Earley Executive Roundtable on Data Analytics - Session 1 - The Business Potential of Machine Learning & Cognitive Computing
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The document outlines a presentation on the business potential of machine learning and cognitive computing, featuring discussions by industry experts on how these technologies can enhance data analysis and decision-making. Key themes include the differentiation between machine learning and cognitive computing, challenges in adoption for various business sizes, and practical applications in marketing and IT services. It highlights the importance of education and understanding in leveraging these technologies effectively.
![Copyright © 2015 Earley Information Science13
Olly Downs – POV
• Successful adoption of Machine Learning (ML) and Cognitive Science to
drive business value is split across 3 segments
– Large businesses for which these capabilities are core to their business
– Businesses for which these capabilities are strategic and that can invest in team and
tools
– Businesses for which these capabilities are valuable but inaccessible
• “Chasm” between 1 & {2,3}
– Getting business-impacting results and operationalizing is difficult
– Processes are unwieldy, and even best practices with teams and tools move slowly
i.e., weeks, months
– Hiring and retaining people with the right skills is not easy (as #1 consumes them)
• Proposition for ML and Cognitive Computing to “Cross the Chasm”
– Add value without hands on intervention – discover and act without human experts
– Inform and educate on what is discovered
– Reduce upfront investment hurdle
[ 13 ]](https://image.slidesharecdn.com/eisertdata-analytics-session-1-slides2015-05-27-151103213256-lva1-app6892/85/Earley-Executive-Roundtable-on-Data-Analytics-Session-1-The-Business-Potential-of-Machine-Learning-Cognitive-Computing-13-320.jpg)
![Copyright © 2015 Earley Information Science14
Example – Applying Machine Learning to
Marketing
[ 14 ]
Plan Development Launch
Traditional
Campaign
Process
Test
Optimize Design
Test
Analyze
Analyst/
Data Scientist
Plan Development
Launch
Campaign
Process
With Amplero
Discover
“Team & Tools” Approach:
Weeks of Design and Configuration
10’s of Marketing Contexts
Weekly Analysis
One-time Optimization
BI Team Researching Results
Machine Learning Approach:
Configuration in Minutes
1,000’s of Marketing Contexts
Daily Discovery
Continuous Optimization
BI Team Researching New Revenue Opportunities](https://image.slidesharecdn.com/eisertdata-analytics-session-1-slides2015-05-27-151103213256-lva1-app6892/85/Earley-Executive-Roundtable-on-Data-Analytics-Session-1-The-Business-Potential-of-Machine-Learning-Cognitive-Computing-14-320.jpg)
Earley Executive Roundtable on Data Analytics - Session 1 - The Business Potential of Machine Learning & Cognitive Computing
- 1. Copyright © 2015 Earley Information Science1 Copyright © 2015 Earley Information Science Earley Executive Roundtable Series on Data Analytics Session 1: Business Potential of Machine Learning and Cognitive Computing May 27, 2015 Presented by Seth Earley CEO Click to watch the recording of this session
- 2. Copyright © 2015 Earley Information Science2 Today’s Agenda • Welcome & Housekeeping – Session duration & questions – Session recording & materials – Take the survey! • Introduction – Seth Earley • Panelist Introductions – Bruce Daley Principal Analyst, Tractica (@brucedaley) – Olly Downs, Chief Scientist/CTO, Globys (@globysinc ) – Mitchell Shuster, Data Scientist, Knowledgent (@Knowledgent) – Patrick Heffernan, Practice Manager, TBR (@TBR_PatrickH) • Panel Discussion • Questions & Answers
- 3. Copyright © 2015 Earley Information Science3 Seth Earley, Founder & CEO, Earley Information Science seth@earley.com @sethearley • Over 20 years experience in data science and technology, content and knowledge management systems, background in sciences (chemistry) • Current work in cognitive computing, knowledge and data management systems, taxonomy, ontology and metadata governance strategies • Co-author of Practical Knowledge Management from IBM Press • Editor of Data Analytics Department IEEE IT Professional Magazine • Member of Editorial Board Journal of Applied Marketing Analytics • Former Co-Chair, Academy of Motion Picture Arts and Sciences, Science and Technology Council Metadata Project Committee • Founder of the Boston Knowledge Management Forum • Former adjunct professor at Northeastern University • Guest speaker for US Strategic Command briefing on knowledge networks • AIIM Master Trainer – Information Organization and Access • Course Developer and Master Instructor for Enterprise IA and Semantic Search • Long history of industry education and research in emerging fields
- 4. Copyright © 2015 Earley Information Science4 Copyright © 2015 Earley Information Science Machine Learning and Cognitive Computing Core Concepts
- 5. Copyright © 2015 Earley Information Science5 Machine Learning - The detection of patterns and surfacing of information through a variety of approaches based on statistics and mathematics A search index is a derivation of structure from unstructured information (clustering, classification, entity extraction and various text analytics approaches use machine learning approaches) Advanced search algorithms detect “signals” from users’ intent and past search patterns to increase the relevance of search results
- 6. Copyright © 2015 Earley Information Science6 Machine Learning - The detection of patterns and surfacing of information through a variety of approaches based on statistics and mathematics “More like this” and “users who liked this also liked that” types of results leverage machine learning algorithms Systems that classify documents based on “training sets” use analytical methods to create mathematical representations of content and documents Personalization – content, search results or product recommendations are all based on a system for “predicting” what you are looking for.
- 7. Copyright © 2015 Earley Information Science7 Machine Learning and Cognitive Computing Siri answers your questions about movie times, sports scores, restaurants nearby Cognitive Computing - A way for computers to be more user friendly and “understand” what humans want Watson answered tricky and ambiguous trivia questions with obscure references, puns, metaphors, time references, slang, idiomatic expressions and other challenging types of ambiguous queries Interpreting signals - what a user is looking for in a query, interpreting questions asked in plain English (natural language), engaging in a dialog, “understanding” the meaning of an ambiguous question, anticipating the next step in a process Pattern recognition, pattern matching and rules for predicting outcomes
- 8. Copyright © 2015 Earley Information Science8 Machine Learning and Cognitive Computing Artificial intelligence encompasses all of these tools and techniques to solve various types of problems – from writing articles to driving cars to detecting fraud, diagnosing disease, making decisions that have previously been in the realm of human judgement
- 9. Copyright © 2015 Earley Information Science9 Copyright © 2015 Earley Information Science Today’s Panel of Experts Bruce Daley, Olly Downs, Mitchell Shuster, Patrick Heffernan
- 10. Copyright © 2015 Earley Information Science10 Bruce Daley • Contributor to Tractica’s Automation & Robotics practice with focus on artificial intelligence and machine learning for enterprise applications • Previously, vice president and principal analyst with Constellation Research covering business research themes related to customer relationship management, mobility, and infrastructure • Also, founder of Great Divide, co-founder of Rabbit Ears Capital Advisors, founder of Test Common Inc., founder of the Enterprise Software Summit, and founder of The Siebel Observer, the largest publication devoted to Siebel Systems • Additionally, held consulting and management roles at Oracle and Bain & Company • Widely quoted industry expert in major publications including The Wall Street Journal, The New York Times, The Financial Times, The International Herald Tribune, IEEE Spectrum, The San Jose Mercury News, and many more. • Author of a soon-to-be-published book on data storage, Where Knowledge is Power, Data is Wealth • Holds a BA from Tufts University Principal Analyst Tractica @brucedaley
- 11. Copyright © 2015 Earley Information Science11 Ad Services Automotive Agriculture Finance Data Storage Education Investment Health Care Legal Manufacturing Media Medical Oil and Gas Philanthropy Retail • Self driving cars • Self parking cars • Diagnostics • Control plane • Watson • Credit scoring • Fraud detection • Personalized geo location • Intelligent agents • Forecasting • Fraud detection • Rotor position estimation • Predicting electricity prices • Optimizing milling parameters • Testing mathematical theorems • Grading exams • AI tutors • Gamification • Writing sports stories • Storytelling analysis • Analyzing seismic data • Estimating size oil reservoirs • Determine min gas miscibility • Fundraising • Optimized giving • Smart charity • Moral AI • Fraud detection • Malignant pleural mesothelioma • Orthodontic diagnosis • Lung CT classify • Clinical trial compliance • Adverse drug reaction prediction • Crop planting optimization • Develop drought tolerant crops • Self driving tractors • Electronic discovery • Patent infringement analysis • Contract • Program trading • High frequency trading • Algorithm trading • Index arbitrage • Personalized ad serving • Ad portfolio optimization POV – Bruce Daley My point of view – after years of false starts, the amalgamation of statistics, GPU chips, deep learning algorithms, and big data has made narrow applications of AI practical. The only limit to the problems they are being asked to solve to seems to be the human imagination New Algorithms GPUData DEEP LEARNING Probability and Statistics
- 12. Copyright © 2015 Earley Information Science12 Dr. Olly Downs • Responsible for the analytics strategy, technical approach and algorithm design and development for Globys’ marketing personalization technology platform (Amplero). • A machine learning scientist and serial technology entrepreneur, credited with bringing advanced analytics and machine learning methods to bear as the creative spark behind numerous early-stage technology companies. • Specializes in applying abstract analytical ideas from mathematical, physical and statistical science to problems in the real world and commercializing them into significant businesses. • Recently served as Chief Scientist at Atigeo, Chief Scientist at Mindset Media (sold to Meebo, February 2011) and Director of Research at Pelago (sold to GroupOn, April 2011). • As Principal Scientist at INRIX, the first technology spin-out from Microsoft Research, delivered a world-first in the provision of real- time traffic information using a nationwide network of GPS-enabled probe vehicles. • Holds Ph.D. and MA degrees in Applied & Computational Mathematics from Princeton University, and BA, MA and MSci degrees in Experimental & Theoretical Physics from the University of Cambridge, UK. Chief Scientist/CTO Globys @globysinc
- 13. Copyright © 2015 Earley Information Science13 Olly Downs – POV • Successful adoption of Machine Learning (ML) and Cognitive Science to drive business value is split across 3 segments – Large businesses for which these capabilities are core to their business – Businesses for which these capabilities are strategic and that can invest in team and tools – Businesses for which these capabilities are valuable but inaccessible • “Chasm” between 1 & {2,3} – Getting business-impacting results and operationalizing is difficult – Processes are unwieldy, and even best practices with teams and tools move slowly i.e., weeks, months – Hiring and retaining people with the right skills is not easy (as #1 consumes them) • Proposition for ML and Cognitive Computing to “Cross the Chasm” – Add value without hands on intervention – discover and act without human experts – Inform and educate on what is discovered – Reduce upfront investment hurdle [ 13 ]
- 14. Copyright © 2015 Earley Information Science14 Example – Applying Machine Learning to Marketing [ 14 ] Plan Development Launch Traditional Campaign Process Test Optimize Design Test Analyze Analyst/ Data Scientist Plan Development Launch Campaign Process With Amplero Discover “Team & Tools” Approach: Weeks of Design and Configuration 10’s of Marketing Contexts Weekly Analysis One-time Optimization BI Team Researching Results Machine Learning Approach: Configuration in Minutes 1,000’s of Marketing Contexts Daily Discovery Continuous Optimization BI Team Researching New Revenue Opportunities
- 15. Copyright © 2015 Earley Information Science15 Mitchell Shuster • Award-winning data scientist, physicist, and technology entrepreneur who seamlessly blends analytic and research knowledge honed in the academic realm with real-world technical and industry expertise. • As Informationist and Data Scientist at Knowledgent, the data and analytics firm, specializes in applying advanced analytics and data science concepts and techniques, including machine learning (Regression, Neural Nets, SVMs, Clustering, PCA, Anomaly Detection, etc.), to help client organizations gain actionable insights and competitive advantage. • Currently leveraging predictive analytics expertise to deliver data- driven models that improve patient outcomes, decrease costs, and increase operational efficiency for healthcare and life sciences organizations. • Previously in Research & Development at Intel Corporation, designed and developed basis for Intel's worldwide high-volume manufacturing at the newest technology node and was recognized for computational modeling and process implementation. • Earned Ph.D. degree in Physics and multiple research fellowships from Penn State University, where he authored research published in multiple prominent peer-reviewed scientific journals, and BA degree in Physics from Cornell University. Informationist and Data Scientist Knowledgent @Knowledgent
- 16. Copyright © 2015 Earley Information Science16 POV – Mitchell Shuster • Machine learning is a powerful tool for extracting information from data – It is non-trivial to frame the questions, prepare the data, and interpret the result in context – The right data is required to answer a given question – “Machine learning” is not a magic wand to solve all problems • Cognitive computing is an extension of compute capabilities into more human-like interactions – The primary distinguishing characteristics are context awareness and tolerance of ambiguity – At present, limited to specific tasks and contexts Beware the hype! What is possible is not always practical. What is practical is not always desirable.
- 17. Copyright © 2015 Earley Information Science17 Patrick Heffernan • Coverage and Focus Areas include IT Services, management consulting, global delivery, strategy and operations, cloud, intelligence cycle, project management, and client engagement • Directs the practice’s syndicated portfolio and cultivates and manages projects on topics ranging from management consulting to firms’ financial advisory services to emerging technologies. • Expertise in competitive intelligence, strategy, and global political- economic impacts on business cycles and consulting vendors. • Prior to joining TBR, was part of a Big Four firm’s competitive intelligence team, conducting field work and analysis. • Professional career started in diplomacy, with Middle East postings as a foreign service officer with the State Department and counterterrorism assignments with the National Security Council and the U.S. Department of the Treasury. • Received a B.A. from Washington and Lee University and an M.A. in foreign affairs from the University of Virginia. Practice Manager and Principal Analyst, Professional Services Practice Technology Business Research @TBR_PatrickH
- 18. Copyright © 2015 Earley Information Science18 POV – Patrick Heffernan • Cognitive computing and machine learning will increasingly have business impacts on --- – IT services vendors, including Accenture, Infosys, Wipro, IBM, and Cognizant, as those vendors must invest in people and capabilities to keep pace with competition and grow in new areas -- and these vendors are afraid of being too slow, too late, or too “me-too” for the market; – clients who appreciate the potential of what the vendors listed above can deliver, but don’t know how disruptive those changes will be – these companies are afraid of being too aggressive in adopting emerging technologies and paying a premium for what will soon be a commodity; and – employees at IT services vendors and at their clients who fear losing their jobs to “robots” – this is a recurring fear when emerging technologies take root, but just because it keeps coming up doesn’t mean it isn’t real
- 19. Copyright © 2015 Earley Information Science19 Discussion • OK, interesting stuff - where do I get started? • How do I tell what is possible from what is practical and achievable for my organization? • What kinds of problems can I solve? • What is the difference between “deep learning” and “machine learning”? • What kinds of education does my team need? Where do I get it? • What are the industries and applications that are most mature?
- 20. Copyright © 2015 Earley Information Science20 Thank you to our sponsors/producers www.computer.org/itpro http://www.henrystewartpublications.com/ama www.informationdevelopmentworld.com www.thecontentwrangler.com http://www.tbri.com
- 21. Copyright © 2015 Earley Information Science21 For more information • IT Professional Magazine - www.computer.org/itpro Next issue focuses on Analytics • Computing Edge http://www.computer.org/web/computingedge (highlights of IEEE publications) • Cognitive Computing and Big Data Analytics by Judith Hurwitz, et al http://www.amazon.com/Cognitive-Computing-Big-Data-Analytics/dp/1118896629 • Artificial Intelligence for Enterprise Applications https://www.tractica.com/research/artificial- intelligence-for-enterprise-applications/ (contact sales@tractica.com mention roundtable to get 10% discount) • Microsoft- Machine learning blog: http://blogs.technet.com/b/machinelearning/archive/2015/05.aspx • McKinsey- AI for the C Suite http://www.mckinsey.com/insights/strategy/artificial_intelligence_meets_the_c-suite • Stanford course in machine learning https://www.coursera.org/course/ml • Data science and machine learning resources: http://conductrics.com/data-science- resources/ • Video lectures: http://videolectures.net/Top/Computer_Science/Machine_Learning/
- 22. Copyright © 2015 Earley Information Science22 Mining Business Insights with Big Data Analytics and the Internet of Things Joanna Schloss Business Intelligence and Analytics Evangelist, Dell Software John Spooner Vice President, Platforms, Technology Business Research, Inc. Ram Sangireddy Dir of Product Management, Predictive & Analytics, Vitria Technology Bruce Daley Principal Analyst, Tractica Next Session: June 3rd 1pm EDT
- 23. Copyright © 2015 Earley Information Science23 Earley Information Science helps organizations establish a strong information architecture and content management foundation Specializing in making information more findable, useable and valuable to drive digital commerce innovation, enhance customer experience, and improve operational efficiency and effectiveness. Realize your digital transformation vision with EIS. Earley Information Science (EIS) A trusted information integrator Founded – 1994 Headquarters – Boston, MA www.earley.com Seth Earley, CEO Email: seth@earley.com Twitter: @sethearley LinkedIn: www.linkedin.com/in/sethearley
- 24. Copyright © 2015 Earley Information Science24 A Broad Spectrum of Business Solutions DIGITAL BUSINESS SOLUTIONS B2C Digital Commerce • Product Curation for a World-Class Product Catalog • Site Merchandising Taxonomy & Attribute Design • Information Architecture for Shopper Context B2B Digital Commerce • Product Search & Findability • Product Information Management • Product Knowledge Management Digital Workplace • Enterprise Content & Records Management • Information Architecture • Enterprise Knowledge Management
- 25. Copyright © 2015 Earley Information Science25 EIS Reference Architecture