sap analytics innovation tour: predictive analysis showcase

SAP Predictive Analysis Transform Your Future with Predictive Insight Charles Gadalla, Solution Management February 2013

© 2013 SAP AG. All rights reserved. 2

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Why predictive now?

3

Changing landscapes and new opportunities

Increased Business Interest

Answer more sophisticated questions

Resolve real-time problems

Exploding data volume Expanding data varieties Invest in data to get value

Increasing Technology Performance

Create efficient business models

Reduce data processing time

Increased Data Value (Big Data)


Extend your analytics capabilities where you want to be…

Sense & Respond Predict & Act

Raw Data

Cleaned Data

Standard Reports

Ad Hoc Reports &

OLAP

Generic Predictive Analytics

Predictive Modeling

Optimization

What happened?

Why did it happen?

What will happen?

What is the best that could happen?

Com

petit

ive

Adv

anta

ge

Analytics Maturity

The key is unlocking data to move decision making from sense & respond to predict & act


SAP in the Leader’s quadrant!

We’ve come a long way in 6 months…

…from standing start to LEADER!


SAP’s Predictive Analytics Strategy

Real-time in-memory predictive and next generation visualization and modeling

Empower the Business

Extend the Business Intelligence competency to Advanced Analytics

Embed Predictive into Apps and BI environments

Lend expertise

In-memory processing No data latencies Big Data ready

In Context

Relevant to your business Within the context of your

Industry and LOB scenario

In-time Actionable Insights


Where SAP has helped customers with Predictive Analytics in Industry

Retail and Consumer Products

Demand forecasting and insights Market basket insights and KVI Markdown (and price)

optimization Size and zone optimization Inventory Optimization Promotional Assessments Identify geographic trends and

performance

Financial Services

Price optimization Product and portfolio optimization Market segmentation Corporate and credit risk

management Customer retention,

segmentation Cross- and up-selling, customer

lifetime value

Manufacturing & Utilities

Demand simulation for configurable products

Supply chain optimization Load demand modeling and

forecasting Smart Energy Meter analytics Customer service, customer

lifetime value Asset efficiency: spare parts,

outages, inventory, risk

High Tech & Telco

Sales Forecasting, Enablement and category management

Customer experience Buyer classification Demand insights

Fraud, Waste and Abuse discovery Analytics enhancement for billing Crime trends and At Risk analytics Predict community movement within

taxing districts Predict likelihood of disease Identify clinical trial outcomes

Public Sector & Healthcare


How SAP has helped customers with Predictive Analytics in Industry Retail and Consumer Products

10.5M accounts clusterered and segmented within 90 seconds for greater promotional insights

Sell larger basket sizes by identifying products that drive drag-along sales

Reduction of returned goods by 40% led to yearly savings of ~ $50M

Improve revenue and net margin by accelerating profitable merchandising initiatives

High Tech & Telco

React faster and more appropriately to the causes of customer churn

Improved quality while meeting strict timelines within budget by realigning resources to projects that complement their skill sets

Financial Services

Significant improvements to retention and reduction in attrition

Improved revenue and net margin by accelerating profitable marketing initiatives by targeting under-served customer segment

Fraud detection and risk management

Manufacturing & Utilities

Reduced time, effort and costs to develop new products

Reduction in defective products Manufacturing process

improvements Energy trading and grid demand

planning Major improvements to energy

demand and resource requirements

Improved taxpayer compliance and revenue collection by +10%

Improve revenue collection levels, discourage fraudulent behavior with better prediction and investigation and contribute to the reduction of budget deficits

Significant savings by more quickly and precisely identifying fraud

Reduction in compliance and policy audits

Public Sector & Healthcare

Success stories


Coinstar

• Inventory Optimization

• Real time Offers

• Servicing


Mitsui Knowledge Industry Healthcare – Speed Research & 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

“ ”


Bigpoint Gaming Industry - Predictive Game Player Behavior Analysis

Business Challenges Increase conversion rates from free paying player Increase the average revenue per paying player Decrease churn – keep paying players playing longer

Technical Challenges Leverage real-time data processing in SAP HANA and

classification algorithms with R integration for SAP HANA to deliver personalized context-relevant offers to players

Analyze vast amounts of historical and transactional data to forecast player behavior patterns

Benefits Real-time insights Per player profitability analysis and increased understanding of

player behavior 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 to load in SAP HANA environment and to work on it to create an individualized game environment to create offers for them. In this co-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

“ ”

5,000 events per second loaded onto SAP HANA (not possible before)

10-30% increase in revenue per year

Interactive data analysis leading to improved design thinking and game planning

SAP Predictive Analysis Solution Overview


Visualize, 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

SAP Predictive Analysis


SAP Predictive Analytics Solution

SAP Predictive Analytics Solution

RDBMS, IQ, BW, Universes, XLS…

Real Time Execution Environment

develop & score in-memory Powered by HANA


modern design, model, visualize

Applications (Industry & LOB) Customer Analytics, Affinity Insight / Unified Demand Forecast for

Retail, Smart Meter Analytics,

Expertise – P

IO, P

artners


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

R is a software environment for statistical computing and graphics

http://www.r-project.org/index.html


Options to use SAP Predictive Analysis


SAP HANA Predictive Ecosystem

SAP HANA Platform

Data Pre-Processing and Loading SAP Data Services, Information Composer, SLT, DXC, Hadoop

Predictive Analysis Library

(PAL) SAP HANA Studio

SAP and Custom

Applications

R Integration for SAP HANA

Business Intelligence

Clients

R



Predictive Analytics - Roadmap & Resources


Predictive Analysis Roadmap – Key Themes


SAP HANA PAL Roadmap*

*These are planned dates and features only and not firm commitments

• HANA PAL SPS06 late Q2 2013*

2013

SPS05

(export)

(import)


Thank You!

Contact information: [email protected] @CGadalla


Appendix


R Integration for SAP HANA Embedded Scenario Embedding R scripts within the SAP HANA database

execution Enhancements are made to the SAP HANA database

to allow R code (RLANG) to be processed as part of the overall query execution plan

This scenario is suitable when the modeling and consumption environment sits on HANA and the R environment is used for specific statistical functions

Send data and R script 1

2 Run the R scripts 3

Get back the result from R to SAP HANA

CREATE FUNCTION LR( IN input1 SUCC_PREC_TYPE, OUT output0 R_COEF_TYPE) LANGUAGE RLANG AS''' CHANGE_FREQ<-input1$CHANGE_FREQ; SUCC_PREC<-input1$SUCC_PREC; coefs<-coef(glm(SUCC_PREC ~ CHANGE_FREQ, family = poisson )); INTERCEPT<-coefs["(Intercept)"]; CHANGEFREQ<-coefs["CHANGE_FREQ"]; result<-as.data.frame(cbind(INTERCEPT,CHANGEFREQ)) '''; TRUNCATE TABLE r_coef_tab; CALL LR(SUCC_PREC_tab,r_coef_tab ); SELECT * FROM r_coef_tab;

Sample Code in SAP HANA SQLScript


SAP HANA – Hadoop, Sentiment Analysis Integration

Pre-process data in Hadoop

Load results into HANA

SAP HANA SP5

Hadoop

Log files

Visualize HANA data in SAP Business Objects BI Data Services

4.1

BusinessObjects BI

Visualize HIVE data in SAP Business Objects BI

unstruct

ured data

Text Analysis 31 languages Entity extraction Sentiment analysis


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


List of Algorithms in SAP Predictive Analysis 1.0.7

SAP PA Native Algorithms – Outlier Detection Analysis o Inter Quartile Range o Nearest Neighbor Outlier

– Regression Analysis o Exponential Regression o Geometric Regression o Linear Regression o Logarithmic Regression

– Time Series Analysis o Triple Exponential Smoothing

SAP PA with R (out of the box) – Association Analysis o R-Apriori

– Segmentation Analysis o R K-Means

– Decision Tree o R CNR Tree

– Neural Network o R MONMLP o R NNet

– Regression Analysis o R Exponential Regression o R Geometric Regression o R Linear Regression o R Logarithmic Regression o R Multiple Linear Regression

– Time Series Analysis o R Triple Exponential Smoothing o R Single Exponential Smoothing o R Double Exponential Smoothing

SAP PA PAL Algorithms via HANA

• K-means clustering • Multi-linear regression • KNN (K Nearest Neighbor) • Apriori • C4.5 decision tree


Predictive Analysis & SAP HANA Synergies

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Predictive Analysis

Prediction x Real-time + Big Data = Competitive Advantage

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

SAP HANA


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

sap analytics innovation tour: predictive analysis showcase

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