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5 Reasons Enterprise Adoption of Spark is Unstoppable by Mike Gualtieri
Spark adoption in enterprises is unstoppable for five key reasons: 1. Enterprises prioritize improved customer experience which requires real-time analysis of vast amounts of customer data. 2. Spark and Hadoop provide a cost-effective way to perform advanced analytics at scale on both internal and external data sources. 3. Spark allows for real-time analysis of data through its in-memory computing capabilities, unlocking the value of perishable insights. 4. Massive machine learning automation enabled by Spark is needed to automate the data science process and gain competitive advantages. 5. The diverse and innovative Spark community is helping drive the development of continuous analytics pipelines required for real-time insights.
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5 Reasons Enterprise Adoption of Spark is Unstoppable by Mike Gualtieri
- 1. Five Reasons EnterpriseAdoption Of Spark Is Unstoppable Mike Gualtieri, Principal Analyst February 17, 2016 New York Twitter: @mgualtieri
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- 3. REASON ADOPTION 1. Customer experienceis a top priority for enterprises.
- 4. © 2015 ForresterResearch, 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. For you Forall For segments For you Demographic Relationships Hyper-Personal, Real-Time Relationships Personal Relationships Mass Relationships CustomerExperience 1800 1900 1950 2000 2015
- 6. Customers want andincreasingly expect to be treated like celebrities.
- 7. • Learn individualcustomer 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. © 2015 ForresterResearch, 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. Using your bestestimate, 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. © 2015 ForresterResearch, 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. © 2015 ForresterResearch, 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!
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- 13. REASON ADOPTION2. Hadoop andfriends makes analytics of all kinds cost-effective at scale.
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- 15. 100% Number of enterprisesthat Forrester estimates will adopt Hadoop and friends!
- 16. Hadoop is designedfor volume.
- 17. Spark is designedfor speed.
- 18. © 2015 ForresterResearch, Inc. Reproduction Prohibited 18 Spark and Hadoop can coexist in the same cluster.
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- 22. REASON ADOPTION3. Perishable insightsmust be captured and used before they expire (or rot).
- 23. Perishable insights canhave exponentially more value than sleepy, after-the-fact traditional historical analytics.
- 24. All data isborn fast!
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- 27. How can youprevent this dude from fleecing you right now?
- 28. What offers shouldyou make to your customer if they are within proximity of your store right now?
- 29. Resilient Distributed Datasets(RDD) is a generalized data structure that can cache data in- memory and spool to disk if necessary. 58,000x
- 30. © 2015 ForresterResearch, Inc. Reproduction Prohibited 30 Spark data processing jobs run exponentially faster when the data set fits in memory.
- 31. © 2015 ForresterResearch, Inc. Reproduction Prohibited 31 Why not just pop your data in-memory?
- 32. Planning, implementing, orexpanding 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
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- 34. REASON ADOPTION4. Massive MachineLearning Automation (MMLA) is the future of data science.
- 35. Massive Machine LearningAutomation (MMLA) is the only competitive way forward.
- 36. Data scientists haveslogged through the same iterative process for 20 years
- 37. LEARNING AUTOMATION MASSIVE MACHINE Toolsand 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.
- 38. The seven characteristicsof massive machine learning automation.
- 40. REASON ADOPTION 5. Spark communityis diverse and innovating fast.
- 41. © 2015 ForresterResearch, 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)
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- 43. I need insights. You shallhave none - until you build a continuous analytics pipeline.
- 44. © 2015 ForresterResearch, Inc. Reproduction Prohibited 44 Generate industrial strength analytics with Spark and Hadoop
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