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Predictive Analytics on Big Data. DIY or BUY?

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Watch the video: http://youtu.be/KFLdWjN0n_k

Customer expectations for relevant and individualized experiences are rising and evolving at breakneck speed. This has enterprises working furiously at building data infrastructure to collect and store data. But collecting and storing is only the beginning. The technology and know-how to derive value from data—to do predictive analytics on big data—is fast becoming the critical competitive differentiator for businesses.

Join Apigee’s Abhi Rele and Alan Ho as they discuss the market dynamics of predictive analytics and big data and the key capabilities needed to deliver the adaptive apps and APIs every business needs to remain relevant and be competitive.

Join to Discuss:
- Data lakes, machine learning, unstructured data processors, real-time access, APIs—the capabilities to rapidly deliver predictive analytics on big data
- Getting from data lake to production app - how putting big data to use and deriving real value requires a fresh approach
- Pros and cons for the build vs. buy decision to deliver adaptive apps and APIs

Veröffentlicht in: Technologie, Business

Predictive Analytics on Big Data. DIY or BUY?

  1. 1. Predictive Analytics on Big Data DIY or BUY?
  2. 2. @karlunho Alan Ho @abhirele Abhi Rele
  3. 3. youtube.com/apigee
  4. 4. slideshare.com/apigee
  5. 5. www.iloveapis2014.com Use BIGDATA10 for 10% off
  6. 6. Agenda • Predictive analytics on big data • Businesses are conflicted • Forging a path forward CC-BY-SA
  7. 7. Why predictive analytics on big data? CC-BY-SA
  8. 8. The new normal • Omni-channel • Individualized • Proactive CC-BY-SA
  9. 9. Challenges • Data lakes: learning to swim • Predictive analytics: in flux • Open source: rapid innovation • Got data scientists? • Point solutions CC-BY-SA
  10. 10. Key conflict DIY with open source OR BUY product CC-BY-SA
  11. 11. Evaluating options CC-BY-SA DIY BUY Pros • Control • Cost savings • Time to market • Market evolution Cons • Expertise • Risk • Hype
  12. 12. CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt. Mobile Web Kiosk IoT Unstructured & structured data Event & entity data Real-time & batch data Partner Internal & external data
  13. 13. Data lake • Hadoop • Entities and events CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt.
  14. 14. Descriptive analytics • Simple • Complex CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt.
  15. 15. Predictive analytics • Summarized vs. fine-grain data • Unstructured data • No open source winner • Difficult to use • Mahout vs. Oryx vs. RHadoop CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt.
  16. 16. Integration • APIs vs. useful APIs • Real time • Scalability • Security CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt.
  17. 17. Monitoring & mgmt. • Achilles heel • Model performance • Model deployment • Availability CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt.
  18. 18. to summarize…
  19. 19. DIY or BUY? CC-BY-SA
  20. 20. CC-BY-SA Data lake Descriptive analytics Predictive analytics Integration Monitoring&mgmt. Mobile Web Kiosk IoT Unstructured & structured data Event & entity data Real-time & batch data Partner Internal & external data
  21. 21. DIY considerations • Maturity of open source • Skills and expertise • Ability to execute • TCO CC-BY-SA
  22. 22. BUY considerations • Hype vs. reality • Time to market • Control & flexibility • True ROI CC-BY-SA
  23. 23. www.iloveapis2014.com Use BIGDATA10 for 10% off
  24. 24. Questions? @karlunho Alan Ho @abhirele Abhi Rele
  25. 25. Thank you!

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