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SAP Analytics Portfolio Webinar Series

© 2016 SAP SE or an SAP affiliate company. All rights reserved. 2Public

SAP Analytics – Overview Webinars

SAP Analytics – Overview

SAP BI – Overview & RoadmapOlivier Duvelleroy /

Saurabh Abhyankar

Kirk Anderson

Jayne Landry Mar 16

Mar 23

Mar 24SAP EPM – Overview & Roadmap

Paul Medaille Mar 30SAP EIM – Overview & Roadmap

Sven Bauszus Mar 31SAP Predictive Analytics – Overview &

Roadmap

Kevin McCollom Apr 13SAP GRC – Overview & Roadmap


SAP Analytics Portfolio - BI Solutions 1/2

Lumira; Francois Imberton

Pred. Analytics, Pierpaolo Vezzosi

Analytics Services

Portfolio

Reporting

Discovery & Analysis

Crystal Reports; Nina Bao / Donald Guo

Web Intelligence Gregory Botticchio

Adopting BI 4.1 /4.2

Intro by Merlijn Ekkel

Intro by Saurabh Abhyankar

Merlijn Ekkel

Markus Schunter

Mar 29

Apr 5

Apr 7

Apr 20

Apr 12

What‘s new in BI 4.2 Merlijn Ekkel

Self Service BI Ina Felsheim

1

2

3

6

4

5 Apr 14


SAP Analytics Portfolio - BI Solutions 2/2

Design Studio & DashboardsDashboards & Apps

Office IntegrationBI for Office /Analysis for Office & Live

Office

Ian Mayor / David Stocker

Alexander Peter

BI Cloud Ty MillerCloud for Analytics

BI in the Cloud

Apr 28

Apr 27

Apr 21

9

8

7

BI platform Maheshwar Singh May 310

May 12Mobile Reena Sethy12

Big Data Angela Harvey May 1111


SAP Analytics Portfolio – Predictive Analytics deep dives

SAP Predictive AnalyticsAn automated process, a technological breakthrough to

produce predictive results in operational IT environments

SAP Predictive Analytics

Why massive modeling is important? Hervé Kauffmann

Ashish Morzaria

Laurent Tessier Date tbd

Date tbd

Date tbdSAP Predictive Analytics - Roadmap session

What’s new & what’s next

Abdel Dadouche Date tbdPredictive Data Preparation

Ashish Morzaria Date tbdSAP Predictive Analytics

How to become a data scientist in no time

6© 2016 SAP SE or an SAP affiliate company. All rights reserved.

Webinars, Recordings & Presentations

available @

http:sap.com/k4u

Sven Bauszus, General Vice President & General Manager, Predictive Analytics, SAP

SAP User Group Webinar - March 31, 2016

SAP Predictive Analytics – Overview & Roadmap

Public


© 2016 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.

http://global12.sap.com/corporate-en/legal/copyright/index.epx


Agenda – Industrial Evolution of Data Mining

Recognizing and Leveraging Opportunities

„Nothing is stronger than an idea whose time has come“

Time for a change in the use of advanced analytics

Predictive Analytics – Demystified and Simplified

Fundamentals of predictive analytics

Industrial democratization of advanced analytics

Application Example

Product demonstration: Fraudulent claims detection

Summary, Roadmap and Q&A

Recognizing Opportunities“Nothing is stronger than an idea whose time has come” – Victor Hugo


Anticipate What Comes Next and Drive Better Decisions… Today!

Social

Network

Customer

DataAutomobiles

Machine

DataSmart Meter

Point of

SaleMobile

Structured

DataClick Stream

Location-

based DataText Data

IMHO, it’s great!

RFID

68% of organizations

that used predictive analytics

realized a competitive

advantageVentana Research

52% use predictive

analytics to increase

profitability

55% use predictive

analytics to create new

revenue opportunities

45% use predictive

analytics for customer

services

43% use predictive

analytics for product

recommendations


Predictive Analytics Delivers Value to Business (ROI vs TCO)

Up to 95% increase in

forecasts due to inclusion

of extra-predictor variables

70% reduction efforts for

generating the predictive

models

68% more likely to deliver

personalized offers to high-

value customers through the

right channel

2x higher response rate

for cross-sell campaigns

which means 100% more

sales

56% more likely to

predict a customers next

(best) purchase

Reduce e-fraud in over 1b

annual transactions and

save 100m in lost revenue

and cut false alerts to

improve customer

satisfaction


Use Case Examples – Potential for Business Optimization

:-)Brand Sentiment / Marketing Mix /

Channel Affinity

Optimize the action plans for medical visitors

Product Recommendation /Market Basket / Cross- & Up-Sell

Customer Attrition Mgt / Win Back Strategy / Churn

Real-time Demand/Supply / Sales Forecast

Predictive Maintenance / Quality Control / Manufacturing Process

Optimization

Fraud Prevention & Detection / Collection Mgt

Network Optimization Insider Threats

Risk Mitigation / Compliance Mgt

Predictive Asset Management /Supply Chain Optimization

Satisfaction Management /HR Developmet


The Cost of Neglecting Predictive Analytics

LOWER MARGIN

LOST REVENUE


It’s No Longer Sense and Respond …

Ever Faster

Decision Cycle

Analytical

Skill Gap

“Demand for deep

analytical talent in the US

could be 50 to 60%

greater than its projected

supply by 2018”

McKinsey Global Institute

1 11

Transactions

Conversations

Machines

Massive

Amount of Data

Gartner


Democratization and Proliferation of Predictive Intelligence Access and operational integration anywhere, anytime

“I’m building churn

models for every

region”

MS Statistics, Berkeley

Data Scientist

.01%

“I need explain to the

CEO why sales are down

in EMEA”

MBA, U of Pennsylvania

Data Analyst

~3% 97%

Business User

“The app needs to tell

me what offer to make in

real time”

BS French Literature, UC

Davis


Predictive Analytics: Not Really One Size Fits All

BUSINESS USERS & LOBDATA

SCIENTISTSBUSINESS ANALYSTS

LEVEL OF SKILL SET - ANALYTICS

LOW HIGHNO

97% 3% >0.1%EMBEDDED ANALYTICS

INDUSTRY & BUSINESS PROCESS ANALYTICS

CUSTOMANALYTICS

SAP PREDICTIVE ANALYTICS


Predictive is Part of the Business Analytics Continuum

From Sense & Respond to Predict & Act

RawData

CleanedData

Standard Reports

Ad Hoc Reports &

OLAP

AgileVisualization

PredictiveAnalytics

PrescriptiveAnalytics

What happened?

Why did it happen?

What will happen?

Us

er

En

ga

ge

me

nt

Maturity of Analytics Capabilities

Self Service BI

What should I do?


SAP Analytics Solution Portfolio – Trusted Business Value Cycle

Discover

Cloud for Analytics

Any Device

Social Collaboration Big DataIoT

EnterprisePerformanceManagement

Governance,Risk, and

Compliance

Inform

Anticipate Plan

BusinessIntelligence

PredictiveAnalytics

Real-timeBusiness

Predictive AnalyticsDemystified and Simplified


What is Predictive Analytics?

Use HistoricalDATA to detect

PATTERN

Predict by executing

PATTERN on current DATA

Control & Maintain the

Quality


Simplified Predictive Modeling Lifecycle – Accurate & Robust

Learn phase Apply phaseDeploy

Retrain / Calibrate


A few data mining functions for answering various business

questions – non-parametric, non-intrusive

Classification / Scoring

Who will churn, fraud or buy next week, next month ?

Regression

How many products will a customer buy next month, next quarter ?

Segmentation / Clustering

What are the groups of customers with similar behavior or profile ?

Forecasting

How much will be the monthly revenue or number of churners next year ?

Recommendations

What is the best offer or action for a customer or internet user ?


SAP Predictive Analytics – The Difference

Predictive Reimagined for the Digital Enterprise

Before SAP Predictive

Analytics

After SAP

Predictive

Analytics

Answer any/all questions

with any/all data sources

In-database automated

dataset production

Automated modeling and

tuning process

Native in-database and in-

application/process

deployment

On-going model management

and recalibration (retrain)

Real Life Example:

1 person x 7 days = 400

models vs. 6 people x 8

weeks = only 20 models

Application ExampleDetecting Fraud from Auto Insurance Data


The Predictive Process with SAP Predictive Analytics

Question

Design

Manipulation

Connect

to Database

Execute

Manipulation

Automated

Modeling

Deploy

Control

Model

Management

Model

Creation

Maintain

Results

Consumption

and Integration

Semantic

Layer


Example Process – Fraudulent Claims Detection

Past Auto Claims

with a Yes/No

Fraud flag

Train Model Apply Model

New

Claims

Fraud

Scores

New

Claims

Model

equation

Summary Predictive Reimagined for the Digital Enterprise


Our Mission: Massive Predictive Analytics

Embedded in the Digital Enterprise for the Masses

Massive Predictive FactoryManage massive number of predictive models to improve margins

Automated mining of massive datasets and E2E lifecycle model management

Predictive for the MassesDemocratize predictive through integration in business applications

Operationalized proliferation of Predictive Insights where people interact


SAP Predictive Analytics (PA) 3.x

The Plan Beyond SAP PA 2.5

Sapphire Q2/2016 – PA 3.0

•Desktop (2.x) required for model authoring

•New Fiori-UX for Model Management use cases

•New Platform (Enterprise grade, already proven in other SAP products)

•Model Management for Auto only

•Segmented Model Management (Automated)

•Native Spark Modelling for auto regression

Planned Innovations

•Unified Web Authoring for most scenarios

•Desktop Optional

•Model Mgmt. for Auto and Expert Models

•Rare events stability (predictive maintenance)

•Analytical scalability to 50K columns

•Ability to author models into UMML-based apps

•Spark MLlib Support

Future

•Full Web Authoring

•Desktop deprecation planned

•Potential plans:

•Big Data innovations - increase scale, leverage Vora as an engine, support graph, spatial, cognitive models,

•Web-based authoring as C4A/PA Professional

•C4A/PA functionality on-premise

•Full(er) native Spark Modeling (more algos)

This is the current state of planning and may be changed by SAP at any time.

Cloud for Analytics for

predictive analytics

(1st wave)


SAP as the Market Leader

Hurwitz: Victor“Fast time to value and ability to

support very large data sets.”

Victory Index Report

Dresner: Top Vendor“The top vendors for advanced and predictive analytics include SAP.”

The Wisdom of Crowds

Forrester: Leader“SAP is a leader due to a

strong architecture and strategy.”

Big Data Predictive Wave


Q&A

? ??

Thank youContact Information:

Sven Bauszus

Global Vice President

Predictive Analytics

Homberger Strasse 25

DE-40882 Ratingen

+49 2102 868-287

+49 151 6783 4623

[email protected]

@SBauszus


© 2016 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.

http://global12.sap.com/corporate-en/legal/copyright/index.epx

Appendix

© 2016 SAP SE or an SAP affiliate company. All rights reserved. 36Privileged

Our Predictive Analytics Moonshot!

Massive Predictive

Factory

Embeddedin The Digital

Enterprise

Predictive for The Masses


SAP Predictive AnalyticsPredictive Reimagined for the Digital Enterprise

Bigger, better and faster insights

Accelerate your work with automated techniques

Empower Analysts with a repeatable, self-service predictive workflow

Extend predictive analytics to next generation digital platforms

Machine learning at scale with the predictive factory

Scale and maintain peak performance for every model

Manage the end to end lifecycle with enterprise governance

Simplify integration into IT landscapes

Predictive insights where people interact

Embed predictive outcome in business processes

Extend predictive to the cloud

Empower people with explanatory knowledge


Applying Predictive to Real Business Problems

Types of Business Problems Solved with Predictive

Predictive

Maintenance

Load Forecasting

Inventory/demand

Optimization

Product

Recommendation

Price Optimization

Manufacturing

Process Opt.

Quality

Management

Yield Management

Operations

Fraud and Abuse

Detection

Claim Analysis

Collection and

Delinquency

Credit Scoring

Operational Risk

Modeling

Crime Threat

Revenue and Loss

Analysis

Fraud

and Risk

Cash Flow and

Forecasting

Budgeting

Simulation

Profitability and

Margin Analysis

Financial Risk

Modeling

Employee Retention

Modeling

Succession Planning

Finance

and HR

Life Sciences

Health Care

Media

High Education

Public Sector/

Social Sciences

Construction and

Mining

Travel and

Hospitality

Big Data and IoT

Others

Churn Reduction

Customer

Acquisition

Lead Scoring

Product

Recommendation

Campaign

Optimization

Customer

Segmentation

Next Best Offer/

Action

Sales and

Marketing


Challenges

Forecasting

Key

Influencers

Trends

Anomalies

Relationships

How do historical sales, costs, key

performance metrics, and so on,

translate to future performance? How do

predicted results compare with goals?

What are the main

influencers of customer

satisfaction, customer

churn, employee

turnover, and so on,

that impact success?

What are the trends:

historical/emerging, sudden

step changes, unusual

numeric values that impact

the business?

What are the correlations

in the data? What are the

cross-sell and up-sell

opportunities?

What anomalies

might exist and

conversely what

groupings or clusters

might exist for

specific analysis?

High Value Use Cases for Predictive Analytics


SAP Predictive Analytics – Automated Approach

SAP Predictive

AnalyticsClustering, Time Series,



SAP Predictive Analytics helps users to quickly

gain business insights through

simplified and automated data mining.

Automated Analytics

For business users where data mining

is simply done without scripting

Data manager / Modeler / Model

manager

Expert Analytics

For advanced users to configure

customized algorithms with R and PAL

R configuration / visualization /

HANA PAL & APL integration

Components of SAP Predictive Analytics Platform

Models can be exported to any database languages and used as stored procedures


Predictive Analytics – Summary / Elevator Pitch

SAP Predictive Analytics – The Art of Democratizing

Advanced Business Insight at Scale and Productivity

Automated industrial mining of massive data sets and end-to-

end lifecycle management for creating and consuming robust

predictive models in business operations at scale and scope

Predictive modeling environment suited for both

business/data analysts and data scientists in a single,

generic platform solution


Market Drivers and SAP Differentiation

Big Data Internet of things Open Source Actionable insights Digital Transformation

Breadth of Advanced

Analytics Portfolio

SAP Predictive

Engineering in single

roof

Industry & LoB

ExpertiseSAP Cloud, SAP Data

Science & RDS

Predictive in all SAP

Apps

Market Drivers & Trends

SAP Advantage

Spot risk and opportunities in real-time

• Real-time predictive on big data & IoT with SAP HANA

Make predictions simple, fast, and accurate

• Redefine experience for each persona – Collaborative and Automated Insights

Act with confidence at the point of decision

• Embedded (“Invisible”) analytics in business processes and apps

SAP Differentiation


Tradition meets Modernity

Traditional Predictive Solutions

Time-intensive,

manual

processes.

Linear

relationship

between model

accuracy and

analyst time

Hard to maintain,

share and port

model code or

workflow

diagrams

Statistical

library where

the right

algorithm must

be selected,

tested and fine-

tuned

Hard IsolatedSlow

Statisticians pre-

select variables,

hence excluding

information

Manual

1

2

34

5


Competitive Advantages


Reduce decision

latency with real-

time insight

Apply actions to

information,

processes, and

apps

Bring Advanced

Analytics to a

broad spectrum

of users

EasyActionableFast

Self-service

application of the

right model with

the best fit

Automated


Use Cases in Retail Finance Processes


SAP Predictive Analytics: Automated Analytics

Optimal model selected

automatically

Automated and simplified by SAP Predictive Analytics

3 Months

1 Week

Data

aggregation

Data pre-

processingSampling

Predictive

model

creationTesting

Inter-

pretation

Application

to business

Simple GUI Automated Automated SimplifiedApplication

to business

Real Life Example: 1 person x 7 days = 400 models vs. 6 people x 8 weeks = 20 models


Key 2016 Themes

Rethought User Experience

• Unified user interface

• One application to create automated and

expert models

• Mass management and

operationalization of models

Eased Consumption

• Business applications

• Analytics applications

• SAP Hana Cloud Platform

predictive services

• SAP Cloud for Analytics

Enterprise-Ready

• SAP HANA

• BI Platform framework

• Hadoop/Big Data

• SAP HANA Cloud Platform (HCP)

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