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ANP104 –
Massive Predictive Analytics
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 2 Public
Speakers
Bangalore, October 5 - 7
Paul Pallath, Chief Data
Scientist, Advanced
Analytics Products
Las Vegas, Sept 19 - 23
Erik Marcadé - Vice
President, Advanced
Analytics Products
Barcelona, Nov 8 - 10
Erik Marcadé - Vice
President, Advanced
Analytics Products
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 3 Public
Disclaimer
The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of
SAP. Except for your obligation to protect confidential information, this presentation is not subject to your license agreement or
any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this
presentation or any related document, or to develop or release any functionality mentioned therein.
This presentation, or any related document and SAP's strategy and possible future developments, products and or platforms
directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice.
The information in this presentation is not a commitment, promise or legal obligation to deliver any material, code or functionality.
This presentation 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. This presentation is for informational
purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this
presentation, except if such damages were caused by SAP’s intentional or gross negligence.
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.
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 4 Public
Agenda
• Predictive Analytics and Machine Learning Overview
• What is Massive Predictive Analytics?
• A Small Word on Automation
• Some Examples
Public
Predictive Analytics and
Machine Learning Overview
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There is no such thing as an analytics continuum!
Raw
Data
Cleaned
Data
Standard
Reports
Ad Hoc
Reports &
OLAP
Agile
Visualization
Predictive
Analytics
Prescriptive
Analytics
What happened?
What will happen?
Why did it happen?
User
En
gag
em
en
t
Maturity of Analytics Capabilities
Self Service BI
What should I do?
Chasm!!!
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 7 Public
There is no such thing as an analytics continuum!
Raw
Data
Cleaned
Data
Standard
Reports
Ad Hoc
Reports &
OLAP
Agile
Visualization
Predictive
Analytics
Prescriptive
Analytics
What happened?
What will happen?
User
En
gag
em
en
t
Maturity of Analytics Capabilities
Self Service BI
What should I do?
Humans make decision
Data is aggregated for
visualization
UI integration at best
Why did it happen?
Chasm!!!
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 8 Public
There is no such thing as an analytics continuum!
Raw
Data
Cleaned
Data
Standard
Reports
Ad Hoc
Reports &
OLAP
Agile
Visualization
Predictive
Analytics
Prescriptive
Analytics
What happened?
Why did it happen?
What will happen?
User
En
gag
em
en
t
Maturity of Analytics Capabilities
Self Service BI
What should I do?
Machines propose/make
decision
Data is de-normalized,
flattened, fine grain
Process integration at last
Chasm!!!
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 9 Public
$10B $3B
$1B
Worldwide Market Sizes… (Guesstimates)
Content/
Unstructured
Analytics
Advanced/
Predictive Analytics Query
Reporting
Agile Viz
Analytics
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 10 Public
SALES &
MARKETING OPERATIONS FRAUD
AND RISK
FINANCE
AND HR
OTHER
SECTORS
• Churn Reduction
• Customer Acquisition
• Lead Scoring
• Product Recommendation
• Campaign Optimization
• Customer Segmentation
• Next Best Offer/Action
• Predictive Maintenance
• Load Forecasting
• Inventory/Demand
Optimization
• Product Recommendation
• Price Optimization
• Manufacturing Process Opt.
• Quality Management
• Yield Management
• Fraud and Abuse Detection
• Claim Analysis
• Collection and Delinquency
• Credit Scoring
• Operational Risk Modeling
• Crime Threat
• Revenue and Loss Analysis
• Cash Flow and Forecasting
• Budgeting Simulation
• Profitability and
Margin Analysis
• Financial Risk Modeling
• Employee Retention
Modeling
• Succession Planning
• Life Sciences
• Health Care
• Media
• High Education
• Public Sector /
Social Sciences
• Construction and Mining
• Travel and Hospitality
• Big Data and IoT
Solving real world problems by optimizing resources and improving margins
Public
What is Massive Predictive
Analytics?
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What is Massive Predictive Analytics?
MASSIVE PREDICTIVE FACTORY
P R E D I C T I V E F O R
THE MASSES!
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Major Investment Areas
A B C
The Predictive Factory
Predictive IP
Big Data
Cloud
On Premise
P
</>
10101010
00100110
0111
High and Low Touch UX’s
Functional
Delivery
Embedded in Processes and Apps
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Crucial Need: Managing a Massive Number of Models
• No coding, just configuration!
• Full predictive lifecycle
• Modeling automation
• Inclusive
The Predictive Factory P
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More Users through Predictive Automation.. Open to Data Scientists!
</> Low and High Touch
User Experiences
• Low touch user interface using wizard-based
approach and automated
• High touch user interface supporting open
languages
• Interoperability between both
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 16 Public
More Models With Generic/Specialized IP. Monetize your IP.
A B C
Predictive
Intellectual Property
• Generic proprietary algorithms
• Niche proprietary algorithms
• Niche predictive IP to be available via a
marketplace
• Open to data science standards and available
resources
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Massive Consumption through Cloud. Extend the Native Capabilities.
• All-in-one SAP BusinessObjects Cloud
• Guided Machine Discovery
• HCP, Predictive Services targeting partners
and integrators
Cloud
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Massive Number of Models on Massive Data Volumes
10101010
00100110
0111 Big Data
• Data Preparation compatible with ultra-wide
data sets
• Distribution of algorithms on scale-out
architectures
• Integration into Big Data streaming
environments
0
2
4
6
8
10
12
PA 2.4 PA 2.5 PA 3.0
Time on Hadoop/Spark
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Massive Deployment requires a Simplified Product Design
APL
Desktop Automated Java
Swing Client
Expert (HTML/Java)
KxShell
Predictive Factory
- Fiori Automated UI5
scheduling app
Automated Java
Swing Client,
KxCORBAShell
Client
Server
Unmanaged
SDK (CORBA)
Predictive Analytics 3.x
Sapphire 2016
Server
BIP managed
Predictive Factory
- Fiori
Automated & Expert UI5
app (successor
authoring)
Automated Java
Swing Client
(legacy features),
KxHTMLShell
Client
APL
Predictive Analytics 4.x
Future
On premise,
HANA (XSA)
SDK
HTML/services
Evolutions
Existing
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Predictive for the Masses through Integration Capabilities
Embedded in Processes
and Apps
Predictive Analytics Integrator
• Leveraging powerful predictive libraries (APL, PAL,
R) in HANA
• Included in every deployment of the Integrating app
• Out-of-the-box lifecycle management of predictive
models (retrain, score, test)
Public
A Small Word on Automation
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 22 Public
Traditional Approach
Problem
Identified
Aggregate & prepare
data
Identify relevant variables
Derived features &
encode variables
Develop models
Debrief models
Write code for
database execution
Business
Results
What competition does
for automation:
Stack several algorithms
and hope for the best
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 23 Public
Value of SAP BusinessObjects Predictive Analytics Automation
Problem
Identified
Aggregate & prepare
data
Identify relevant variables
Derived features &
encode variables
Develop models
Debrief models
Write code for
database execution
Business
Results
Data Manager
• Generate SQL for HANA, Hadoop, Hive,
SparkSQL, and all major databases
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 24 Public
Value of SAP BusinessObjects Predictive Analytics Automation
Problem
Identified
Aggregate & prepare
data
Identify relevant variables
Derived features &
encode variables
Develop models
Debrief models
Write code for
database execution
Business
Results
Auto-algorithms
• Auto-Algorithms make this section
obsolete
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 25 Public
Value of SAP BusinessObjects Predictive Analytics Automation
Problem
Identified
Aggregate & prepare
data
Identify relevant variables
Derived features &
encode variables
Develop models
Debrief models
Write code for
database execution
Business
Results
Auto-algorithms
• Numbers, strings, dates
• Categorical, continuous, textual
• Date parts
• Composite variables (example: position
from latitude and longitude)
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 26 Public
Value of SAP BusinessObjects Predictive Analytics Automation
Problem
Identified
Aggregate & prepare
data
Identify relevant variables
Derived features &
encode variables
Develop models
Debrief models
Write code for
database execution
Business
Results
Auto-algorithms
• Classification, regression, clustering,
times series, key influencers
• Link analysis, recommendations
• HANA (APL)
• Hadoop (Scala)
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 27 Public
Value of SAP BusinessObjects Predictive Analytics Automation
Problem
Identified
Aggregate & prepare
data
Identify relevant variables
Derived features &
encode variables
Develop models
Debrief models
Write code for
database execution
Business
Results
Auto-algorithms
• All descriptive statistics available
• Key influencers, decision trees, segments,
optimal binning and banding
• Communities
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 28 Public
Value of SAP BusinessObjects Predictive Analytics Automation
Problem
Identified
Aggregate & prepare
data
Identify relevant variables
Derived features &
encode variables
Develop models
Debrief models
Write code for
database execution
Business
Results
In-Database Apply:
• Automated SQL generation
• Optimized with data manager
• HANA: Streaming (CCL)
• Hadoop: Streaming (Java)
Public
Some Examples
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 30 Public
Some Examples
• ~1600 models
retrained every
month for product
appetency
Large video provider
• 400 models in 11
families for more
than 6 years for
demand forecasting
Chemical company
• 400 models for sales
pipeline accuracy
improvement (built in
5 days…)
High tech company
• 15,000 models for
objects routing
Transportation company
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SAP TechEd Online
Continue your SAP TechEd
education after the event!
Access replays of
Keynotes
Demo Jam
SAP TechEd live interviews
Select lecture sessions
Hands-on sessions
…
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Further information
Related SAP TechEd sessions:
ANP101 - SAP BusinessObjects Predictive Analytics – What You Really Need To Know – 1hr Lecture
ANP105 - Turning Big Data and IoT into Intelligence You Can Actually Use – 1hr Lecture
ANP108 - Predictive Business Intelligence: Give Your BI the Gift of Foresight – 1hr Lecture
ANP160 - SAP BusinessObjects Predictive Analytics 101: Discovery Session – 2hrs Hands-On Workshop
ANP205 - SAP BusinessObjects Cloud Brings Predictive Tools to the Business Analyst – 1hr Lecture
ANP260 - Massively Automate Predictive Models with Predictive Factory and SAP HANA – 2hrs Hands-On Workshop
ANP270 - SAP BusinessObjects Planning and Consolidation with Predictive Analytics – 2hrs Hands-On Workshop
ANP360 - Integration and Scripting with SAP BusinessObjects Predictive Analytics – 2hrs Hands-On Workshop
ANP600 - Start developing with the SAP HANA Cloud Platform predictive services – 1hr CodeJam
ANP806 - Road Map Q&A: SAP BusinessObjects Predictive Analytics – 1hr Session
SAP Public Web
scn.sap.com/community/predictive-analytics
sap.com/predictive
SAP Education and Certification Opportunities
https://training.sap.com/shop/sap-search?q=predictive+analytics
Watch SAP TechEd Online
www.sapteched.com/online
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 33 Public
Please complete your
session evaluation for
ANP104
Contact information:
Erik Marcadé VP Advanced Analytics Products [email protected]
Feedback
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 34 Public
© 2016 SAP SE or an SAP affiliate company. All rights reserved.
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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.