Machine learning 060517

Emerging Technologies Series
Machine Learning
June 2017
©2017 The Solutions Group Inc. All rights reserved.

What We’ll Cover
 What is Machine Learning (ML)
 Impact it is having
 Where it is today
 Major players in our industry
 How consulting groups approach ML
 Marketing and ML
 Source documents
©2017 The Solutions Group Inc. All rights reserved. | 2

What is Machine Learning?

Skynet is real

Machine Learning
Artificial
Intelligence
AI
Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode
of self-learning without being explicitly programmed. When exposed to new data, computer programs,
are enabled to learn, grow, change, and develop by themselves. The process of machine learning is
similar to that of data mining.
Machine
Learning
ML
Data
Mining
DM
Artificial Intelligence (AI) is usually defined as the science of making computers do things that require
intelligence when done by humans.
Data Mining or Knowledge Discovery is the process of analyzing data from different perspectives and
summarizing it into useful information─information that can be used to increase revenue, cuts costs, or both.

Impact
Machine learning, will give marketers the ability to
train their own machine to learn algorithms from
scratch.
An important source for machine learning is online
recommendation engines.
Other common uses include:
 fraud detection
 sales forecasting
 predictive equipment maintenance
 programmatic online advertising
 price optimization
 Image and voice recognition







Impact
A brave new world:
 Run on servers
 Built into bots
 Drive decision-making systems
 Embedded into cars and aircraft
 Extend CX through mobile devices




DataProcessing
Power
IoT
True
Business
Intelligence
AlgorithmBig Data

How companies are transforming
©2017 The Solutions Group Inc. All rights reserved.
| 8
Evaluating data availability and structures
Tremendous spike in amount of data, both structured and unstructured
Addressing data quality, gaps and silos
Rise of Chief Data Officer
Investing in people assets
Seeking top talent
Know-how re: application of technology with all business functions
Preparing for cultural changes
Organization design
Overcome fear/concerns of adoption and impact on jobs

Impact
In the future, Adobe wants to see more integration between machine learning and the customer experience,
integrating data streams from web sites, apps and social media.
“Ultimately we are moving towards re-creating the intimacy that was lost by …the previous generation of
marketing technology that produced spam but wasn't effective.” “We want to create a unique
personalized experience for buyers where everything is catered to their problems and communicates in
their vocabulary… Not only do we want AI and machine learning to provide insights, but in-fact be
biased towards taking action directly rather than recommending actions.”

Impact
Apple plans to start testing self-driving cars on California
roads, the clearest signal yet that the world’s most
valuable technology company wants to design or build
autonomous vehicle technology. ─The New York Times
“Apple uses machine learning to make its products and services smarter, more intuitive and
more personal. The company is investing heavily in the study of machine learning and automation, and
is excited about the potential of automated systems in many areas, including transportation.”
─Timothy D. Cook, CEO

Impact
“Computer kaizen…our ability to learn from data by
developing and testing algorithms.”
“Just as mass production changed the way products were assembled and continuous improvement
changed how manufacturing was done, so continuous and automatic experimentation will
improve the way we optimize business processes in our organizations.”
─Hal Varian, Google Chief Economist

Impact
Today
 Loan approvals, managing assets, assessing risk
 Portfolio management (robo advisors)
Betterment, Wealthfront, Schwab Intelligent Portfolios
 Automated trading systems
Renaissance Technologies, Walnut Algorithms
 Fraud detection
Kount, APEX Analytics
 Loan/Insurance underwriting
Compare.com
Financial
Services
Future
 Customer service: chat bots
Kasisto
 Security 2.0: biometric data
Cognitec

Impact
Today
 Medical imaging diagnosis, treatment queries, drug discovery
IBM Watson
 Medical imaging diagnosis
Microsoft InnerEye
 Crowdsourced medical data collection
IBM, Apple ResearchKit
 Treatment queries
IBM Watson/Memorial Sloan Kettering
 Drug discovery
IBM, Google, Drug companies
 Robotic surgery: da Vinci robot
Healthcare
Future
 Personalized medicine
 Automatic treatment
IBM/Medtronic

Impact
Today
 Production efficiency & effectiveness, quality improvement,
optimization
 Increase production capacity while lowering material
rates
 Better manage factory and demand-side constraints
(workflows, inventory, WIP)
 Monitoring of plant equipment to avoid machine disruption
 Improve product/service quality to the workflow level;
predict quality
 Increase production yields by optimizing team, machine,
and customer rqmts
Manufacturing
Future
 Manufacturing-as-a-Service (MaaS): rapid, highly customized
runs on subscription model basis
 Optimized pricing to accelerate sales cycles

Lifecycle Position: still a niche technology but…
Machine learning ranked 18th in adoption on a list of
27 business intelligence and analytics technologies,
according to a report on the survey that TDWI
published October 2015.
Despite its relatively low adoption rate, machine
learning isn't really a new field. Experimental uses of
machine learning methods go back to the early days
of artificial intelligence (1950s). It has been a key
facet of data mining and predictive analytics
processes in leading-edge organizations for many
years.

Lifecycle Position: …moving fast
In 2016, the machine
intelligence market garnered
US$5 billion in venture
capital investment.
By 2018, 45 percent of the
fastest-growing companies
will have more smart
machines than employees.
Machine-learning technology
will hit full mainstream
adoption within the next
two to five years.
The total machine learning
market could reach $2 trillion
within the next decade.

Major Players in relation to our industry

Major Players in relation to our industry: sales

Analyst and Consulting Groups
 Large global teams/SMEs provide thought leadership
 Paid/free research
 Workshops, seminars, webinars
 Onsite learning labs
 Beginning to focus on helping clients implement via
and communications programs

What does it take to get started? McKinsey: “It
means putting strategy first.”
Asking fundamental questions to shape the
strategic vision:
• What will data and analytics be used for?
• How will the insights drive value?
• Which data sets are most useful for the
insights needed?
Marketers need to be both
Marketing and Machine Learning
Two types of people will unleash the potential
of ML:
 Quants: schooled in its language
and methods
 Translators: bridge the disciplines of data,
machine learning and decision making by
reframing the quants’ complex results as
actionable insights that generalist
managers can execute

Sources
www.mckinsey.com/industries/high-tech/our-insights/an-executives-guide-to-machine-learning
https://www.digitalistmag.com/digital-economy/digital-futures/2017/01/23/machine-learning-real-business-intelligence-04830225
https://d.dam.sap.com/a/nXJPh/sap_digitalfutures_no21_010917.pdf
https://hbr.org/2016/04/welcoming-the-chief-strategy-robot
www.mckinsey.com
www.bcg.com
www.Accenture.com
www.gartner.com

Machine learning 060517

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