Lead your organization to become evidence-driven Data. It’s the benchmark that informs corporate projections, decision-making, and analysis. But, why do many organizations that see themselves as data-driven fail to thrive? In Leading with AI and Analytics, two renowned experts from the Kellogg School of Management show business leaders how to transform their organization to become evidence-driven, which leads to real, measurable changes that can help propel their companies to the top of their industries. The availability of unprecedented technology-enabled tools has made AI (Artificial Intelligence) an essential component of business analytics. But what’s often lacking are the leadership skills to integrate these technologies to achieve maximum value. Here, the authors provide a comprehensive game plan for developing that all-important human factor to get at the heart of data science: the ability to apply analytical thinking to real-world problems. Each of these tools and techniques comes to powerful life through a wealth of powerful case studies and real-world success stories. Inside, you’ll find the essential tools to help you: Develop a strong data science intuition quotient Lead and scale AI and analytics throughout your organization Move from “best-guess” decision making to evidence-based decisions Craft strategies and tactics to create real impact Written for anyone in a leadership or management role—from C-level/unit team managers to rising talent—this powerful, hands-on guide meets today’s growing need for real-world tools to lead and succeed with data.

Summary Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Succeeding with AI sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Succeeding with AI requires talent, tools, and money. So why do many well-funded, state-of-the-art projects fail to deliver meaningful business value? Because talent, tools, and money aren’t enough: You also need to know how to ask the right questions. In this unique book, AI consultant Veljko Krunic reveals a tested process to start AI projects right, so you’ll get the results you want. About the book Succeeding with AI sets out a framework for planning and running cost-effective, reliable AI projects that produce real business results. This practical guide reveals secrets forged during the author’s experience with dozens of startups, established businesses, and Fortune 500 giants that will help you establish meaningful, achievable goals. In it you’ll master a repeatable process to maximize the return on data-scientist hours and learn to implement effectiveness metrics for keeping projects on track and resistant to calcification. What's inside Where to invest for maximum payoff How AI projects are different from other software projects Catching early warnings in time to correct course Exercises and examples based on real-world business dilemmas About the reader For project and business leadership, result-focused data scientists, and engineering teams. No AI knowledge required. About the author Veljko Krunic is a data science consultant, has a computer science PhD, and is a certified Six Sigma Master Black Belt. Table of Contents: 1. Introduction 2. How to use AI in your business 3. Choosing your first AI project 4. Linking business and technology 5. What is an ML pipeline, and how does it affect an AI project? 6. Analyzing an ML pipeline 7. Guiding an AI project to success 8. AI trends that may affect you

The ultimate marketing resource from the world’s leading scholars From the world’s #1 MBA marketing program comes the latest edition of Kellogg on Marketing, presented by Philip Kotler and Alexander Chernev. With hundreds of pages of brand-new material on timely topics, like creating value to disrupt markets, defensive marketing strategies, strategic customer management, building strong brands, and marketing in the metaverse, the book explores foundational and advanced topics in marketing management. You’ll discover a renewed focus on digital transformation and data analytics, as well as comprehensive explanations of the strategic and tactical aspects of effective marketing. From managing business growth to identifying target customers, developing a meaningful value proposition, and data-driven marketing, every area relevant to marketing professionals is covered by expert contributors possessing unique insights into their respective competencies. Readers will also find: Discussions of the unique challenges facing brands in designing and managing their image and techniques for building resilient brands Strategies for creating loyal customers and developing personalization at scale Strategies for designing effective omni-channel marketing platforms Strategies for crafting a successful cross-platform communications campaigns Discussions on the application of data analytics and artificial intelligence to the creation of successful marketing programs An indispensable resource for any professional expected to contribute to their organization’s marketing efforts or business growth, Kellogg on Marketing, Third Edition, also earn a place in curricula of the business school educating the next generation of business leaders.

The use of data and analytics significantly improves project performance, but it requires a cultural foundation that connects and engages people, enables evidence-based thinking and facilitates new capabilities. In an era of rapid change and an ever-increasing flow of information, data is a highly-valued asset. Organizations are transforming business areas into data-driven practices to make better and faster decisions and respond accurately to fast-changing market behaviors and demands. The project management domain cannot afford to be left behind. Old practices will not serve the sector in the twenty-first century. That means project delivery functions must embrace new and innovative ways to deliver change. In this book, Marcus Glowasz argues the urgent need to employ data and analytics for improved project performance. Leading Projects with Data is full of actionable insights to drive the behaviors and culture shifts necessary to ensure a successful transition to data-informed project delivery practices. A thriving practice needs people with the mindset to collaborate across boundaries, learn from failure, adapt to a new normal of frequent disruption and change, and value knowledge. Diversity, transparency, and critical thinking are key drivers in the new world of project management. The future is here. Embrace it.

This book presents a comprehensive and innovative exploration of the role of Artificial Intelligence (AI) in the digital marketing arena. It begins with historical context and fundamental AI principles, and subsequently, details AI's applications across a spectrum of areas, including data analytics, content creation, customer targeting, Search Engine Optimization (SEO), Pay Per Click (PPC) advertising, Social Media Marketing (SMM), and Email Marketing. A distinctive feature lies in the author's extensive real-world experience, providing many useful business applications of AI. The book is designed for marketing professionals, business executives, educators, and students, and offers numerous examples and case studies.

For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisionsAnalytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organisations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.

This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)