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Notilyze SAS
- 1. Lean Mean Data Science Machine Tom Dogger & Mark Bakker 21-09-2017
- 2. Tom Dogger Founder Notilyze Mark Bakker Senior Business Solution Manager at SAS Who are we?
- 3. Copyright © 2012, SAS Institute Inc. All rights reserv ed. 40 #1 13,811 93 94% SAS employees worldwide of the top 100 companieson the GLOBAL 500 LIST Annual customer retention rateYears of BUSINESS ANALYTICS World’s privately held software company LARGEST 35.4% Market share Worldwide Advanced and Predictive Analytics Software Predictive Analytics Founded in 2016 Data-analytics -as-a-service NotilyzeAnalytics Platform Subscriptionbased
- 4. Lean Mean Data Science Machine Image credit: Shutterstock
- 5. Lean Mean Data Science Machine
- 6. Start Start initial project Implementation Complexity Wink to the meaning of Lean ;) From 0 to fully Data Driven in very Short time Operationalized Insights for your organization
- 7. About the Client Cases Credit Exchange Groep FULL-SERVICE CREDIT MANAGEMENT COMPANY
- 8. Lean Mean Data Science Machine
- 9. Start Start initial project Report results Business case Improve Experimental model Show possibilities Implementation Complexity You’ve started; and now? Data Discovery
- 10. Case 1: From insight to prediction
- 11. Value Drivers for Analytical Visualization of your Data Data Discovery z • Predictive Self Service • Get a detailed view of large Data Sets • Increase Productivity • Make advanced analytics approachable • Zoom in on outliers
- 12. Lean Mean Data Science Machine
- 13. Start Start initial project Report results Business case Improve Experimental model Show possibilities Implement Create operational value Implementation Complexity Operationalize your new insights Implement
- 15. Neural networks Text mining Pattern recognitionPredictive analytics Advanced Analytics Case 2
- 16. Workflow Analytics IT Extend business case Advanced Analytics Advanced Analytics Case 2
- 18. Start Start initial project Report results Business case Improve Experimental model Show possibilities Implement Create operational value Improve Advanced analytics Become data driven Implementation Complexity Manage your Analytics to keep it Accurate Model Management
- 19. Value Drivers for Model Mangement Improve your Analytics structured • More accurate predictions lead to the best decisions • Eliminate the error prone process of moving models from a development environment to an operational environment • Less reliance on a large team of data scientists • Collaboration IT Business • Reduce Costs
- 20. Lean Mean Data Science Machine
- 21. Start Start initial project Report results Business case Improve Experimental model Show possibilities Implement Create operational value Improve Advanced analytics Become data driven Implementation Complexity And now we can make a working Machine Automated Decision Management Operationalized Insights for your organization
- 23. • Start simple; Copy business rules Decision Making Case 3
- 25. Value Drivers for automated Decision Management The business value rests at making better decisions • More accurate predictions lead to the best decisions • Reduces single point of failure • Less reliance on key employees • Enable collaboration with experts, IT and business • Underpinned by a governance framework • Shortens the development and deployment process • Increases Innovation Opportunity
- 26. Lean Mean Data Science Machine Conclusion
- 27. Start Start initial project Report results Business case Improve Experimental model Show possibilities Implement Create operational value Improve Advanced analytics Become data driven Implementation Complexity Lean Mean Data Science Machine Operationalized Insights for your organization
- 28. Questions?
Editor's Notes
- More accurate predictions lead to the best decisions Automated model development with SAS offering the broadest array of analytical techniques applying the right methodology for the data The error prone process of moving models from a development environment to an operational environment is eliminated Reduces single point of failure Less reliance on a large team of data scientists An open modelling approach that enables collaboration with experts, IT and business Underpinned by a governance framework that automatically tracks all the steps from model initiation to model retirement Reduces cost Expands the analytics talent pool through automated machine learning allowing scarce analytical resources to focus on high value predictions and forecasts Shortens the development and deployment process while reducing manual IT costs Increases Innovation Opportunity to try out new modelling approaches quickly to see which add significant value and which fail
- More accurate predictions lead to the best decisions * Automated model development with SAS offering the broadest array of analytical techniques applying the right methodology for the data Reduces single point of failure * Less reliance on key employees * An open modelling approach that enables collaboration with experts, IT and business * Underpinned by a governance framework that automatically tracks all the steps from model initiation to model retirement Reduces cost * Expands the analytics talent pool through automated machine learning allowing scarce analytical resources to focus on high value predictions and forecasts * Shortens the development and deployment process while reducing manual IT costs Increases Innovation Opportunity to try out new modelling approaches quickly to see which add significant value and which fail