SAP Analytics Innovation Tour: Predictive Analysis Showcase

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Presentation by SAP's Director of Advanced Analytics, Charles Gadalla, as part of the SAP Analytics Innovation Tour of Asia and ANZ. More details here: http://timoelliott.com/blog/2013/02/innovative-analytics-in-asia-and-anz.html

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SAP Analytics Innovation Tour: Predictive Analysis Showcase

  1. 1. SAP Predictive AnalysisTransform Your Future with Predictive InsightCharles Gadalla, Solution ManagementFebruary 2013
  2. 2. Safe Harbor StatementThe information in this presentation is confidential and proprietary to SAP and may not be disclosedwithout the permission of SAP. This presentation is not subject to your license agreement or anyother service or subscription agreement with SAP. SAP has no obligation to pursue any course ofbusiness outlined in this document or any related presentation, or to develop or release anyfunctionality mentioned therein. This document, or any related presentation and SAPs strategy andpossible future developments, products and or platforms directions and functionality are all subjectto change and may be changed by SAP at any time for any reason without notice. The informationon this document is not a commitment, promise or legal obligation to deliver any material, code orfunctionality. This document is provided without a warranty of any kind, either express or implied,including but not limited to, the implied warranties of merchantability, fitness for a particularpurpose, or non-infringement. This document is for informational purposes and may not beincorporated into a contract. SAP assumes no responsibility for errors or omissions in thisdocument, except if such damages were caused by SAP intentionally or grossly negligent.All forward-looking statements are subject to various risks and uncertainties that could cause actualresults to differ materially from expectations. Readers are cautioned not to place undue reliance onthese forward-looking statements, which speak only as of their dates, and they should not be reliedupon in making purchasing decisions.© 2013 SAP AG. All rights reserved. 2
  3. 3. Why predictive now? Increased Business Increased Data Value Increasing Technology Interest (Big Data) Performance  Answer more sophisticated  Exploding data volume  Create efficient business questions  Expanding data varieties models  Resolve real-time problems  Invest in data to get value  Reduce data processing time Changing landscapes and new opportunities3 © 2013 SAP AG. All rights reserved. 3
  4. 4. Extend your analytics capabilities where you want to be… Sense & Respond Predict & ActCompetitive Advantage Optimization Predictive Modeling What is the best that could happen? Generic Predictive Analytics Ad Hoc Reports & What will happen? OLAP Standard Cleaned Reports Raw Why did it happen? Data Data What happened? Analytics Maturity The key is unlocking data to move decision making from sense & respond to predict & act © 2013 SAP AG. All rights reserved. 4
  5. 5. © 2013 SAP AG. All rights reserved. 5
  6. 6. SAP in the Leader’s quadrant!We’ve come a long way in 6 months……from standing start to LEADER! © 2013 SAP AG. All rights reserved. 6
  7. 7. SAP’s Predictive Analytics Strategy In-time Actionable Empower the Business In Context Insights Extend the Business  In-memory processing  Relevant to your business Intelligence competency to  Within the context of your  No data latencies Advanced Analytics Industry and LOB scenario  Big Data ready Embed Predictive into Apps and BI environments Lend expertise Real-time in-memory predictive and next generation visualization and modeling© 2013 SAP AG. All rights reserved. 7
  8. 8. Where SAP has helped customers withPredictive Analytics in IndustryRetail and Consumer Products Financial Services Manufacturing & Utilities Demand forecasting and insights  Price optimization  Demand simulation for Market basket insights and KVI  Product and portfolio optimization configurable products Markdown (and price)  Market segmentation  Supply chain optimization optimization  Corporate and credit risk  Load demand modeling and Size and zone optimization management forecasting Inventory Optimization  Customer retention,  Smart Energy Meter analytics Promotional Assessments segmentation  Customer service, customer Identify geographic trends and  Cross- and up-selling, customer lifetime value performance lifetime value  Asset efficiency: spare parts, outages, inventory, riskPublic Sector & Healthcare High Tech & Telco Fraud, Waste and Abuse discovery  Sales Forecasting, Enablement Analytics enhancement for billing and category management  Customer experience Crime trends and At Risk analytics  Buyer classification Predict community movement within taxing districts  Demand insights Predict likelihood of disease Identify clinical trial outcomes © 2013 SAP AG. All rights reserved. 8
  9. 9. How SAP has helped customers withPredictive Analytics in IndustryRetail and Consumer Products Financial Services Manufacturing & Utilities 10.5M accounts clusterered and  Significant improvements to  Reduced time, effort and costs to segmented within 90 seconds for retention and reduction in attrition develop new products greater promotional insights  Improved revenue and net margin  Reduction in defective products Sell larger basket sizes by identifying by accelerating profitable  Manufacturing process products that drive drag-along sales marketing initiatives by targeting improvements Reduction of returned goods by 40% under-served customer segment  Energy trading and grid demand led to yearly savings of ~ $50M  Fraud detection and risk planning Improve revenue and net margin by management  Major improvements to energy accelerating profitable demand and resource merchandising initiatives requirementsPublic Sector & Healthcare High Tech & Telco Improved taxpayer compliance and  React faster and more revenue collection by +10% appropriately to the causes Improve revenue collection levels, of customer churn discourage fraudulent behavior with  Improved quality while meeting better prediction and investigation and strict timelines within contribute to the reduction of budget budget by realigning resources to deficits projects that complement their skill sets Significant savings by more quickly and precisely identifying fraud Reduction in compliance and policy audits © 2013 SAP AG. All rights reserved. 9
  10. 10. Success stories
  11. 11. Coinstar• Inventory Optimization• Real time Offers• Servicing © 2011 SAP AG. All rights reserved. 11
  12. 12. Mitsui Knowledge IndustryHealthcare – Speed Research & Improve Patient Support Business Challenges 408,000x  Reduce delays and minimize the costs associated with new drug faster than discovery by optimizing the process for genome analysis traditional disk-  Improve and speed decision making for hospitals which conduct based systems in a cancer detection based on DNA sequence matching technical PoC Technical Implementation  Leveraged the combination of SAP HANA, R, and Hadoop to store, pre-process, compute, and analyze huge amounts of data 216x faster by  Provide access to breadth of predictive analytics libraries reducing genome analysis from Benefits several days to  For pharmaceutical companies, provide required new drugs on only 20 minutes time and aid identification of “driver mutation” for new drug targets making real-time  Able to provide a one stop service including genomic data cancer/drug analysis of cancer patients to support personalized patient screening possible therapeutics“ ”Our solution is to incorporate SAP HANA along with Hadoop and R to create a single real-time big data platform. With this wehave found a way to shorten the genome analysis time from several days down to only 20 minutes.Yukihisa Kato, CTO and Director of MITSUI KNOWLEDGE INDUSTRY © 2011 SAP AG. All rights reserved. 12
  13. 13. BigpointGaming Industry - Predictive Game Player Behavior Analysis Business Challenges 5,000 events per  Increase conversion rates from free  paying player second loaded onto SAP HANA (not  Increase the average revenue per paying player possible before)  Decrease churn – keep paying players playing longer Technical Challenges  Leverage real-time data processing in SAP HANA and 10-30% classification algorithms with R integration for SAP HANA to increase in revenue deliver personalized context-relevant offers to players per year  Analyze vast amounts of historical and transactional data to forecast player behavior patterns Interactive Benefits data analysis  Real-time insights leading to improved  Per player profitability analysis and increased understanding of design thinking and player behavior game planning  Increase data volume and processing capabilities to communicate personalized messages to players“”At Bigpoint in the Battlestar Galactica online game, we have more than 5,000 events in the game per second which we have toload in SAP HANA environment and to work on it to create an individualized game environment to create offers for them. In thisco-innovation project with SAP HANA, using Real Time Offer Management Bigpoint, we hope to increase revenue by 10-30%.Claus Wagner, Senior Vice President SAP Technology, Bigpoint © 2011 SAP AG. All rights reserved. 13
  14. 14. SAP Predictive AnalysisSolution Overview
  15. 15. SAP Predictive AnalysisVisualize, discover, and share hidden insights • Advanced visualization designed where you’d expect it – natively from within the modelling tool • Share insights via PMML and with other BI client tools© 2013 SAP AG. All rights reserved. 15
  16. 16. SAP Predictive Analytics Solution SAP Predictive Analytics Solution Applications (Industry & LOB) Expertise – PIO, Partners Customer Analytics, Affinity Insight / Unified Demand Forecast for Retail, Smart Meter Analytics, SAP Predictive Analysis modern design, model, visualize Real Time Execution Environment develop & score in-memory Powered by HANA RDBMS, IQ, BW, Universes, XLS…© 2013 SAP AG. All rights reserved. 16
  17. 17. R is a software environment for statistical computing and graphics Open Source statistical programming language Over 3,500 add-on packages; ability to write your own functions Widely used for a variety of statistical methods More algorithms and packages than SAS + SPSS + Statistica Who is using it? Growing number of data analysts in industry, government, consulting, and academia Cross-industry use: high-tech, retail, manufacturing, CPG, financial services , banking, telecom, etc. Why are they using it? Free, comprehensive, and many learn it at college/university Offers rich library of statistical and graphical packages© 2013 SAP AG. All rights reserved. 17
  18. 18. Options to use SAP Predictive Analysis© 2013 SAP AG. All rights reserved. 18
  19. 19. SAP HANA Predictive Ecosystem SAP and Business SAP Predictive Custom Intelligence Analysis Applications Clients SAP HANA Platform Predictive SAP HANA Studio R Integration for Analysis Library SAP HANA R (PAL) Data Pre-Processing and Loading SAP Data Services, Information Composer, SLT, DXC, Hadoop© 2013 SAP AG. All rights reserved. 19
  20. 20. Predictive Analytics - Roadmap & Resources© 2013 SAP AG. All rights reserved. 20
  21. 21. Predictive Analysis Roadmap – Key Themes© 2013 SAP AG. All rights reserved. 21
  22. 22. SAP HANA PAL Roadmap* 2013 SPS05• HANA PAL SPS06 late Q2 2013**These are planned dates andfeatures only and not firm commitments (import) © 2013 SAP AG. All rights reserved. 22 (export)
  23. 23. © 2013 SAP AG. All rights reserved. 23
  24. 24. © 2013 SAP AG. All rights reserved. 24
  25. 25. Thank You! Contact information: Charles.Gadalla@sap.com @CGadalla© 2013 SAP AG. All rights reserved. 25
  26. 26. Appendix© 2013 SAP AG. All rights reserved. 26
  27. 27. R Integration for SAP HANAEmbedded Scenario Embedding R scripts within the SAP HANA database Sample Code in SAP HANA SQLScript execution CREATE FUNCTION LR( IN input1 SUCC_PREC_TYPE, OUT output0 Enhancements are made to the SAP HANA database R_COEF_TYPE) to allow R code (RLANG) to be processed as part of LANGUAGE RLANG AS the overall query execution plan CHANGE_FREQ<-input1$CHANGE_FREQ; SUCC_PREC<-input1$SUCC_PREC; This scenario is suitable when the modeling and coefs<-coef(glm(SUCC_PREC ~ consumption environment sits on HANA and the R environment is used for specific statistical functions CHANGE_FREQ, family = poisson )); INTERCEPT<-coefs["(Intercept)"]; CHANGEFREQ<-coefs["CHANGE_FREQ"]; result<- as.data.frame(cbind(INTERCEPT,CHANGEFREQ )) ; TRUNCATE TABLE r_coef_tab; Send data and R CALL LR(SUCC_PREC_tab,r_coef_tab ); script 1 SELECT * FROM r_coef_tab; 2 3 Run the R scripts Get back the result from R to SAP HANA © 2013 SAP AG. All rights reserved. 27
  28. 28. SAP HANA – Hadoop, Sentiment Analysis Integration Visualize HIVE data in SAP Business Objects BI BusinessObjects BI Pre-process data in Hadoop Hadoop Visualize HANA data in Log unstruct Data Services Load results into SAP Business Objects BI files ured 4.1 HANA data SAP HANA SP5 Text Analysis  31 languages  Entity extraction  Sentiment analysis© 2013 SAP AG. All rights reserved. 28
  29. 29. SAP Predictive Analysis: Algorithms• Supports In-Process and SAP HANA In-Database Predictive Analytics Algorithms• In Process Predictive Analysis Algorithms (Desktop)• Data is brought to SBOP PA and analysis is performed in the client• Sources: • SAP PA Native Algorithms • Open Source ‘R’ integration algorithm• In Database Predictive Analytics Algorithms (within SAP HANA)• Analysis is done within HANA (no movement of data) and controlled by SBOP PA• Sources: • SAP HANA Predictive Analysis Library (PAL) algorithms • K-means clustering • Multi-linear regression • KNN (K Nearest Neighbor) • Apriori • C4.5 decision tree © 2013 SAP AG. All rights reserved. 29
  30. 30. List of Algorithms in SAP Predictive Analysis 1.0.7 SAP PA with R (out of the box) SAP PA Native Algorithms – Association Analysis – Outlier Detection Analysis o R-Apriori o Inter Quartile Range – Segmentation Analysis o Nearest Neighbor Outlier o R K-Means – Regression Analysis – Decision Tree o R CNR Tree o Exponential Regression – Neural Network o Geometric Regression o R MONMLP o Linear Regression o R NNet o Logarithmic Regression – Regression Analysis – Time Series Analysis o R Exponential Regression o Triple Exponential Smoothing o R Geometric Regression o R Linear Regression o R Logarithmic Regression SAP PA PAL Algorithms via HANA o R Multiple Linear Regression • K-means clustering – Time Series Analysis • Multi-linear regression o R Triple Exponential Smoothing • KNN (K Nearest Neighbor) o R Single Exponential Smoothing • Apriori o R Double Exponential Smoothing • C4.5 decision tree© 2013 SAP AG. All rights reserved. 30
  31. 31. Predictive Analysis & SAP HANA Synergies Predictive SAP Analysis HANA  Leverage the complementary capabilities of both SAP Predictive Analysis and SAP HANA  Integrated and optimized for interoperability, enabling the combination of real-time and operational analytics, access to big data, and predictive capabilities  If it’s available through SAP HANA, it can be used for data mining and predictive analysis – gain real-time access to BPC, BW, ERP, Analytic Applications, and more Prediction x Real-time + Big Data = Competitive Advantage31 © 2013 SAP AG. All rights reserved. 31
  32. 32. SAP Predictive Analysis Integration with SAP HANA -Today• Simplified UI/UX for predictive analysis in HANA• HANA as source of data for In Database Predictive Analysis • HANA table as source • HANA view as source • Attribute View • Analytical View • Calculation View• Sample and filter the data in HANA• Visualize the data in SBOP PA• HANA as source of data through JDBC • Apply algorithms on the data and perform the analysis • Visualize the results• Persist the results back to HANA as tables © 2013 SAP AG. All rights reserved. 32

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