% of accounts expand 3X 90% software licenses <1% lifetime churn >$1B in incremental revenue generated by 1 customer John Wallace, CEO marketing analytics company DataSong, is “Married to big data, but started dating her before she was popular. Now she’s very popular! We moved to MapR two years ago because of the investment they made in Hadoop. MapR is our data store for everything. It is our data processing and analytical engine, the main file system, the utility, the hub of everything” http://data-informed.com/datasongs-hadoop-bet paying/#sthash.AVZYD59l.WgOskAxl.dpuf
  • 8. © Talend 2014 8 Agenda Big Data from Sandbox to Analytics and Real Time Business 5 challenges to Big Data success Meet your 5 Challenges with MapR and Talend Jump Start your Big Data journey with the MapR/Talend Sandbox & Talend’s Readiness Scorecard Agenda
  • 9. © Talend 2014 10 Driving your Big Data Journey Sandbox Analytics Real Time Operations Value Break Even PointPlanning Stage Project is Justified Real Time Operations Begin Business Is Transformed Talend Platform for Big Data
  • 10. © Talend 2014 11 From clickstream to customer analytics and to real time recommendations Case study in retail Sandbox Analytics Real Time Operations Value Planning Stage From Clickstream… …to Customer Analytics … …to Real-Time Recommendations
  • 11. © Talend 2014 15 Operations + Analytics = Real-time, Personalized Services Fraud model Recommendations table MapR Distribution for Hadoop Fraud investigator Interactive marketer Online transactions Fraud detection Personalized offers Clickstream analysis Fraud investigation tool Real-time Operational Applications Analytics
  • 12. © Talend 2014 16 Zions Bank: Fraud Detection Cost effective security analytics and fraud detection on one platform • Prevent Fraud: Global bank fraud costs $200B annually • Operationalize Big Data Fraud Detection: Fraud Operations and Security Analytics team at Zions maintains data stores, builds statistical models to detect fraud, and then uses these models to data mine and evaluate suspicious activity “We initially got into centralizing all of our data from an information security perspective. We then saw that we could use this same environment to help with fraud detection” Michael Fowkes - SVP Fraud Operations and Security Analytics, Zions Bank • Existing technology infrastructure could not scale • Timeliness of reports degraded over the last several years • Chose MapR and cut storage costs by 50% • Gained huge performance advantage – Querying time reduced from 24 hours to 30 min on 1.2 PB of data • Leverage MapR scale for increased model accuracy and deeper insights BUSINESS OBJECTIVES CHALLENGES MAPR ADVANTAGE
  • 13. © Talend 2014 18 … but then it is all about execution Your business case is the prerequisite… Source: Gartner - Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype - 12 September 2013 - G00255160 Together, MapR and Talend Directly Address these Challenges
  • 14. © Talend 2014 20 The Skills Challenges • Use existing skills • Time to deliver • Future proof “Above all, this demands flexibility… …Consider using a metadata-driven codeless development environment to increase productivity and help insulate you from underlying technology changes“ Ralph Kimball – Newly emerging best practices for Big Data
  • 15. © Talend 2014 21 The Infrastructure Challenge • Comprehensive platform • Integrates existing systems • Ready for business-critical production applications
  • 16. © Talend 2014 22 The Data Sources Challenge • IT generated data • Machine generated data • User generated and external data “Big data will change the time and cost equation for all data applications. It is about reducing the need for costly upfront data preparation and data engineering, which typically constitute 80% of the time and cost of data managemen“ Randy Bean – It may be everywhere now, but big data matters more than ever.
  • 17. © Talend 2014 23 The Data Governance Challenge • Experiment your data • Certify your data • Ensure fair usage of data and related inferences
  • 18. © Talend 2014 24 The Funding Challenge • Predictable costs • Ready for the “information long tail” • Minimum resources
  • 19. © Talend 2014 26 MapR and Talend: your perfect guides throughout your Big Data journey • Reference architecture • Talend and MapR: our joint value proposition
  • 20. © Talend 2014 27 FOUNDATION ArchitectureMatters for Success Data protection & security High performance Multi-tenancy Operational & Analytical Workloads Open standards for integration NEW APPLICATIONS SLAs TRUSTEDINFORMATION LOWERTCO
  • 21. © Talend 2014 28 Talend and MapR, altogether: Reference architecture Clickstream Product Catalog Server Logs Merchant Listings Call Detail Records Billing Data Mobile Data Set-Top Box Data Network Data Social Media MapR Data Platform High Speed Ingest Rapid Access & Iteration NFS DATA SOURCES VISUALIZATIONPROCESSING & ANALYTICS SQOOP FLUME HDFS API HBase API HIVE 800+ connectors ActiveMQKaraf CamelCXF KafkaStorm MetaSecurity MDMiPaaS GovernHA TRANSFORMTAP DELIVER PROFILE PARSEMAP MATCH CHANGE DATA C. CLEANSE STANDAR- DIZE MACHINE LEARNING BATCH (Map Reduce) SQL (Hive, Drill) STREA MING (Spark) SCRIPTING (Pig) ML (Mahout, MLLib) GRAPH (GraphX) SEARCH (SolR) ONLINE (HBase)
  • 22. © Talend 2014 29 Talend and MapR: value proposition Designed for high end performance Speed with Hadoop Native with Hadoop Disruptive, but with your existing resources MapR NFS Connected to your systems, Familiar to your human resources Proven track record and ready to go Cloud or on premises Talend Jumpstart Sandbox One platform MapReduce, file based apps, search, SQL & NoSQL, stream processing… Big Data Integration, Data Quality and MDM, application and process integration Enterprise-ready for your IT Transformation
  • 23. © Talend 2014 30 ACCELERATING YOUR BIG DATA JOURNEY Discover Hadoop and how it applies to your use case with the sandbox Draw your Big Data plan and roadmap with our readiness scorecard
  • 24. © Talend 2014 31 Announcing the Big Data Jumpstart Sandbox - Talend Jumpstart Sandbox - virtual image installed with: • MapR Distribution for Apache Hadoop • Pre-configured Talend Platform for Big Data* • Three analysis scenarios for you to try: – Clickstream data – Twitter sentiment – Apache weblogs • Demonstrations of several NoSQL databases https://info.talend.com/prodevaltpbdsandbox-reservemapr.html *Includes Talend Studio (graphical IDE), team working, management, data quality and advanced big data features. Big Data Jumpstart Sandbox
  • 25. © Talend 2014 33 Introducing the Big Data Readiness Scorecard Understand the capability maturity model Define your current status and aspirational goal Formalize your milestones Engage your Big Data program • What is it?  A way to better assess your maturity with regards to the top 5 execution challenges of Big Data  A guideline for you big data journey  A valuable report to present and share your big data plans to your peers • How it works  A ready to run questionnaire to assess your maturity with regards to the top 5 execution challenges of Big Data  A guideline for you big data journey  A valuable report that the client can share with their management
  • 26. © Talend 2014 34 Wrap up and next steps Draw the milestones of your Big Data journey Anticipate the challenges and plan for the right resources Leverage accelerators such as our sandbox and readiness scorecard Contact us and get your readiness scorecard: talend@mapr.com or mapr@talend.com Reserve your seat for the sandbox: https://info.talend.com/prodevaltpbdsandbox-reservemapr.html