Data Science Automation: The Next
Frontier
Doug Freud
Associate Vice President, Innovation, Data Science, & Data Strategy
If you do not wake up everyday and
think about how your company is
going to disrupt your industry you
can bet that one of your competitors
is already doing it.
Amazon’s Digital Disruption of Retail World
In the Beginning……
15 years ago A/B tests ruled!
Amazon.com’s high upsell cart
Add an item to your cart and get a whole
page of product recommendations
SVP at the time said, “No way. Do not test it.
Anything that
distracts shoppers from
checkout will hurt our
conversion rate.”
Greg Linden said, “Okay
I’ll test it.” Generated $7m
attributable sales a day in 2004.
First Generation Recommendation Engine
Today the Recommendation Engine
Accounts for 35% of Sales
Revenue
ML is the New BI
BI:
Powered by
humans for human
consumption.
Understanding
what has happened
Exploratory Analysis: Powered by advanced
techniques
(Statistical/Machine
Learning/Data Mining) for
human consumption. Ad-
Hoc / R&D approach. Why
did it happen and how to
improve and optimize
ML: Machines/Systems are
making decisions or
recommendations that
are powered by ML
algorithms. The
probabilities or rules
are consumed by
software or processes,
and automatically
continue to learn and
improve.
“Interconnected” Data is Growing Exponentially
*** Gartner says the “The Internet of Things” install base will grow to 26 billion units by 2020 ** How Much Data Is Generated Every Minute On Social Media?
26B Connected things by 2020***
3.5 B Google search per day*
“Data is the new oil, but in order to monetize it requires predictive analytics”
IOT • expected to be a $1.9 billion market by
2020, according to Gartner.
• 90 percent of all data generated by IOT is never analyzed or utilized
in business decision processes. • Sensors & Computer logs
Consumer Internet • Internet Surfing, email, Social media • 1.4 Billion active Facebook users,
Twitter 21 million tweets per hour, 300 Million active Instagram users, &
290,728 Tinder matches per minute • Text & Web Logs
Challenges for the Chief Analytics Officer
Shortage of
Analytical
Talent
Complicated
Data
Management
Shorter
Decision
Cycles
Keeping Pace Requires Automation: A Factory Approach
Models Per Month
Source: Adopted from Factory Analysis vs.
Craftsman Analysis, Gartner, 2010
2005 2010 2015 2020
Factory Automation:
• Creating Analytical Data Sets
• Automation around Model Building
• Deployment
• Model Management
works with Lucy to make
more accurate expert
models works with Frederick to gather
the data he needs.
works with Susan
on data mining
tasks.
ETL
Champion
Predictive
Model
Manager
Platform
Architect
Data
Scientist
Sets business requirements and
sponsors project
Data Science is a Team Sport
Dynamically Creating Analytical Data Sets
Automating Modeling Process
Automating Model Management
17 © 2016 SAP AG or an SAP affiliate company. All rights reserved.
Fleet Level Information
Machine Level Information
PdMS Application shows insights about health
score of machines and business information
ERP + MRS
Maintenance Schedules & Financial Information
Action
~10% Reduction in Maintenance Costs
Machine Learning for SAP’s digital core, cloud and networks
18
• CV matching
• Freelancer matching
• EE scoring with NLP
• Employee Lifetime Value
• Career path recommender
• Employee Churn Prediction
• Service Finder
• Decision Support
• Predictive Maintenance
• Smart Forecasting Model
• Failure Detection and
Prediction
• Maintenance Planning
Optimization
• Invoice matching
• Cash flow forecasting • Travel virtual agent
• Vendor matching
• Pay Me Now solution
• Procurement recommenders
• Semantic product understanding
• Procurement risk diversification
• Product recommender
• Customer Lifetime Value
• Social sentiment analysis
• Automated sales forecasting
• Cart abandonment prediction
• Marketing action recommender
27 INDUSTRIES • SAP Big Data Margin Assurance • XM Exchange Media Video Advertising recommender
SAP HANA
CLOUD
PLATFORM
• Clustering
• Regression
• Classification
• Anomaly detection
• Reinforcement
learning
© 2016 SAP SE or an SAP affiliate company. All rights reserved. 19 Internal
A B C
The Predictive Enterprise
The Predictive Factory Improve bottom line through managing 000’s of
data sets and models
Predictive IP Generic and specialized algorithms
Big Data Make economical sense of more
data collected
Embedded in Processes
and Apps Cloud Easy to consume apps and micro services
On Premise In tandem with SAP HANA and SAP BI
P
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High and Low Touch UX’s For all data scientists: citizens and experts
Functional
Delivery
© 2016 SAP SE or an SAP affiliate company. All rights reserved.
Thank You!
Doug Freud
Associate Vice President
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