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Intelligent data summit: Self-Service Big Data and AI/ML: Reality or Myth?

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Companies collect more data but struggle with how to glean the best insights. Use of Machine Learning also needs power data integration.

In this presentation, Janet Jaiswal, SnapLogic's VP of product marketing, reviews key strategies and technologies to deliver intelligent data via self-service ML models.

To learn more, visit https://www.snaplogic.com

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Intelligent data summit: Self-Service Big Data and AI/ML: Reality or Myth?

  1. 1. Self-Service Big Data and AI/ML Reality or Myth? Janet Jaiswal, VP of Product Marketing at SnapLogic, Inc. August 23, 2018
  2. 2. 2 Agenda The Rise of the Data-Driven Enterprise & Empowered user Big Data, Big Problems! Self-Service ML: Reality or Myth? Demo and Key Takeaways Resources
  3. 3. corporate overview Rise of the Data-Driven Organization & the Empowered User
  4. 4. Most companies are undergoing Digital Transformation but technology is holding them back 4 Source: Vanson Bourne Legacy databases: The culprit behind the data dilemma ©2018 SnapLogic, Inc. All Rights Reserved
  5. 5. Employees are increasingly demanding self-service yet significant barriers remain Confidential Content©2018 SnapLogic, Inc. All Rights Reserved Source: TDWI, https://tdwi.org/articles/2017/10/23/bi-all-data-driven-focus-on-user-empowerment.aspx What are the most significant barriers to increasing users’ self-reliance and reducing their dependencies on IT for BI, analytics and data preparation? 5
  6. 6. corporate overview Big Data, Big Problems!
  7. 7. The data value disconnect Source: 2018 Survey of 500 IT and business users across US & UK conducted by Vanson Bourne 80% believe legacy technology is holding their organization back from taking advantage of data-driven opportunities 51% organizations use only half of the data they collect or generate 29% have complete trust in their organizations’ data when it comes to making business- critical decisions ©2018 SnapLogic, Inc. All Rights Reserved Confidential Content 7
  8. 8. 8 Data lakes remain an empty promise... #1 reason for failure: Talent shortage 90% of Data Lake projects are delayed and over budget 60% completely fail... Source: Gartner Survey Analysis: Traditional Approaches Dominate Data and Analytics Initiatives, October 13, 2017
  9. 9. Key challenges that are slowing adoption Confidential Content 1. Lack of specialized developer & data science skills ◦ Citizen Data Scientists - Scenario modeling and forecasting ◦ Skilled Data Scientists - Develop new algorithms and models (extremely large datasets) and augment ML frameworks to create models 2. Lack of useful data ◦ Integrate a variety of endpoints to obtain relevant, quality data ◦ Prepare the data in useful format ◦ Create and manage big data (manage high costs due to size of data & skills gap) A need for a no-code paradigm ◦ Employee empowerment means users of all skillset must be able to access data and perform their own analysis ©2018 SnapLogic, Inc. All Rights Reserved 9
  10. 10. SnapLogic eXtreme: What is it? SnapLogic eXtreme extends the SnapLogic Integration platform to provide a serverless, cloud-based runtime environment for complex & high-volume data transformation routines servicing various big data use-cases at elastic scale ©2018 SnapLogic, Inc. All Rights Reserved Confidential Content
  11. 11. Customer’s journey to the cloud for big data projects • Big Data historically started on-premise • High CapEx, High OpEx, Skill-set gap • Sizing for peak loads • Move to cloud for infrastructure savings • Eliminate CapEx; Take advantage of IaaS, High OpEx, skills-set gap remains • Still sizing for peak loads • Move to fully managed data architecture to reduce complexity • Dramatically reduces OpEx and skills-set gap • Provides Elastic scale ©2018 SnapLogic, Inc. All Rights Reserved ©2018 SnapLogic, Inc. All Rights Reserved Confidential Content 11
  12. 12. EDW Data Mart Data Mart Data Mart The evolution of enterprise data architecture S3, ADLS, GCS SnapLogic eXtreme EDWData Lake Push Data Mart Data Mart Data Mart Data Science Workbench Social Media IoT Database SaaS App File Pull Push Stream Big Data as a Service (AWS, Azure, GCP) 1990’s & 2000’s Batch Today Batch + Streaming One Integrated Platform (SnapLogic EIC) ©2018 SnapLogic, Inc. All Rights Reserved Confidential Content
  13. 13. SnapLogic eXtreme: Key benefits Improved Productivity across Developers, Big Data Architects and Administrators • Requires no special data engineering skills • Visual Spark pipeline development with no code Improved TCO (Total Cost of Ownership) for Data Lake Implementations • Unified “Elastic Scale” platform integrated with Enterprise Integration Cloud • Fully-automated, managed cloud-based big data runtime environment ©2018 SnapLogic, Inc. All Rights Reserved Confidential Content 13 • Do more with less resources, improve time to market, faster time to insights • Speed up data-driven big data project implementations Improved Business Agility Enables ML-based Analytics • ML model building for prescriptive and predictive analytics requires a large amount of data
  14. 14. corporate overview Self-Service ML: Reality or Myth?
  15. 15. More and more companies are becoming data-driven 15 Source: https://www.forrester.com/report/InsightsDriven+Businesses+Set+The+Pace+For+Global+Growth/-/E-RES130848 “By 2021, insights-driven business will steal $1.8 trillion a year in revenue from competitors that are not insights- driven.” Confidential Content 15 ©2018 SnapLogic, Inc. All Rights Reserved
  16. 16. ML is a key part of many organization’s plans 16 Sources: McKinsey Global Institute and Google of respondents are exploring use cases for Machine Learning Confidential Content 16 ©2018 SnapLogic, Inc. All Rights Reserved of executives believe their organization’s future success depends on the successful implementation of Machine Learning 48% 60%
  17. 17. However, organizations have a lot of analysts but not enough Data Scientists 17 McKinsey Global Institute predicts that the US economy will be short 250,000 data scientists by 2024. Source: McKinsey Global Institute “The Age of Analytics: Competing In A Data-Driven World.” Confidential Content 17 ©2018 SnapLogic, Inc. All Rights Reserved
  18. 18. Why the shortage? So they can satisfy the needs of…..Organizations are competing with a few elite companies for talent Confidential Content 18 ©2018 SnapLogic, Inc. All Rights Reserved
  19. 19. Machine Learning stages Data Collection Collect and prepare data Data Preparation Make sense of data ML Model Training & Testing Use data to answer questions Model Deployment Deploy and operationalize models 70-80% of effort Confidential Content 19 ©2018 SnapLogic, Inc. All Rights Reserved
  20. 20. SnapLogic EIC + ML: End-to-end data science • Clean Missing Values • Type • Categorical to Numeric • Numeric to Categorical • Scale • Shuffle • Sample • Date Time Extractor Data Preparation ML Data Prep Snap Pack ML Core Snap Pack ML Analytics • Classification Trainer • Classification Cross Validator • Classification Predictor • Regression Trainer • Regression Cross Validator • Regression Predictor • Remote Python Script • Jupyter Notebook Integration • Profile • Type Inspector ML Deployment • Ultra (Real-Time) Pipeline for hosting ML Models as REST API Model Deployment Confidential Content 20 ©2018 SnapLogic, Inc. All Rights Reserved Data Collection • Connect anything (applications, data warehouses, IoT, APIs and processes) • Any deployment mode (on- premises, cloud , hybrid) • Any speed (streaming, batch, event- driven, real-time) Model Training & Testing SnapLogic EIC SnapLogic EIC SnapLogic ML
  21. 21. corporate overview Product Demonstration
  22. 22. Case Study: Iris AI’s Integration Assistant 22 Task Integration Assistant Conventional Approach Integration Assistant With SnapLogic ML Data Acquisition 200 Lines Of Code (LOC) 1 Days 10 LOC + 11 Snaps 20 Mins Data Preparation 350 LOC 2 Days 150 LOC + 32 Snaps 3 Hours Model Development 50 LOC 1 Days 0 LOC + 8 Snaps 1 Days ML API Deployment 200 LOC 3 Days 0 LOC + 7 Snaps 20 Mins Continuous Learning 200 LOC 3 Days 0 LOC + 0 Snap 20 Mins Total 1000 LOC 10 Days 160 LOC + 58 Snaps 1 Days 4 Hours Confidential Content 22 ©2018 SnapLogic, Inc. All Rights Reserved
  23. 23. Benefits of SnapLogic ML No need for specialized skillsets • In some cases, no coding needed • BYOML - Bring your own ML to the Native Python Snap • Operationalize model training & deployment Access to multiple data sources • Current ML products do not offer integration plus operationalization Data security • No need to send data to cloud service for training • Keep you training data completely private ML Algorithm accuracy • Better accuracy with our ML algorithms compared to cloud services Confidential Content 23 ©2018 SnapLogic, Inc. All Rights Reserved
  24. 24. SnapLogic Machine Learning Showcase Confidential Content Go to: labs.snaplogic.com ©2018 SnapLogic, Inc. All Rights Reserved 24
  25. 25. Demos available at SnapLogic ML Lab Showcase Confidential Content 25 ©2018 SnapLogic, Inc. All Rights Reserved
  26. 26. corporate overview Key Takeaways
  27. 27. Digital Transformation requires that organizations become data driven SnapLogic Enterprise Integration Cloud (EIC) SnapLogic eXtreme SnapLogic ML Connects the entire organization Simplifies at scale data processing The only unified Cloud-agnostic platform. It does not matter where the data sits, lives or breathes! Simplifies data science with AI/ML Snaps Confidential Content 27 ©2018 SnapLogic, Inc. All Rights Reserved
  28. 28. Key steps to leveraging Big Data and AI/ML Use SnapLogic EIC to Make ALL Data Sources Easy to Access#1 Make Big Data Fast to Deploy and Easy to Manage with SnapLogic eXtreme#2 Make ML Tools Easy to Use and Accessible for all Types of Users with SnapLogic ML#3 Empowers Users with SnapLogic EIC’s Self- Service Capabilities to Alleviate IT Bottlenecks#4 28 Summary: Key steps to leveraging Big Data and AI/ML
  29. 29. corporate overview Resources
  30. 30. Resources 30 • SnapLogic Integration Buyer's Guide • Modern Enterprise Data Architecture White Paper • Extending the Value of Microsoft Dynamics CRM White Paper • SnapLogic vs. MuleSoft Comparison guide Confidential Content 30 ©2018 SnapLogic, Inc. All Rights Reserved
  31. 31. Thank You San Mateo, CA New York, NY London, UK Hyderabad, India Janet A. Jaiswal VP of Product Marketing Jjaiswal@snaplogic.com www.snaplogic.com Connect with SnapLogic via our blog, Twitter, Facebook, or LinkedIn

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