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Genevieve Hayes Consulting Episode 48: Overcoming the Machine Learning Deployment Challenge It's been 12 years since Thomas H Davenport and DJ Patil first declared data science to be “the sexiest job of the 21st century” and in that time a lot has changed. Universities have started offering data science degrees; the number of data scientists has grown exponentially; and generative AI technologies, such as Chat-GPT and Dall-E have transformed the world.Yet, throughout that time, one thing has remained the same. Most machine learning projects still fail to deploy.However, it's not the technical capabilities of data scientists that let them down – those are now better than ever before. Rather, “it's the lack of a well-established business practice that is almost always to blame.”In this episode, Dr Eric Siegel joins Dr Genevieve Hayes to discuss bizML, the new “gold-standard”, six-step practice he has developed “for ushering machine learning projects from conception to deployment.” Guest Bio Dr Eric Siegel is a leading machine learning consultant and the CEO and co-founder of Gooder AI. He is also the founder of the long-running Machine Learning Week conference series; author of the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die and the recently released The AI Playbook; and host of The Dr Data Show podcast. Highlights (01:21) Challenges in machine learning deployment(05:00) The importance of business involvement in ML projects(15:39) Defining bizML and its steps(25:32) Understanding predictive analytics(26:52) Challenges in model deployment and MLOps(29:12) BizML for generative and causal AI(31:25) Exploring uplift modeling(35:45) Gooder AI: bridging the gap between data science and business value(45:45) Beta testing and future plans for Gooder AI(47:35) Final advice for data scientistsb Links BizML website Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 48: Overcoming the Machine Learning Deployment Challenge first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.
It's been 12 years since Thomas H Davenport and DJ Patil first declared data science to be “the sexiest job of the 21st century” and in that time a lot has changed. Universities have started offering data science degrees; the number of data scientists has grown exponentially; and generative AI technologies, such as Chat-GPT and Dall-E have transformed the world.Yet, throughout that time, one thing has remained the same. Most machine learning projects still fail to deploy.However, it's not the technical capabilities of data scientists that let them down – those are now better than ever before. Rather, “it's the lack of a well-established business practice that is almost always to blame.”In this episode, Dr Eric Siegel joins Dr Genevieve Hayes to discuss bizML, the new “gold-standard”, six-step practice he has developed “for ushering machine learning projects from conception to deployment.”Guest BioDr Eric Siegel is a leading machine learning consultant and the CEO and co-founder of Gooder AI. He is also the founder of the long-running Machine Learning Week conference series; author of the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die and the recently released The AI Playbook; and host of The Dr Data Show podcast.Highlights(01:21) Challenges in machine learning deployment(05:00) The importance of business involvement in ML projects(15:39) Defining bizML and its steps(25:32) Understanding predictive analytics(26:52) Challenges in model deployment and MLOps(29:12) BizML for generative and causal AI(31:25) Exploring uplift modeling(35:45) Gooder AI: bridging the gap between data science and business value(45:45) Beta testing and future plans for Gooder AI(47:35) Final advice for data scientistsbLinksBizML websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE
In this episode, Casey Green – creator of the Campus Computing Project and award-winning industry analyst in higher ed – dives deep into past myths, present trends, and future practices that for higher ed leaders to consider as they drive their institutions' strategy, evolution, and operation. Casey covers all the bases from multiple perspectives, exploring these topics and their impacts on CIOs, provosts, CFOs, presidents and governing boards. References: Casey Green – creator of the Campus Computing Project and award-winning industry analyst in higher ed https://www.campuscomputing.net/caseygreenCampus Computing Project https://www.campuscomputing.net/ On moving only certain applications to the cloud:Dr. Miloš Topić, Vice President for Information Technology & Chief Digital Officer, Grand Valley State University, TRANSFORMED Episode 66, 9:05Article summarizing latest Wavestone survey on data analytics:Survey: GenAI Is Making Companies More Data Oriented, Thomas H. Davenport and Randy Bean, Harvard Business Review, 1/15/24On funding a data scient program centrally to incent collaboration among deans:Dr. Anthony Wutoh, Provost and Chief Academic Officer, Howard University, TRANSFORMED Episode 47, 17:25Articles summarizing how Georgia State University dramatically improved student retention across all demographic categories:Still Not Using Data to Inform Decisions and Policy, Kenneth C. Green, Inside Higher Ed/Digital Tweed Blog, 2/25/20Georgia State, Leading U.S. in Black Graduates, Is Engine of Social Mobility, Richard Fausset, New York Times, 5/15/18On higher ed executives needing to get a technology tutor:Presidents and Digital Learning: Get a Student Tutor, Kenneth C. Green, Inside Higher Ed/Digital Tweed Blog, 4/12/18On faculty members innovating with subdivided classrooms:Dr. Bill Coppola, President, Tarrant County College, Southeast Campus, TRANSFORMED Episode 50, 4:22
The product. Secret hat time with Sgoti. Source: What is a "product":? noun; Something produced by human or mechanical effort or by a natural process, as. noun; An item that is made or refined and marketed. Supporting Source: The Product model: ... In addition, a specific unit of a product is often (and in some contexts must be) identified by a serial number, which is necessary to distinguish products with the same product definition. Supporting Source: What is Commodification? Within a capitalist economic system, commodification is the transformation of things such as goods, services, ideas, nature, personal information, people or animals into objects of trade or commodities. A commodity at its most basic, according to Arjun Appadurai, is "anything intended for exchange," or any object of economic value. Commodification is often criticized on the grounds that some things ought not to be treated as commodities-for example, water, education, data, information, knowledge, human life, and animal life. Supporting Source: What is Attention economy? Attention economics is an approach to the management of information that treats human attention as a scarce commodity and applies economic theory to solve various information management problems. According to Matthew Crawford, "Attention is a resource-a person has only so much of it." Thomas H. Davenport and John C. Beck add to that definition: Attention is focused mental engagement on a particular item of information. Items come into our awareness, we attend to a particular item, and then we decide whether to act. Supporting Source: What is Surveillance capitalism? Surveillance capitalism is a concept in political economics which denotes the widespread collection and commodification of personal data by corporations. This phenomenon is distinct from government surveillance, though the two can reinforce each other. The concept of surveillance capitalism, as described by Shoshana Zuboff, is driven by a profit-making incentive, and arose as advertising companies, led by Google's AdWords, saw the possibilities of using personal data to target consumers more precisely. Source: AirTags are being used to track people and cars... Source: Exposure Notifications: Help slow the spread of COVID-19... Source: FTC: Ring employees spied on users; cameras were unsecure... Source: FDA Takes Additional Action in Fight Against COVID-19 By Issuing Emergency Use Authorization... This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Don't underestimate Milwaukee - a thriving city with not only a rich history, but a bright future with opportunities to live, work and play.Kathy Henrich is CEO of the Milwaukee Tech Hub Coalition, a nonprofit on a mission to grow the region's tech ecosystem while creating life-altering opportunities for tech talent. We dive deep into Milwaukee's unique tech landscape (and the blending of traditional industry with high-tech), the status of our tech talent pool, and the impact of artificial intelligence on business and work.This is an insight-packed episode that can help organizations in similar regions across the U.S. strengthen their own technology ecosystem.3 Big Takeaways from this episode:Our approach to the tech talent gap needs to change: Tech occupations are up 30% in the last 10 years, with no sign of slowing down. Every year there are 100,000 new tech jobs nationwide, with only 90,000 graduates for those roles (with others leaving the workforce). On top of that, every sector is now tech-based. Tech can't just rely on traditional higher education for its talent pipeline. Instead, we must embrace other methods, like apprenticeships, skills-based training, hiring workers from non-traditional places.People are looking for these 3 things in a company. If you want to hire great tech talent, make sure you: 1) Show people how they can have a great career at your company, with opportunities for mobility both laterally and vertically, 2) Help people solve problems that matter to the world (give them purpose in their work), 3) Let them work on advanced technologies / show them their career will be cutting-edge.Leading companies are implementing artificial intelligence in 3 areas: 1) AI in their processes (on the business side and manufacturing systems), 2) AI embedded in products (do you have products to which you can add AI to make a better customer experience?), 3) AI in gaining customer insights and enhancing customer service.Resources mentioned in this episode:To learn more about the Milwaukee Tech Hub Coalition, visit their website: https://www.mketech.org/Other resources mentioned in this episode:Book: All-in On AI: How Smart Companies Win Big with Artificial Intelligence by Thomas H. Davenport and Nitin MittalAI Data Specialist degree at Waukesha County Technical College (WCTC)Northwestern Mutual Data Science InstituteAI programs at the Milwaukee School of Engineering (MSOE)Connect with the Milwaukee Tech Hub Coalition online:LinkedIn | X (Twitter) | Facebook | Instagram | Connect with Kathy on LinkedInEpisode pageInstagram - Facebook - YouTube - TikTok - Twitter - LinkedIn
He has skin in the game as a top banker -- and saw the power of AI long before others did. Piyush Gupta joins Vasant Dhar in episode 63 of Brave New World to share his excitement for the present -- and his vision for the future. Useful resources: 1. Piyush Gupta at DBS Bank, LinkedIn, Wikipedia and Twitter. 2. Tom Davenport on Artificial Intelligence in Business -- Episode 56 of Brave New World. 3. David Yermack on The Crypto Revolution -- Episode 30 of Brave New World. 4. SVB was a hedge fund in disguise–and the banking crisis is an overreaction -- Vasant Dhar. 5. The Three Laws of Robotics -- Isaac Asimov. 6. The Passions and the Interests -- Albert O Hirschman. 7. All-in On AI -- Thomas H Davenport and Nitin Mittal. Check out Vasant Dhar's newsletter on Substack. Subscription is free!
Eric Siegel is a leading consultant and former Columbia University professor. He is the founder of the popular Predictive Analytics World and Deep Learning World conference series. In this episode, Eric shares his decades of experience in predictive analytics. He discusses why ML is useful, and how predictive analytics have been used in business. Eric shares his view on prescriptive analytics, AI, and also explains uplift-modelling concepts, and why it is hard and so powerful. Eric's RecommendationsBooks:Competing on Analytics: Updated with a New Introduction, The New Science of Winning by Thomas H. Davenport, Jeanne G. Harris, 2017Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, by Dean Abbot Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel Papers: Sculley, David, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. "Hidden technical debt in machine learning systems." Advances in neural information processing systems 28 (2015). Elder IV, John F. "The generalization paradox of ensembles." Journal of Computational and Graphical Statistics 12, no. 4 (2003): 853-864.
Ep 427 - Working with Artificial Intelligence Guest: Thomas Davenport By Stuart McNish “The world does not lack for management ideas [sic]. Thousands of researchers, practitioners, and other experts produce tens of thousands of articles, books, papers, posts, and podcasts each year. But only a scant few promise to truly move the needle on practice, and fewer still date to reach into the future of what management will become. It is this rare breed of idea – meaningful to practice, grounded in evidence, and built for the future – that we seek to present,” says Robert Holland, the Editor-in-chief of MIT Sloan Management Review. “Working with AI, Real Stories of Human-Machine Collaboration” endeavours to show that the needle can and will move through the addition of artificial intelligence to the complex work of today's world. Thomas H. Davenport, one of the co-authors of the book says, “There is no shortage of commentary on what artificial intelligence will do to human jobs. It's easy to find a multiplicity of predictions, prescriptions, or denunciations. It is not so easy, however, to find descriptions of how people work day-to-day with smart machines.” We invited Thomas Davenport to join us for a Conversation That Matters about our emerging and ever-expanding relationship with a technology that scares a wide range of people, including Elon Musk and Bill Gates.
In this episode we talked to Tom Davenport and Laks Srinivasan from Return on AI Institute (ROAI) about how AI is empowering and challenging organizational models worldwide, and how the platform business model is often based on AI capabilities in the background. Tom is a world-renowned thought leader and author on AI. He is the President's Distinguished Professor of Information Technology and Management at Babson College, as well as a fellow at the MIT Initiative on the Digital Economy, a visiting professor at Oxford's Saïd Business School, and is the Chairman of ROAI. Laks is a data and analytics executive with more than 15 years of experience in management, entrepreneurship, and innovation roles to help clients create measurable value from AI. He is a co-founder and Managing Director at ROAI and former CEO of Opera Solutions (ElectrifAI now), an applied AI solutions company with 500+ employees globally as well as the winner of the Netflix Prize and several Kaggle AI competitions. Tom and Laks explore with us how different forms of artificial intelligence might transform product teams at companies around the globe. In the second part of this episode, Tom and Laks offer concrete examples of companies that have created new business models powered by AI, as well as suggestions on what traditional organizations should look at when preparing to adopt artificial intelligence. At Boundaryless we're partnering with ROAI to explore the convergence between AI and Platforms, check out our research and services here: https://blss.io/ROAI Key highlights
If you read nothing else on the principles and practices that lead to successful digital transformation, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you reinvent your digital strategy, overcome barriers to change, and win in the continuously connected world. This book will inspire you to: Devise an industry-transforming business model Minimize risk using discovery-driven transformation Leverage torrents of data more strategically Prepare your employees for the future of work Prioritize the right initiatives Compete in the age of AIThis collection of articles includes "Discovery-Driven Digital Transformation," by Rita McGrath and Ryan McManus; "The Transformative Business Model," by Stelios Kavadias, Kostas Ladas, and Christoph Loch; "Digital Doesn't Have to Be Disruptive," by Nathan Furr and Andrew Shipilov; "What's Your Data Strategy?," by Leandro DalleMule and Thomas H. Davenport; "Competing in the Age of AI," by Marco Iansiti and Karim R. Lakhani; "Building the AI-Powered Organization," by Tim Fountaine, Brian McCarthy, and Tamim Saleh; "How Smart, Connected Products Are Transforming Companies," by Michael E. Porter and James E. Heppelmann; "The Age of Continuous Connection," by Nicolaj Siggelkow and Christian Terwiesch; "The Problem with Legacy Ecosystems," by Maxwell Wessel, Aaron Levie, and Robert Siegel; "Your Workforce Is More Adaptable Than You Think," by Joseph B. Fuller, Judith K. Wallenstein, Manjari Raman, and Alice de Chalendar; "How Apple Is Organized for Innovation," by Joel M. Podolny and Morten T. Hansen; and "Digital Transformation Comes Down to Talent in Four Key Areas," by Thomas H. Davenport and Thomas C. Redman.HBR's 10 Must Reads paperback series is the definitive collection of books for new and experienced leaders alike. Leaders looking for the inspiration that big ideas provide, both to accelerate their own growth and that of their companies, should look no further. HBR's 10 Must Reads series focuses on the core topics that every ambitious manager needs to know: leadership, strategy, change, managing people, and managing yourself. Harvard Business Review has sorted through hundreds of articles and selected only the most essential reading on each topic. Each title includes timeless advice that will be relevant regardless of an ever‐changing business environment.
In this podcast, I will be joined by Thomas H. Davenport, who is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative for the Digital Economy, and a Senior Advisor to Deloitte Analytics. He is also the author of 20 books and many articles in Harvard Business Review and Sloan Management Review.We will be taking a look at the future of AI, the impact of the global pandemic, as well as the key tips to deliver successful AI projects in the future.
In our first ever episode, we talk with data scientist Skye Reymond. Skye lays the groundwork for our series focusing on how data and analytics are key competitive differentiators for manufacturing companies. Here are just a few highlights: 1:44: Start where you are Skye: There’s a book that I reference when a company is assessing where they stand in their analytical strategy. It’s called ‘Competing on Analytics.’ It outlines 5 levels of maturity...2:22: Stage 1: Analytically Impaired Skye: This is when a company is flying blind, they’re very reactive, the systems might not be integrated, and their data is poor quality.3:13: Stage 2: Localized AnalyticsSkye: These companies collect transactional data, something like you would see in an ERP system. But it’s still very reactive.5:58: Stage 3: Analytical AspirationsSkye: These companies are making investments in the right talent and tools. They’re preparing to use analytics to improve a distinctive capability of their company. They have a roadmap to automation.6:57: The Road MapSkye: A really good place to start is in the area of your business that you believe is going to be your differentiator. So if you have a repair shop, maybe your differentiator is service. If you’re a logistics company, your differentiator is going to be speed of delivery...8:19: Stage 4: An Analytical CompanySkye: This is an enterprise-wide analytical strategy that’s viewed as a company priority… They're often using more automated analytics and more advanced modeling techniques… things like artificial intelligence, time series forecasting... 9:30: Building A Data-Driven Culture Skye: It really needs to start from the top down... The next step is to give the people who are doing the jobs day-to-day some ownership and input. These are the experts who can give you some of the most valuable insight as you’re figuring out your analytics strategy. 10:31: Stage 5: The Ultimate LevelSkye: This is [a company] using analytics as a key component in their competitive strategy…analytics are fully automated, completely integrated. Decisions organization-wide are data-driven. Analytics are the central theme to how the organization operates. 11:52: The Last DifferentiatorSkye: Analytics are really going to be the last differentiator. Analytics are going to be the big advantage that makes companies win over others. 16:05: Descriptive, Predictive, and Prescriptive AnalyticsAndrew: I like this concept because it helps you connect your analytical strategy toward tangible goals for your business and it really guides your thinking towards asking the right questions... References: Competing on Analytics by Thomas H. Davenport6 Steps Manufacturers Should Take to Optimize and Monetize Their DataIDC: Revenues for Big Data and Business AnalyticsTesla’s Over the Air FixConnecting Business Data to Business Goals
This episode is about books, we swear. Mike and Tammy each share three book recommendations from or adjacent to planning and analytics.Titles:Competing on Analytics by Thomas H. Davenport and Jeanne G. HarrisThe Death and Life of Great American Cities by Jane JacobsThe Signal and the Noise by Nate SilverHappy City by Charles MontgomeryCreative Confidence by Tom and David KelleyThe Devil in the White City by Erik LarsonSong credit: 300 MB by Neil Cicierega (from the album Mouth Moods)
Recorded Future - Inside Threat Intelligence for Cyber Security
Our guest this week is Thomas H. Davenport. He’s a world-renowned thought leader and author, and is the president’s distinguished professor of information technology and management at Babson College, a fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte Analytics. Tom Davenport is author and co-author of 15 books and more than 100 articles. He helps organizations to revitalize their management practices in areas such as analytics, information and knowledge management, process management, and enterprise systems. His most recent book is “The AI Advantage: How to Put the Artificial Intelligence Revolution to Work (Management on the Cutting Edge).” Returning to the show to join the discussion is Recorded Future’s chief data scientist, Bill Ladd.
Our guest this week is Thomas H. Davenport. He's a world-renowned thought leader and author, and is the president's distinguished professor of information technology and management at Babson College, a fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte Analytics. Tom Davenport is author and co-author of 15 books and more than 100 articles. He helps organizations to revitalize their management practices in areas such as analytics, information and knowledge management, process management, and enterprise systems. His most recent book is “The AI Advantage: How to Put the Artificial Intelligence Revolution to Work (Management on the Cutting Edge).” Returning to the show to join the discussion is Recorded Future's chief data scientist, Bill Ladd.
Many companies and observers are excited about the possibility of competitive advantage from analytics on "big data," but many don’t understand the differences between big and small data analytics. There are also substantial differences in how large, established organizations and startups approach big data. In this presentation, Tom Davenport will describe what organizations are attempting to accomplish with big data. Several leading examples of companies—large firms and startup—that are aggressively pursuing big data will be presented. Davenport will then describe how big data differs from previous approaches to analytics and data management on small data. Finally, he'll address some of the key factors that big and small data analytics have in common, and will describe his ideas on their integration using the “Analytics 3.0” framework he has developed.
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