Tech 2014 PAL - APL

1. Public
- Narasimha Rao Addanki /Customer Experience Group
- Ravin Angara /Customer Experience Group
EA261 – Extend Analytics to
predict trends, anticipate change,
and drive strategy

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Disclaimer
This presentation outlines our general product direction and should not be relied on in making a
purchase decision. This presentation is not subject to your license agreement or any other agreement
with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to
develop or release any functionality mentioned in this presentation. This presentation and SAP's
strategy and possible future developments are subject to change and may be changed by SAP at any
time for any reason without notice. 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
particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this
document, except if such damages were caused by SAP intentionally or grossly negligent.

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Agenda
Introduction
 Overview of SAP Predictive Analysis
 SAP Portfolio of Solutions and positioning of Predictive Analysis
 SAP Predictive Analysis Architecture and Process
Hands on Exercises

4. Introduction
Overview of SAP Predictive Analysis

5. What is Predictive Analysis
 Quite Simply - “ It is quantitative analysis to support predictions. For
example :-Product sales, cost , head count, customer churn, credit scoring,
cross sell/upsell opportunities, anomalies, fraud etc.
 Predictive Analysis comprises of primarily statistical Analysis and data
mining and but can also include methods and techiniques of operations
research
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6. SAP 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%Embedded Analytics
Industry & Business
Process Analytics
Custom
Analytics
SAP Lumira SAP InfiniteInsight SAP Predictive Analysis
Predictive in
SAP HANA
R
Integration
SAP ADVANCED ANALYTICS
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7. 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
+
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8. Introduction
SAP portfolio of solutions and Positioning of Predictive Analysis

9. SAP Lumira
Provides the freedom to understand your data,
personalize it, and create beautiful content
• Download and install on your desktop in
less than 5 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
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10. SAP Predictive Analysis
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
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11. SAP InfiniteInsight
Provide Business analysts and Data
scientists with a fully automated process
Data preparation
 Create 1000’s of derived attributes
 Define metadata once
 Builds analytic dataset automatically
Predictive modeling / Data mining
 Regression / Classification
 Segmentation
 Forecasting
 Association rules
 Social Network Analysis
Advanced model deployment and management
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 11Public

12. Predictive in SAP HANA
Build High Performance Predictive Apps
 The Predictive Analysis Library (PAL) is a built-
in C++ library to perform in-memory data mining
and statistical calculations, designed to provide
high performance on large data sets.
 The Predictive Analysis Library provides high
performance analytic algorithms aimed for real-
time analytics
 Currently over 60+ algorithms and many in
pipeline for future roadmap
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
basket
Text Analysis
SAP HANA
HANA Studio/AFM,
Apps & Tools
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13. R Integration
R - Integration with SAP Predictive Analysis
 Drag and Drop – No Coding
 Custom R Algorithms – Programming
Access to over 5000+ algorithms and
packages
More algorithms and packages than SAS +
SPSS + Statsoft
Embedding R scripts within the SAP HANA
database execution
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14. Introduction
SAP Predictive Analysis Architecture and Process

15. Architecture
Prepare
Room
Predict
Room
Viz
Room
Compose
Room
HTML5 Client
Lumira
Backend
In Data Base
Engine
Offline
Engine
HANA
Sybase IQ
Engine
R Server
R scripts
PAL
scripts
HTTP
JDBC
Share
Room
R scripts R scripts
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16. What is Predictive Analysis
Questions
?
Relationships
Trends
Groupings
Anomalies
Associations
What are the trends, both
historical and emerging an
how might they continue ?
What are the main
influencers , for example
customers churn, employee
turnover etc.
Are there any clear
groupings of data for
example customer segments
for specific marketing
compaign.
What anomalies or unusual
values exists ? Are they
errors are real change in the
behavior
What are the correlations in
the data ? What are cross-
sell oppurtunities
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 16Public

17. Predictive Analysis Process
Define the
objectives of the
analysis,
understanding
the business
problem
Data Selection,
cleansing,
transformation.
Initial Data
exploration
Model Building,
training. Testing
and evaluation
Model
Deployment,
scoring and
monitoring
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18. 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-timeAsset Tracking Personalized Care
MANUFACTURI
NG
RETAIL CPG HEALTHCARE BANKING UTILITIES TELCO
PUBLIC
SECTOR
25+
Industries
MARKETING
SALES
FINANCE
HR
OPERATIONS
SERVICE
IT
SUPPLY CHAIN
FRAUD / RISK
11+ LoB
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19. Which Algorithm !! When !!!
Two Main Factors
 What do you want to do? For Example Cluster the data, look for associations,
predict etc.
 What data you have and what are the attributes of the data
For which you then may apply a selection of algorithms and the question
becomes –
 Which one gives the best fit, a question in itself, as there can be several criteria,
and in some cases, for example cluster analysis, best fit can be subjective measure
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 19Public

20. Algorithms
 Association Analysis
 Cluster Analysis
 Regression Analysis
 Classification Analysis – Decision Tree and others
 Time Series Analysis
SAP supports several algorithms which can be extended by easy R-Integration
 Correlation Matrix
 Hierarchical Clustering
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 20Public

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Exercise 1
What is “Exponential Smoothing”?
• Exponential Smoothing assigns exponentially decreasing weights as the
observation get older.
• Recent observations are given relatively more weight in forecasting than the
older observations.
What is the data about?
• Customer Order Transactional Data
• Forecast sales revenue over time period for a group of products / single
product
• Single, Double & Triple Smoothing for specific Item: Camera: Fuji Finepix
Single Smoothing Functions to predict
best fit curve for sales revenue over
period of time
Double Smoothing Functions for sales
revenue trend over period of time and
Triple Smoothing Functions to predict
seasonal patterns or variations in sales
revenue over period of time

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Exercise 2
This exercise deals with determining the influence of different attributes
on Items Sold KPI.
The Infinite Insight algorithm tries to do a piece-wise linear regression
model. It first splits the items sold into 20 different buckets; and for each
of those buckets, it tries to figure out which combination of factors
(customer income, customer gender, etc.) are important.
Performance Indicators
Predictive Power : Is a measure how accurately the model can predict the target variable chosen
based on the other columns in the dataset.
Predictive Confidence : Used to represent how well the results can be generalized to new datasets

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Exercise 3
What is the data about?
Apply R- Classification and Regression Tree algorithm to do
an analysis on shipwreck dataset for determining how the
attributes of Class, Gender/Sex and Age influence the
chances of survival.
• Classifying and predicting one or more discrete variables based on
other variables in the dataset.
• Use this algorithm to classify observations into groups and predict one
or more discrete variables based on other variables. However, you can
also use this algorithm to find trends in data.

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Exercise 4
 Using R-Apiori algorithm , marketing team identifies which product they can offer as promotion.
 Data belongs to several stores is available for analysis. This contains store sales records.
 This exercise identifies what are the items that were most commonly sold together in the same sales record
Association rules are rules presenting correlation between item sets:
three most widely used measures :
 Support: % of cases that contains both A and B
 Confidence : % of cases that contains A and also contain B
 Lift: ration of confidence to the % of cases that contain B

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Custom R script
An expert-user creates new components which can leverage the thousands of algorithms from the R
libraries.
The casual-user can then easily and graphically embed these components into his own data flows.
Exercise 5 & 6 will introduce you from creating simple to complex custom R Scripts
At the end of this session :
1. You created custom R scripts in PA
2. At design time you can select the model and pass the filed from your dataset
3. New charting types will be displayed which are not available in PA
4. The model can be re-used with other datasets

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Exercise 5 :- Correlation Matrix
A correlation matrix is a helpful tool to see the relationships between numerical variables.
A correlation matrix algorithm can be built using the R-Scripting
In this exercise you will learn how to add :
 A correlation matrix to SAP Predictive Analysis by implementing a new Custom R
Component.
 How to use it in your analysis

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Exercise 6 :- Hierarchical Clustering
Hierarchical Clusters are often used, because they
 help identify how many clusters you want to break your data into.
 can be nicely displayed in charts that help understand the differences between the
individual clusters.
In this exercise you will learn how to add such a correlation matrix to SAP Predictive Analysis
by implementing a new Custom R Component.

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SAP d-code Virtual Hands-on Workshops and SAP d-code Online
Continue your SAP d-code education after the event!
SAP d-code Online
 Access replays of keynotes, Demo Jam, SAP d-code
live interviews, select lecture sessions, and more!
 Hands-on replays
http://sapdcode.com/online
SAP d-code Virtual Hands-on Workshops
 Access hands-on workshops post-event
 Starting January 2015
 Complementary with your SAP d-code registration
http://sapdcodehandson.sap.com

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Further Information
SAP Education and Certification Opportunities
www.sap.com/education
Watch SAP d-code Online
www.sapcode.com/online
SAP Public Web
scn.sap.com
www.sap.com

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Feedback
Please complete your session evaluation for
EA261.
Thanks for attending this SAP TechEd && d-code session.
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Further queries you may Contact :
Narasimha Rao Addanki : narasimha.addanki@sap.com
Ravin Angara : ravin.angara@sap.com

31. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 31Public
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