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A Technical Guide to Leveraging Advanced
Analytics Capabilities from SAP
Charles Gadalla
SAP
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 1
Agenda
• Intro to Big Data and Analytics
• Big Data and ...
Intro to Big Data and
Analytics
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 3
Big Data — The Four Vs
Customer
Data
Automobiles
Machine...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 4
Most
Established
KPIs too
10%
75%
Use Analytics
Today
Ne...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 5
Social
In-memory
Cloud
Mobile
Real-Time
Empowerment
Expl...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 6
Harnessing the Power of Big Data
Descriptive,
Predictive...
Imagine the Business Potential …
:-)
Brand
Sentiment
360O Customer View
Product
Recommendation
Propensity to
Churn
Real-ti...
Big Data and Advanced
Analytics — Lifecycle
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 9
Big Data and Analytics — Value Chain
Data
Origins /
Prod...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 10
Big Data — Component Architecture
Data sources /
Classi...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 11
Big Data and Analytics — Cross Section
Customer
DataAut...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 12
Big Data and Analytics — Core Patterns
Real-Time
Analyt...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 13
Advanced Analytics — Lifecycle
Prepare
Explore
Discover...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 14
Data
Prepa-
ration
Data
Exploration
and Discovery
Predi...
SAP Vision and Strategy —
Advanced Analytics
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 16
How Analytics Need to Evolve to Deliver Collective
Insi...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 17
Challenges and Inefficiencies
Analysts:
Talent
Shortage...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 18
Analytics Solutions from SAP
Agile
Visualizatio
n
Advan...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 19
Advanced Analytics
Confidently Anticipate What Comes Ne...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 20
Three Types of Personas
• Create complex
predictive mod...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 21
Solutions for the Entire Spectrum of Users
Business Use...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 22
Solutions for the Entire Spectrum of Users (cont.)
Busi...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 23
Advanced Analytics — SAP Vision
Operationalize
predicti...
Advanced Analytics
Solutions from SAP
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 25
Advanced Analytics Solutions from SAP
R
Integration
SAP...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 26
Analytics Lifecycle — Tools and Personas
SAP HANA
(Plat...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 27
SAP Lumira: Visualizing Big Data
Unleash Analyst Creati...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 28
Self-Service for Data Scientists and Business Analysts
...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 29
SAP InfiniteInsight
Modeler
Build your models
Social
Fi...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 30
Reusable  Reduces Human Error  Self-Service
Prepare
C...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 31
Easy to Use  Time to Market  More Models
Build
Fully ...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 32
Put Scores into Action
One-click deployment of scores
i...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 33
Refresh analytic data sets
and models automatically
Dep...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 34
Improve Insight  Extend Reach  Boost ROI
Social
Use s...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 35
Adaptive Big Data Plug and Play
Recommend
Addresses a...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 36
Improve, Unlock, Govern, and Predict
SAP
InfiniteInsigh...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 37
In-Memory Predictive and Machine Learning
C4.5
decision...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 38
SAP HANA: Text Analytics for Big Data
File Filtering
 ...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 39
SAP HANA: Spatial Analytics for Big Data
SAP HANA
Spati...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 40
Big Data Open and Flexible Architecture
SAP
HANA
Log
fi...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 41
R Integration
Adoption by the market
 R Integration wi...
DEMO
Use Cases and Customer
Case Studies
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 44
Predictive Use Cases — Industry and LoB
•Customer
Churn...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 45
eBay – Professional Service (Internet)
American Multina...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 46
Mitsui Knowledge Industry
Healthcare – Speed Research a...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 47
Eldorado — Boosting Sales Forecast Accuracy
Business Ch...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 48
Belgacom — Reduces Churn and Increases
Customer Satisfa...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 49
Banglalink — Boosts Customer Retention
Objectives
• Imp...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 50
Groupe SAMSE — Improving Marketing,
Risk Prevention, an...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 51
Aviva: Building Predictive Models with Ease
Using SAP® ...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 52
AAA: Boosting Marketing Insight Across the
Customer Lif...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 53
Tipp24: Quadrupling Marketing Campaign
Performance with...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 54
Pirelli: Improving Safety and Cutting the Cost of
Every...
Wrap-Up
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 56
Unleash Your Collective Insight
sapbusinessobjectsbi.co...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 57
Where to Find More Information
• SAP Predictive Analyti...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 58
7 Key Points to Take Home
• Identify the entry “V”
• As...
© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Thank you
Charles Gadalla
charles.gadalla@sap.com
@cgadall...
© 2015 SAP SE or an SAP affiliate company. All rights reserved. 60
© 2015 SAP SE or an SAP affiliate company. All rights
r...
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A technical guide to leveraging advanced analytics capabilities from SAP

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sap.com/analytics - This SAPinsider #BI2015 session examines how to exploit SAP's advanced analytics solutions for big data and their associated algorithms to drive business impact.

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A technical guide to leveraging advanced analytics capabilities from SAP

  1. 1. A Technical Guide to Leveraging Advanced Analytics Capabilities from SAP Charles Gadalla SAP
  2. 2. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 1 Agenda • Intro to Big Data and Analytics • Big Data and Advanced Analytics – Lifecycle • SAP Vision and Strategy – Advanced Analytics • Advanced Analytics Solutions from SAP • Use Cases and Customer Case Studies • Wrap-up
  3. 3. Intro to Big Data and Analytics
  4. 4. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 3 Big Data — The Four Vs Customer Data Automobiles Machine Data Smart Meter Big Data Point of Sale Mobile Click Stream Social Network Location- based Data Text Data IMHO, it’s great! RFID Volume Variety Velocity Veracity
  5. 5. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 4 Most Established KPIs too 10% 75% Use Analytics Today Need Analytics by 2020 $2.01B Annual revenue increase possibility if the median Fortune 1,000 business increased the usability of its data by just 10% 1,000% Return on investment for every $1 spent on analytics Nucleus Research, Gartner, Fortune Magazine Companies Are Missing New Signals 4
  6. 6. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 5 Social In-memory Cloud Mobile Real-Time Empowerment Explosive Demand For Predictive Big Data Sensing and Responding Sentiment Intelligence Predictive Analytics Personalized Insights Real-Time Analysis Internet of Things ? Shift in Mindset Competing in Today’s Marketplace Means Leveraging All Types of Data
  7. 7. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 6 Harnessing the Power of Big Data Descriptive, Predictive and Prescriptive analytics Resources Decisions – Tactical and Strategic Moving towards: Analytics-Driven Decision Making Culture Customer Data Automobiles Machine Data Smart Meter Point of Sale MobileStructured Data Click Stream Social Network Location - based Data Text Data IMHO, it’s great! RFID
  8. 8. Imagine the Business Potential … :-) Brand Sentiment 360O Customer View Product Recommendation Propensity to Churn Real-time Demand/ Supply Forecast Predictive Maintenance Fraud Detection Network Optimization Insider Threats Risk Mitigation, Real-time Asset Tracking Personalized Care MANU- FACTUR- ING RETAIL CPG HEALTH CARE BANKING UTILITIES TELCO PUBLIC SECTOR 25+ Industries MARKET- ING SALES FINANCE HR OPERA- TIONS SERVICE IT SUPPLY CHAIN FRAUD / RISK 11+ LoB
  9. 9. Big Data and Advanced Analytics — Lifecycle
  10. 10. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 9 Big Data and Analytics — Value Chain Data Origins / Producers Data Sources Classificati on Data Storage Data Integration Analytics Consumers
  11. 11. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 10 Big Data — Component Architecture Data sources / Classification Meta data Master data Transaction- al data Weblog Social networks Data storage and Processing RDBMS NoSQL Distributed File Systems Files - semi- structured, unstructured Images, Audio/Video Data Integration/ Quality Connectors ETL Messaging CDC Analytics Advanced Analytics Map Reduce Consumers BI Business Processes LoB/ Industry Applica- tion Data Discovery Big Data and Analytics Governance Warehouse Data Producers Enterprise IT Systems Machines Devices Sensors Media Internet Sensors Big Data – Smart Applica- tionIn Memory
  12. 12. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 11 Big Data and Analytics — Cross Section Customer DataAutomobiles Machine Data Smart Meter Point of Sale Mobile Click Stream Social Network Location - based Data Text Data IMHO, it’s great! RFID Structured Unstructured Semi- Structured Data Sources Format Advanced Analytics Data Discovery Query & Reporting Frequency Processing Continuous Real Time On Demand Analysis Type Real Time Near Real Time Batch
  13. 13. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 12 Big Data and Analytics — Core Patterns Real-Time Analytics Near Real- Time or Interactive Analytics Pure Batch High Low
  14. 14. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 13 Advanced Analytics — Lifecycle Prepare Explore Discover PredictModel Operatio nalize Optimize Validate
  15. 15. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 14 Data Prepa- ration Data Exploration and Discovery Predict, Model and Validate Extend – App Dev., Partner, Developer Community Operationalize - Deploy, Manage, Monitor and Optimize Evaluate and Decide Personas in Advanced Analytics Lifecycle Business Analyst (Horizontal) Business Analyst (Vertical) Data Scientist Data Miner/ Statistician Application Developer IT System Admins Business Manager
  16. 16. SAP Vision and Strategy — Advanced Analytics
  17. 17. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 16 How Analytics Need to Evolve to Deliver Collective Insights Raw Data Cleaned Data Standard Reports Ad Hoc Reports & OLAP Agile Visualization Predictive Modeling Optimization What happened? Why did it happen? What will happen? What is the best that could happen? UserEngagement Maturity of Analytics Capabilities Self Service BI Generic Predictive Analysis End-to-end Easy adoption Fast implementation Business focused Enable storytelling CollectiveInsight
  18. 18. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 17 Challenges and Inefficiencies Analysts: Talent Shortage Fragmented Point Solutions Usability Shortcomings Lack of Visualization Model Proliferation High Latency Operational Datastore Sensors Mobile Archives Social & Text Order Processing Operational Reporting RT Risk & Fraud Trend Analysis Sentiment Analytics Predictive Analytics Pattern Recognition Spatial Processing Analyze Data Stores Integrate/Load Staging Collect Clean-Data Quality Transact Report Explore Communicate Monitor Predict Planning 0 1 Data Warehouse Geo- Spatial Cache Cache Cache Cache CacheCache Business & IT: Segregated Organization Structure Lack of Decision Support Lack of Data Governance
  19. 19. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 18 Analytics Solutions from SAP Agile Visualizatio n Advanced Analytics Big Data Mobile Collaboration Cloud Enterprise Business Intelligence
  20. 20. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 19 Advanced Analytics Confidently Anticipate What Comes Next to Drive Better Business Outcomes Universally apply advanced analytics to information, processes and applications to optimize actions Make sophisticated advanced analytics easy to use for a broad spectrum of users Predict and act in real time on Big Data PREDICT
  21. 21. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 20 Three Types of Personas • Create complex predictive models and simulations • Validate predictive business requirements • Publish results back to source Data Scientist 0.1% Representative User Base • Transform and enrich data source(s) • Create simple predictive models and simulations • Visualize results and publish to BI Platform Data Analysts ~3% 97% Executives/ Business Users • Interact with published predictive analysis • Visualize results in context of use case • Collaborate with colleagues toward closure/action
  22. 22. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 21 Solutions for the Entire Spectrum of Users Business Users & LOB Data Scientist Business Analysts Level of Skill Set – Analytics Low HighNo 97% 3% >0.1%
  23. 23. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 22 Solutions for the Entire Spectrum of Users (cont.) Business Users & LOB Data Scientist Business Analysts Level of Skill Set – Analytics Low HighNo 97% 3% >0.1% Embedded Analytics Industry & Business Process Analytics Custom Analytics SAP Lumira SAP InfiniteInsight (KXEN) SAP Predictive Analysis SAP PAL R Integration SAP ADVANCED ANALYTICS
  24. 24. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 23 Advanced Analytics — SAP Vision Operationalize predictive and optimization models across the enterprise Reduce Decision Latency with Advanced Analytics Bringing Predictive Analytics to a broad spectrum of users Embed Smart Agile Analytics into Decision Processes to Deliver Business Impact Easy Fast Efficient
  25. 25. Advanced Analytics Solutions from SAP
  26. 26. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 25 Advanced Analytics Solutions from SAP R Integration SAP HANA Search Rules Engine Text Mining Predictive Analysis Library Business Function Library Spatial SAP Lumira SAP InfiniteInsight (KXEN) SAP Predictive Analysis SAP Predictive Analytics +
  27. 27. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 26 Analytics Lifecycle — Tools and Personas SAP HANA (Platform) Data Preparation Data Exploration & Discovery Predict, Model & Validate Extend App Dev., Partner, Developer Community Operationalize Deploy, Manage, Monitor & Optimize Evaluate & Decide SAP HANA Studio SAP Lumira SAP Predictive Analysis and SAP InfiniteInsight SAP HANA Studio AFM SAP HANA Studio Personas in Analytics Lifecycle (Illustrative)Business Analyst (Vertical) Data Scientist Business Analyst (Horizontal) Data Miner/Statistician Application Developer IT Systems Admin Business Manager
  28. 28. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 27 SAP Lumira: Visualizing Big Data Unleash Analyst Creativity Provides the freedom to understand your data, personalize it, and create beautiful content  Download and install on your desktop in less than five minutes  Insight from many data sources  Combine, manipulate, and enrich data to apply it to your business scenarios  Self-service visualizations and analytics to tell your story  Optimized for SAP HANA for real time on detailed data Self-Service for Analysts 27
  29. 29. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 28 Self-Service for Data Scientists and Business Analysts Provide Data Scientist and Business Analysts with sophisticated algorithms to take the next step in understanding their business and modeling outcomes  Perform statistical analysis on your data to understand trends and detect outliers in your business  Build models and apply to scenarios to forecast potential future outcomes  Breadth of connectivity to access almost any data  Optimized for SAP HANA to support huge data volumes and in-memory processing
  30. 30. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 29 SAP InfiniteInsight Modeler Build your models Social Find your influencers Scorer Deploy your scores Factory Improve your models Explorer Prepare your data Recommendation Personalized recommendations
  31. 31. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 30 Reusable  Reduces Human Error  Self-Service Prepare Create 1,000s of derived attributes Define metadata once Select time-stamped population Builds analytic dataset automatically Analytical Data Sets with Clicks Not Code
  32. 32. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 31 Easy to Use  Time to Market  More Models Build Fully automated modeling process • Regression • Classification • Segmentation • Time series forecasting • Association rules Identify key variables Executive and operational reports Predictive Power in Days Not Months
  33. 33. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 32 Put Scores into Action One-click deployment of scores into production In-database scoring (SQL) Interface with business apps via scoring equations in: • Java • PMML • SAP HANA • Many more Non-Intrusive  Time to Value  Repeatable Deploy
  34. 34. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 33 Refresh analytic data sets and models automatically Deploy scores to production Alert on data and model deviations No Programming  Scale  Manage By Exception Improve Every Model at Peak Performance
  35. 35. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 34 Improve Insight  Extend Reach  Boost ROI Social Use social variables for enhanced prediction Identify communities amongst your customers Find influencers to make your campaigns viral Improve Insight with Social Networks
  36. 36. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 35 Adaptive Big Data Plug and Play Recommend Addresses any type of business questions Make product recommendations, targeting digital content Social recommendations (e.g., friends) and targeted ads Personalize the Recommendations
  37. 37. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 36 Improve, Unlock, Govern, and Predict SAP InfiniteInsight SAP Business Suite, Success Factors, RDBMS, 3rd party Apps Text and Binary Files, XML, Excel, JMS, Web Sources Hadoop/Hive SAP Data Services Native support for 40+ sources & interfaces SAP HANA (SAP In-memory computing) SAP Sybase IQ • Connectivity • Transformations • Quality SAP Predictive Analysis
  38. 38. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 37 In-Memory Predictive and Machine Learning C4.5 decision tree Weighted score tables Regression ABC classification Spatial, Machine, Real-time data Hadoop/Sybase IQ, Sybase ASE, Teradata Unstructured PAL R-scripts SQL Script Optimized Query Plan Main Memory Virtual Tables Spatial Data R-Engine KNN classification K- means Associate analysis: market basketText Analysis SAP HANA HANA Studio/AFM, Apps & Tools
  39. 39. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 38 SAP HANA: Text Analytics for Big Data File Filtering  Unlock text from binary documents  Ability to extract and process unstructured text data from various file formats (txt, html, xml, pdf, doc, ppt, xls, rtf, msg)  Load binary, flat, and other documents directly into HANA for native text search and analysis Native Text Analysis  Give structure to unstructured textual content  Expose linguistic markup for text mining uses  Classify entities (people, companies, things, etc.)  Identify domain facts (sentiments, topics, requests, etc.)  Supports up to 31 languages for linguistic mark-up and extraction dictionary and 11 languages for predefined core extractions SAP HANA Text & Sentiment Analysis SearchAnalyze Predict
  40. 40. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 39 SAP HANA: Spatial Analytics for Big Data SAP HANA Spatial Processing Real-time Spatial Processing High-performance algorithms analyze massive amounts of spatial data in real time Mobility Visualization Analytics HTML 5 GIS Applications Spatial Analytics Optimization Columnar storage architecture eliminates need to create spatial indexes, tessellation, or other optimization techniques Geo-content & services Maps, geo-content, and geospatial services for seamless application development and deployment Spatial Data Types & Functions Store, process, manipulate, share and retrieve spatial data directly in the database Business Data + Spatial Data + Real-time Data Geo – Services - Geocoding - Base maps Geo – Content - Political Boundaries - POIs - Roads Columnar Spatial Processing Calc Model / Views - Joins - Views Spatial Functions - Area - Distance - Within Spatial Data Types - Points - Lines - Polygons Transact- ion Data Unstructur ed Data Location Data Machine Data
  41. 41. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 40 Big Data Open and Flexible Architecture SAP HANA Log files Unstructured files Data loading for Pre-process Load results into SAP HANA SAP Sybase IQ (Data Services) Query Federation Smart Query Access (Data Virtualization) SAP Sybase IQ Integration at ETL layer  Data Services provides bi-directional SAP Hadoop connectivity: HIVE, HDFS, Push down entity extraction to Hadoop as MapReduce jobs  ETL data into SAP Sybase IQ Direct SAP HANA-Hadoop connectivity  Virtual Table (SAP HANA smart data access) – Virtual HANA table to federate a Hive table at query time  HCatalog integration – Leverage Hadoop metadata to improve query performance, e.g. partition pruning in Hadoop before executing query  Query federation with SAP Sybase IQ SAP BI connectivity  SAP BOBJ multi- source Universe can access Hadoop HIVE SAP Predictive Analysis and SAP InfiniteInsight
  42. 42. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 41 R Integration Adoption by the market  R Integration with SAP Predictive Analysis  Drag and Drop – No Coding  Custom R Algorithms – Programming  Access to over 5,000+ algorithms and packages  More algorithms and packages than SAS + SPSS + Statsoft  Embedding R scripts within the SAP HANA database execution
  43. 43. DEMO
  44. 44. Use Cases and Customer Case Studies
  45. 45. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 44 Predictive Use Cases — Industry and LoB •Customer Churn/ Retention •Cross- Sell/Upsell •Campaign Management •Lifetime Value •Pricing Optimization •Product Launch Success •Brand Sentiment and Sales Analytics •Cross/Up Sell •Product Launch Success •Brand Sentiment and Sales Analytics •Regional Forecasting •Brand Sentiment and Sales Analytics •Next Best Activity •Cross Sell/Upsell •Churn Reduction •Customer Segmentation •Brand Sentiment and Sales Analytics •Brand Sentiment and Sales Analytics •Credit Risk •Fraud Management and Prevention •Credit Scoring •Fraud Management and Prevention •Optimizing Product Quality •Credit Scoring •Compliance •Retail Outlier •Fraud Management and Prevention •Optimizing Product Quality •Credit Scoring •Compliance •Fraud Management and Prevention •Optimizing Product Quality •Credit Scoring •Underwriting •Default/bankruptcy Risk •Tax Fraud •Credit Card Fraud •Insurance Fraud •Predictive Asset Maintenance •Fraud Management and Prevention •Optimizing Product Quality •Anomaly Detection •Usage Forecasting •Customer Segmentation •KPI Forecasting •Anomaly Detection •Usage Forecasting •Store Segmentation •In-Store Workforce Optimization •Size and Zone Optimization •Market Share Prediction •KPI Forecasting •Anomaly Detection •Usage Forecasting •KPI Forecasting •Anomaly Detection •Usage Forecasting •KPI Forecasting •Anomaly Detection •Usage Forecasting •KPI Forecasting •Anomaly Detection •Usage Forecasting •Variable Margin Analysis •Yield Management •Equipment Effectiveness •Labor Utilization •Out of Stock Prediction •Demand Forecasting •Inventory and Logistics Planning •Out of Stock Prediction •Inventory and Logistics Planning •Out of Stock Prediction •Inventory and Logistics Planning •Predictive Commodity Management •Improving Demand Planning and Inventory Management Retail CPG Financial Services ManufacturingTelecom E-Business Customer/ Marketing Fraud/ Risk Operations Supply Chain
  46. 46. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 45 eBay – Professional Service (Internet) American Multinational Internet Consumer-to-Consumer Corporation Product: Early Signal Detection System Powered by Predictive Analytics on SAP® HANA Business Challenges/ Objectives  Increase ability to separate signal from noise to identify key changes to the health of eBay’s marketplace  Improve predictability and forecast confidence of eBay’s virtual economy  Increase insights into deviations and their causes Technical Challenges  Detect critical signals from 100 PBs of data in eBay EDW  Highly manual process because one model does not fit all the metrics hence requires analyst intervention Benefits  Automated signal detection system powered by predictive analytics on SAP HANA selects best model for metrics automatically; increases accuracy of forecasts  Reliable and scalable system provides real-time insights allowing data analysts to focus on strategic tasks  Decision tree logic and flexibility to adjust scenarios allows eBay to adapt best model for their data “HANA is valuable in the sense that it accelerates that speed to insight. HANA, with in-memory capability, with multicore, fast, lots of data, all of that coming together is how I think analytics is going to work broadly in the future.” - David Schwarzbach, VP&CFO eBay North America at eBay Inc. “HANA system will free up all the bandwidth right now involved in figuring out what is going. The user just has to feed in their metric, doesn’t have to really worry about which algorithm is the best and be able to use the system because it is inherently intelligent and configurable.” - Gagandeep Bawa, Manager, North America FP&A at eBay Inc. “ ” Determine with 100% Accuracy that a signal is positive at 97% confidence Automated Early Signal Detection system powered by SAP HANA
  47. 47. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 46 Mitsui Knowledge Industry Healthcare – Speed Research and Improve Patient Support Business Challenges  Reduce delays and minimize the costs associated with new drug discovery by optimizing the process for genome analysis  Improve and speed decision making for hospitals which conduct cancer detection based on DNA sequence matching Technical Implementation  Leveraged the combination of SAP HANA, R, and Hadoop to store, pre- process, compute, and analyze huge amounts of data  Provide access to breadth of predictive analytics libraries Benefits  For pharmaceutical companies, provide required new drugs on time and aid identification of “driver mutation” for new drug targets  Able to provide a one stop service including genomic data analysis of cancer patients to support personalized patient therapeutics Our solution is to incorporate SAP HANA along with Hadoop and R to create a single real-time big data platform. With this we have 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 408,000x faster than traditional disk- based systems in a technical PoC 216x faster by reducing genome analysis from several days to only 20 minutes making real-time cancer/drug screening possible “ ”
  48. 48. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 47 Eldorado — Boosting Sales Forecast Accuracy Business Challenges/Objectives  Analyze data stored in the SAP® 360 Customer solution from over 1.5 million point- of-sale transactions for more than 420 product groups and sales of over 8,000 products each month  Improve forecast precision to boost sales and reduce inventory costs Benefits  Building approximately 500 predictive models a month, a task impossible with traditional modeling techniques that required weeks or months to build a single model  Creating forecasts for assortment planning, shelf replenishment, pricing and promotion analysis, store clustering, store location selection, and sales and purchasing planning  Achieving up to 82% accuracy in sales forecasts, a 10% improvement over prior forecasting techniques “SAP InfiniteInsight has given us a scalable approach to create accurate forecasts across our business” Elena Zhukova, Head of Analytics, Eldorado LLC “ ” 82% Accuracy in Sales Forecast 500+ predictive models per Month
  49. 49. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 48 Belgacom — Reduces Churn and Increases Customer Satisfaction Business Challenges/Objectives  Leverage previously unseen customer insights to reduce customer churn and identify new revenue opportunities  Enhance churn detection, speed up deployment for predictive models, and identify revenue potential across the customer lifecycle Benefits  Enables next-best-action marketing across all channels, from call centers to the Web to retail stores  Optimizes interactions throughout the complete customer relationship, revealing previously unseen customer insights  Identifies market gaps, turning them into revenue  Increases customer satisfaction and reduces customer churn  Raises return on marketing investments  Accelerates modeling time from months to days “ ” Modeling time reduced from months to days 4x increase in campaign response rates “With SAP InfiniteInsight, we can deliver the right offer to the right customer at the right time. It’s a real competitive advantage. We’re getting the most out of our marketing dollars and a higher return on our marketing investments.” Filip Deroover, Business Intelligence Specialist, Belgacom Group
  50. 50. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 49 Banglalink — Boosts Customer Retention Objectives • Improve retention campaign results to combat customer churn • Analyze Big Data coming from sources such as call detail records, product subscriptions, voucher transactions, package conversions, and cell site locations Why SAP • Supports intuitive building of predictive models, even for users with no or little experience in data science or statistics • Includes prepackaged predictive models and a predefined analytical data architecture to accelerate the time required to prepare analytical data, build predictive models, and deploy resulting scores into production Benefits  Enabled a model to detect more than a quarter of all future churners with only a 10% sample of the highest scores  Deployed SAP® InfiniteInsight® solution within five months  Gained the tools to build and deploy predictive models in hours, as opposed to weeks or months “Using SAP InfiniteInsight, we are able to build customer loyalty through targeted retention programs which drive hard-line results to our business.” Nizar El-Assaad, CIO, Banglalink Digital Communications Ltd. “ ” 55% of future churners within 5% of all subscribers Predictive models in hours as opposed to weeks or Month
  51. 51. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 50 Groupe SAMSE — Improving Marketing, Risk Prevention, and Inventory Forecasting Business Challenges/Objectives  Boost marketing campaign performance, risk prevention, and inventory forecasting across 25 brands and 290 sales outlets  Analyze terabytes of data on over 300,000 loyalty cardholders and 30,000 enterprise customers each day  Build and analyze a 360-degree view of both business-to-business and business-to- customer relationships  Update predictive models weekly, rather than monthly, to ensure timely predictions Benefits  Response rate to direct marketing campaigns up by 220% • Predictive models that require just a week, rather  than months, to update  Balance between systematic and flexible exploration of daily data across group brands using predictive models  Early-warning system for individual customer construction projects, enabling personalized product recommendations in near-real time across multiple customer- facing channels, including retail outlets, call centers, and sales “SAP InfiniteInsight has helped uncover dependable patterns and insight that were previously unattainable.” Corentin Jouan, Head of Business Intelligence, Groupe SAMSE “ ” 220% increase in marketing campaign responses Predictive models that require just a week, rather than months
  52. 52. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 51 Aviva: Building Predictive Models with Ease Using SAP® InfiniteInsight® Objectives  Leverage predictive analytics to build propensity models for individual customer groups rather than build generic models for all customers  Avoid contacting customers too frequently, while also improving campaign response rates  Increase return on marketing and campaign response rates by identifying customers most likely to respond Why the SAP® InfiniteInsight® solution  Charts that help marketing experts visualize the anticipated business impact of models  Significantly better modeling automation that allows many models to be built with ease  Automatic analysis of the individual contributions of hundreds of variables to a model, rather than manual inspection of a limited number of variables Future plans  Further improve return on marketing with uplift modeling that predicts the impact of marketing activities on specific target groups  Build predictive models to analyze customer acquisition and win-back "Modeling made easy – thanks to SAP InfiniteInsight.” Dr. Margaret Robins, Statistical Analyst, Data Analytics and Insight, Aviva plc Personalized Further improve return on marketing with uplift modeling that predicts the impact of marketing activities on specific target groups Efficient Significant increase in the number of propensity models used within the company, with more than 30 models in production Current Ability to use the freshest data to keep models up-to- date and capture the latest trends 30599 (14/05) This content is approved by the customer and may not be altered under any circumstances.
  53. 53. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 52 AAA: Boosting Marketing Insight Across the Customer Lifecycle with SAP® InfiniteInsight® Objectives  Optimize marketing insight across all stages of the customer lifecycle  Provide a more powerful and centralized means of analyzing customer information and optimizing marketing across motor clubs  Establish a cost-effective, easy-to-access approach to predictive analytics Why SAP  Standard reporting features of the SAP® InfiniteInsight® solution, including modeling results, variable contributions, and gain charts, that club marketing teams can easily understand  Ability to provide collective insight to clubs about members most likely to benefit from the association’s wide range of offerings  Scalability of predictive models that can be managed by just two business analysts across multiple motor clubs Benefits  Optimized marketing across channels for nearly 70% of members  Enabled custom offers to fit individual member interests and needs  Cut attrition and increased overall customer lifetime value by extending targeted offers to members with low usage  Earned millions of dollars in sales, thanks to optimized marketing campaigns for some clubs "SAP InfiniteInsight helps us put the right products and services in front of members at the right time.“ Daniel Mathieux, Member Insights and E-Business, American Automobile Association (AAA) Optimized Marketing campaigns across channels for nearly 70% of members Customized Enabled custom offers to fit individual member interests and needs Loyal Cut attrition and increased overall customer lifetime value by extending targeted offers to members with low usage Valuable Earned millions of dollars in sales, thanks to optimized marketing campaigns for some clubs 28759 (13/12) This content is approved by the customer and may not be altered under any circumstances.
  54. 54. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 53 Tipp24: Quadrupling Marketing Campaign Performance with SAP® InfiniteInsight® Top objectives  Better understand the customer lifecycle to nurture high-value customers, increase up- sell and cross-sell opportunities, and reduce churn  Gather detailed customer behavior data to optimize marketing campaigns  Enable efficient predictive modeling across all marketing activities and customer channels Why the SAP® InfiniteInsight® solution  Better performance and scalability when compared to SAS software and SPSS software from IBM  Ability to identify customer behavior patterns to improve satisfaction  Ability to predict which customers are at risk of becoming inactive and which inactive customers are likely to become active again Key benefits  Optimizes campaigns and the customer lifecycle across multiple channels, including telephone, direct mail, and e-mail  Enables proactive relationship management with existing and potential high-value customers  Reduces churn and increases overall customer lifetime value “In our first year using SAP InfiniteInsight, we realized a 300% uplift in targeting accuracy.” Pankaj Arora, Senior Analytics Consultant, Tipp24.com 300% Improvement in targeting accuracy, including identifying likely players for weekly, monthly, or permanent tickets for specific lotteries 25% Reduction in target audience size for any individual campaign, thanks to more-precise analytics 90% Less time to build and deploy predictive models (from weeks to days), increasing the productivity of the analytics team 30153 (14/08) This content is approved by the customer and may not be altered under any circumstances.
  55. 55. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 54 Pirelli: Improving Safety and Cutting the Cost of Every Customer’s Commute with SAP HANA® Business Challenges  Allow Pirelli to deliver new services to fleet managers to monitor tire usage and predict maintenance needs  Provide timely information on monthly costs, profitability, sales and distribution, and supply chain management  Process and analyze large volumes of tire data in real time to predict diagnostic and maintenance work requirements Technical Implementation  Installed tire sensors to collect pressure and temperature data that can be transmitted to the driver, fleet manager, or dealer  Centralized data from sensors, GPS devices, and customer records  Enabled processing and analysis of data from 600 fleets with 1,000 assets (trucks and trailers) each with the SAP HANA platform, providing real-time data updates every 1–2 minutes for 16 hours per day, 6 days per week and resulting in 40 billion data events per year Key benefits  Increased competitiveness and innovation using cutting-edge technology  Increased customer satisfaction, thanks to proactive tire maintenance, improved safety, and lower costs associated with greater fuel efficiency and longer tire lifespan “With SAP HANA, Pirelli can capture, store, and analyze data from multiple fleets to discover new insights. For example, we can correlate street conditions, climate, and local practices, then use that insight to improve product quality and performance.” Daniele Benedetti, Applicative Architectures – Integration and Innovation, Pirelli & C. SpA >40 billion Events analyzed per year Up to 3% Up to 20% Lower fuel and tire costs Extended tire lifespan
  56. 56. Wrap-Up
  57. 57. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 56 Unleash Your Collective Insight sapbusinessobjectsbi.com sap.com/predictivesaplumira.com ENGAGE PREDICTVISUALIZE Real-Time Platform saphana.com
  58. 58. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 57 Where to Find More Information • SAP Predictive Analytics • www.sap.com/pc/analytics/predictive-analytics.html • www.sap.com/pc/analytics/predictive-analytics/software/infiniteinsight/lob- industry/overview.html • https://help.sap.com/ii_re • https://help.sap.com/pa10 • http://marketplace.saphana.com/Industries/Industrial-Machinery-%26-Components/SAP- Predictive-Analysis/p/3527 • SAP HANA • www.saphana.com/community/about-hana/advanced-analytics • www.saphana.com/community/hana-academy • https://help.sap.com/hana_platform/ • SAP Big Data • www.sapbigdata.com/
  59. 59. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 58 7 Key Points to Take Home • Identify the entry “V” • Assess current capabilities against what’s required • Get the initial project, move iteratively • Find the compelling use case where Advanced Analytics can help • Leverage advanced analytics from SAP to drive value out of Big Data • Download the SAP Predictive Analytics 30-day trial • Predict and act in real time on Big Data
  60. 60. © 2014 SAP SE or an SAP affiliate company. All rights reserved. Thank you Charles Gadalla charles.gadalla@sap.com @cgadalla © 2015 SAP SE or an SAP affiliate company. All rights reserved.
  61. 61. © 2015 SAP SE or an SAP affiliate company. All rights reserved. 60 © 2015 SAP SE or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additional trademark information and notices. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.
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