Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

5 Reasons Enterprise Adoption of Spark is Unstoppable by Mike Gualtieri

1.157 visualizaciones

Publicado el

Spark Summit East talk

Publicado en: Datos y análisis
  • Sé el primero en comentar

5 Reasons Enterprise Adoption of Spark is Unstoppable by Mike Gualtieri

  1. 1. Five Reasons Enterprise Adoption Of Spark Is Unstoppable Mike Gualtieri, Principal Analyst February 17, 2016 New York Twitter: @mgualtieri
  2. 2. #Customers
  3. 3. REASON ADOPTION 1. Customer experience is a top priority for enterprises.
  4. 4. © 2015 Forrester Research, Inc. Reproduction Prohibited 4 52% 53% 53% 54% 58% 64% 64% 65% 66% 73% 75% 0% 10% 20% 30% 40% 50% 60% 70% 80% Better leverage big data and analytics in business decision-making Create a comprehensive strategy for addressing digital technologies like mobile, social & smart products Create a comprehensive digital marketing strategy Better comply with regulations and requirements Improve differentiation in the market Increase influence and brand reach in the market Address rising customer expectations Improve our ability to innovate Reduce costs Improve our products /services Improve the experience of our customers A strong majority of business leaders prioritize improved customer experience and products. › Base: 3,005 global data and analytics decision-makers › Source: Global Business Technographics Data And Analytics Online Survey, 2015
  5. 5. For you For all For segments For you Demographic Relationships Hyper-Personal, Real-Time Relationships Personal Relationships Mass Relationships CustomerExperience 1800 1900 1950 2000 2015
  6. 6. Customers want and increasingly expect to be treated like celebrities.
  7. 7. • Learn individual customer characteristics and behaviors (understanding) • Detect customer needs and desires in real-time (context) • Adapt applications to serve an individual customer (experience) Celebrity experiences must:
  8. 8. © 2015 Forrester Research, Inc. Reproduction Prohibited 8 Fortunately, every industry is graced with more data › Richer transactional data from portfolio of hundreds of business applications › Usage and behavior data from web and mobile apps › IoT device sensor and event data › Social media data › Log data › Data economy – firms buying and selling data
  9. 9. Using your best estimate, what is the size of all data stored within your company? Source: Forrester Research, September 2015 Base: 100 US Managers and above currently using Hadoop for processing and analyzing data. Enterprises have plenty of data from both internal and external sources 10-49 Terabytes 5% 50-99 Terabytes 12% 100-500 Terabytes 54% Greater than 500 Terabytes 29% Internal business data 49% External source data 51% What % of the data available is from internal business applications (ERP and business applications) versus external sources (social, IoT)?
  10. 10. © 2015 Forrester Research, Inc. Reproduction Prohibited 10 Learn Model Detect Adapt Four kinds of analytics are necessary Predictive Analytics Streaming Analytics Descriptive Analytics (Advanced Analytics) Prescriptive Analytics Batch Real-time Most firms invest here They must invest here too
  11. 11. © 2015 Forrester Research, Inc. Reproduction Prohibited 11 Source: Forrester Research That’s why use of advanced analytics is surging “What is your firm's/business unit's current use of the following technologies?” Source: Forrester's Global Business Technographics Data And Analytics Survey, 2015 and 2014 Base: 1805 (2015), 1063 (2014) 19% 19% 24% 31% 34% 22% 22% 35% 31% 43% 53% 54% 50% 50% 69% 39% 42% 42% 42% 42% 43% 43% 46% 48% 52% 54% 55% 56% 57% 69% Non modeled data exploration and discovery Search/interactive discovery Streaming analytics Metadata generated analytics OLAP Advanced visualization Text analytics Location analytics Predictive analytics Process analytics Embedded analytics Web analytics Dashboards Performance analytics Reporting 2015 2014 Most of your competitors still haven’t started!
  12. 12. #Hadooponomics
  13. 13. REASON ADOPTION2. Hadoop and friends makes analytics of all kinds cost-effective at scale.
  14. 14. #
  15. 15. 100% Number of enterprises that Forrester estimates will adopt Hadoop and friends!
  16. 16. Hadoop is designed for volume.
  17. 17. Spark is designed for speed.
  18. 18. © 2015 Forrester Research, Inc. Reproduction Prohibited 18 Spark and Hadoop can coexist in the same cluster.
  19. 19. #Perishable
  20. 20. REASON ADOPTION3. Perishable insights must be captured and used before they expire (or rot).
  21. 21. Perishable insights can have exponentially more value than sleepy, after-the-fact traditional historical analytics.
  22. 22. All data is born fast!
  23. 23. 110010011011001 010010011 010011001101 0100 CustomerData Transactions DataWarehosue IoT But, analytics is usually done much later.
  24. 24. #WhyWait
  25. 25. How can you prevent this dude from fleecing you right now?
  26. 26. What offers should you make to your customer if they are within proximity of your store right now?
  27. 27. Resilient Distributed Datasets (RDD) is a generalized data structure that can cache data in- memory and spool to disk if necessary. 58,000x
  28. 28. © 2015 Forrester Research, Inc. Reproduction Prohibited 30 Spark data processing jobs run exponentially faster when the data set fits in memory.
  29. 29. © 2015 Forrester Research, Inc. Reproduction Prohibited 31 Why not just pop your data in-memory?
  30. 30. Planning, implementing, or expanding the use of in-memory data platform. 73% Base: 1,805 global data and analytics decision-makers Source: Forrester Global Business Technographics Data And Analytics Online Survey, 2015
  31. 31. #MMLA
  32. 32. REASON ADOPTION4. Massive Machine Learning Automation (MMLA) is the future of data science.
  33. 33. Massive Machine Learning Automation (MMLA) is the only competitive way forward.
  34. 34. Data scientists have slogged through the same iterative process for 20 years
  35. 35. LEARNING AUTOMATION MASSIVE MACHINE Tools and technologies that automate through configuration rather than coding the process of data preparation, model building using statistical and machine learning algorithms, model evaluation, and model monitoring at scale.
  36. 36. The seven characteristics of massive machine learning automation.
  37. 37. REASON ADOPTION 5. Spark community is diverse and innovating fast.
  38. 38. © 2015 Forrester Research, Inc. Reproduction Prohibited 41 Learn Model Detect Adapt Only the analytical enterprise can compete and win in the age of the customer Predictive Analytics Streaming Analytics Descriptive Analytics (Real-time) Prescriptive Analytics (Continuous Batch)    
  39. 39. #Insights
  40. 40. I need insights. You shall have none - until you build a continuous analytics pipeline.
  41. 41. © 2015 Forrester Research, Inc. Reproduction Prohibited 44 Generate industrial strength analytics with Spark and Hadoop
  42. 42. forrester.com Thank you Mike Gualtieri mgualtieri@forrester.com Twitter: @mgualtieri

×