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Machine learning 060517
1.
Emerging Technologies Series Machine
Learning June 2017 ©2017 The Solutions Group Inc. All rights reserved.
2.
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
3.
©2017 The Solutions
Group Inc. All rights reserved. | 3 What is Machine Learning?
4.
©2017 The Solutions
Group Inc. All rights reserved. | 4 Skynet is real
5.
Machine Learning ©2017 The
Solutions Group Inc. All rights reserved. | 5 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.
6.
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. ©2017 The Solutions Group Inc. All rights reserved. | 6 Other common uses include: fraud detection sales forecasting predictive equipment maintenance programmatic online advertising price optimization Image and voice recognition
7.
Impact ©2017 The Solutions
Group Inc. All rights reserved. | 7 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
8.
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
9.
Impact ©2017 The Solutions
Group Inc. All rights reserved. | 9 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.”
10.
Impact ©2017 The Solutions
Group Inc. All rights reserved. | 10 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
11.
Impact ©2017 The Solutions
Group Inc. All rights reserved. | 11 “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
12.
Impact ©2017 The Solutions
Group Inc. All rights reserved. | 12 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
13.
Impact ©2017 The Solutions
Group Inc. All rights reserved. | 13 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
14.
Impact ©2017 The Solutions
Group Inc. All rights reserved. | 14 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
15.
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. ©2017 The Solutions Group Inc. All rights reserved. | 15
16.
Lifecycle Position: …moving
fast ©2017 The Solutions Group Inc. All rights reserved. | 16 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.
17.
Major Players in
relation to our industry ©2017 The Solutions Group Inc. All rights reserved. | 17
18.
Major Players in
relation to our industry: sales ©2017 The Solutions Group Inc. All rights reserved. | 18
19.
Analyst and Consulting
Groups ©2017 The Solutions Group Inc. All rights reserved. | 19 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
20.
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 ©2017 The Solutions Group Inc. All rights reserved. | 20 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
21.
Sources ©2017 The Solutions
Group Inc. All rights reserved. | 21 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
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