Leading With Ai And Analytics Build Your Data Science Iq To Drive Business Value

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.

Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AI Key FeaturesLearn to create an operationally effective advanced analytics team in a corporate environmentSelect and undertake projects that have a high probability of success and deliver the improved top and bottom-line resultsUnderstand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your teamBook Description In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. What you will learnAvoid organizational and technological pitfalls of moving from a defined project to a production environmentEnable team members to focus on higher-value work and tasksBuild Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organizationOutsource certain projects to competent and capable third partiesSupport the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analyticsAnalyze the operational area, the processes, the data, and the organizational resistanceWho this book is for This book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful.

Review: "I would recommend this book to all prospective data scientists - as well as those software professionals who choose to transfer or migrate to the domain of data science. It is a useful addition to the body of work already available to guide project managers of data science projects." Lt Col (Dr) Rajesh Kapur (Retd), AI Investor, Asst. Prof. TIMSCDR, Hyderabad, India "It's a masterpiece of work for the aspiring leaders of data science and AI. It's also a guide for executives and investors to get maximum value from their investment in AI. Beginners in data science can also get the most out of this book.", Jay Ojha, Business intelligence and analytics manager, HCL Infosystem LtdWhy should you read this book? 87% of data science project fails to make to production in enterprises. Only 50% is the data leadership success rate. Is it not surprising to know when data science and AI are in the top trend? If you are looking for a career in data science or looking for leadership, these insights may disturb you. Don't worry, "Step up for Leadership in Enterprise Data Science & Artificial Intelligence with Big Data." will -Burst the myths around data science, AI & big data-Presents the real business scenarios -Take you on the journey of data science, AI & big data even if you are an ultimate beginner.-Introduce you to the essential skills of success in this field -Develop a leadership mindset by cutting edge methodologies & strategies-Make you aware of technical trends around it-Develop technical skills with R, Python, Machine learning with big data as well as business skills-Reduce failure possibility and increase the chance of success by covering the 360 degrees view of the field. Each day counts. So as your steps. Step up immediately and begin your journey to your dreams of data science and AI.

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.

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.

We are entering the era of digital transformation where human and artificial intelligence (AI) work hand in hand to achieve data driven performance. Today, more than ever, businesses are expected to possess the talent, tools, processes, and capabilities to enable their organizations to implement and utilize continuous analysis of past business performance and events to gain forward-looking insight to drive business decisions and actions. AI-Enabled Analytics in Business is your Roadmap to meet this essential business capability. To ensure we can plan for the future vs react to the future when it arrives, we need to develop and deploy a toolbox of tools, techniques, and effective processes to reveal forward-looking unbiased insights that help us understand significant patterns, relationships, and trends. This book promotes clarity to enable you to make better decisions from insights about the future. Learn how advanced analytics ensures that your people have the right information at the right time to gain critical insights and performance opportunities Empower better, smarter decision making by implementing AI-enabled analytics decision support tools Uncover patterns and insights in data, and discover facts about your business that will unlock greater performance Gain inspiration from practical examples and use cases showing how to move your business toward AI-Enabled decision making AI-Enabled Analytics in Business is a must-have practical resource for directors, officers, and executives across various functional disciplines who seek increased business performance and valuation.

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.

Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how. Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.