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SAP Applications and the Modern Data Scientist - Predictive Analytics for the End User

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Abstract: SAP Predictive Analytics 2.3 bridges the gaps between IT, Business and Analytics. Learn how SAP Predictive Analytics 2.3 leverages current SAP infrastructure and provides the data preparation, visualization and data modeling tools necessary to gain insights from your data.

Join us as we will take a look at SAP Predictive Analytics (PA) 2.3 and demonstrate the value by analyzing data that will help drive real-world decisions:
• Understand where SAP PA 2.3 resides in SAP’s analytics roadmap
• Outline system and hardware requirements for implementation
• Learn how SAP PA 2.3 is not just for Data Scientists
• Demonstrate capabilities within SAP PA 2.3
• Answer questions and discuss how PA 2.3 can improve your business

Published in: Technology
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SAP Applications and the Modern Data Scientist - Predictive Analytics for the End User

  1. 1. SAP Applications and the Modern Data Scientist – Predictive Analytics for the End User
  2. 2. Introductions What is Predictive Analytics SAP Predictive Analytics 2.3 Overview Where SAP is in the Advanced Analytics Market System Demonstration Use Case: Association Analysis Use Case: Regression Questions/Next Steps
  3. 3. 3 On the Phone: Rob Jerome Vice President, Innovation + Technology rob.j@dickinson-assoc.com Todd Siedlecki Consultant, Predictive Analytics Practice Lead todd.s@dickinson-assoc.com Olavo Figueiredo Consultant olavo.f@dickinson-assoc.com
  4. 4. 4 We Are: Focus: Delivery of quality SAP Business Suite, BI/Analytics, and Mobility consulting services to customers across North America, Europe, and Asia. Our People: A team of 140+ full-time SAP professionals reflects the ideal mix of years of relevant business knowledge, very strong SAP credentials, and solid communication skills. Our team has an average of 15 years SAP and 19 years business experience. Offices: Chicago, IL (Headquarters) Satellites: New York, NY | Scottsdale, AZ | Cincinnati, OH We are:
  5. 5. 5 Experience What Sets Us Apart? Our People.  Experienced consultants with strong SAP knowledge, sound project management capability, and years of industry experience.  Proven experience in delivering innovative ERP solutions with minimal disruption to the business.  An open corporate culture that makes us “big enough to deliver value and small enough to care”.  We carefully create each project team or support team to match the client objectives and its culture.  Most important, we understand and believe strongly that Companies don’t implement SAP… People Do. No.TeamMembers 0 – 3yrs 3 – 8yrs 8 – 14yrs 14+ yrs
  6. 6. 6 Partnership and Designations  SAP Gold Channel Partner  SAP Services Partner  SAP All-in-One Certified Solutions  SAP-Qualified Partner for RDS  Business Objects  Sybase Partner  SuccessFactors Partner
  7. 7. 7 Service Offerings SAP Strategy Implementation Process Optimization Services SAP Upgrade Services Application Support Professional Staffing
  8. 8. What is Predictive Analytics?
  9. 9. 9 Predictive Analytics Defined
  10. 10. 10 SAP Predictive Analytics - Myths  Requires a Ph.D. to implement  Hard to execute without technical expertise  Does not require business input  Only for large companies
  11. 11. 11 Why do we need it?
  12. 12. 12 Value of Predictive Analytics
  13. 13. 13 Value of Predictive Analytics
  14. 14. 14 Users of Predictive Analytics
  15. 15. 15 Users of Predictive Analytics
  16. 16. Applications of Predictive Analytics
  17. 17. 17 Applications of Predictive Analytics
  18. 18. 18 Use Cases by Line of Business
  19. 19. 19 Use Cases by Industry
  20. 20. 20 Predictive Analytics Process Model deployment, scoring, monitoring Define the objectives of the analysis; Understanding the business problem Data selection, cleansing, transformation; initial data exploration Model building, training, testing, evaluation Reiterate
  21. 21. 21 Classes of Applications  Time Series Analysis  Classification Analysis  Cluster Analysis  Association Analysis  Outlier Analysis
  22. 22. 22 Time Series Analysis  Use past data points as the basis for projecting future values  Variable = Data (i.e. Sales or Headcount) with a series of values over time  Historical patterns of past data are used to make predictions
  23. 23. 23 Classification Analysis  Goal is to predict a variable (a.k.a. target or dependent variable) using the data of other variables  Largest group of applications of predictive analysis  Examples: churn analysis, target marketing, predictive maintenance
  24. 24. 24 Cluster Analysis  Takes the data set and groups it into segments (clusters) that have similar attributes  Application is often used to subset a large data set in order to better understand the attributes of the smaller subsets  Helps to find patterns and explanations for relationships  Examples: customer segmentation
  25. 25. 25 Association Analysis  Find associations between items  Example: Shopping basket and product recommendations
  26. 26. 26 Outlier Analysis  This class of applications seeks unusual or unexpected values in the dataset  Possible significant impact on predictive models, so it’s used in the context of all other classes of predictive applications  Could be genuine variations or errors  Example: fraud detection
  27. 27. SAP Predictive Analytics 2.3
  28. 28. 28 SAP Predictive Analytics 2.3 - Overview  Automate data prep, predictive modeling, and deployment – and easily retain models  Harness in-database predictive scoring for a wide variety of target systems  Leverage advanced visualization capabilities to quickly reveal insights  Integrate with R to a enable a large number of algorithms and custom R scripts  Deploy SAP Predictive Analytics stand-alone or with SAP HANA
  29. 29. 29 SAP Predictive Analytics 2.3 – System Requirements  Server Requirements  300 MB of disk space  2GB of RAM  Client Hardware Requirements  150 MB of disk space  512 MG of RAM  30 day free trial available  http://go.sap.com/product/analytics/predictive-analytics.html
  30. 30. 30 SAP Predictive Analytics 2.3 – Automated vs. Expert Automated Analytics  Designed for business analyst or super user  Drag and drop/Point and click tool  Preps data for the user  Automatically selects appropriate model Expert Analytics  Designed for statisticians  Robust functionality with statistical software R  Create your own algorithms  Compare effectiveness of models
  31. 31. Demo 1 – Predictive Maintenance
  32. 32. 32 Demo 1 – Business Problem Background  A manufacturing company is seeking to lower their preventative maintenance costs on certain machines
  33. 33. 33 Demo 1 – Predictive Maintenance  Maintenance scheduled according to set time period Future State – Predictive Maintenance  Maintenance scheduled according to data analysis Current State – Preventative Maintenance
  34. 34. Demo 2 – Employee Turnover
  35. 35. 35 Demo 2 – Business Problem  A marketing company is experiencing a high rate of turnover among employees  When an employee leaves, the process is very expensive due to the following:  Lost Knowledge  Training Costs  Interviewing Costs  Lowered Productivity
  36. 36. 36 Demo 2 – Analytics to Improve HR  HR would like to use analytics to know not only which employees will be likely to leave, but also take a more refined approach by grouping employees with similar characteristics together  Goals:  Segment out employees into different groups  Determine which groups are most likely to have a high turnover rate  Analyze data to determine what incentives could be best offered to keep employees from leaving
  37. 37. Questions and Next Steps
  38. 38. 38 What’s Next?  Q+A  Contact Todd Siedlecki to discuss how SAP Predictive Analytics may fit in to your analytics strategy  Email – todd.s@dickinson-assoc.com

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