This book presents and develops the deep data analytics for providing the information needed for successful new product development. Deep Data Analytics for New Product Development has a simple theme: information about what customers need and want must be extracted from data to effectively guide new product decisions regarding concept development, design, pricing, and marketing. The benefits of reading this book are twofold. The first is an understanding of the stages of a new product development process from ideation through launching and tracking, each supported by information about customers. The second benefit is an understanding of the deep data analytics for extracting that information from data. These analytics, drawn from the statistics, econometrics, market research, and machine learning spaces, are developed in detail and illustrated at each stage of the process with simulated data. The stages of new product development and the supporting deep data analytics at each stage are not presented in isolation of each other, but are presented as a synergistic whole. This book is recommended reading for analysts involved in new product development. Readers with an analytical bent or who want to develop analytical expertise would also greatly benefit from reading this book, as well as students in business programs.

This book connects predictive analytics and simulation analytics, with the end goal of providing Rich Information to stakeholders in complex systems to direct data-driven decisions. Readers will explore methods for extracting information from data, work with simple and complex systems, and meld multiple forms of analytics for a more nuanced understanding of data science. The methods can be readily applied to business problems such as demand measurement and forecasting, predictive modeling, pricing analytics including elasticity estimation, customer satisfaction assessment, market research, new product development, and more. The book includes Python examples in Jupyter notebooks, available at the book's affiliated Github. This volume is intended for current and aspiring business data analysts, data scientists, and market research professionals, in both the private and public sectors.

This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of: 1. statistical, econometric, and machine learning techniques; 2. data handling capabilities; 3. at least one programming language. Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research.

This book develops survey data analysis tools in Python, to create and analyze cross-tab tables and data visuals, weight data, perform hypothesis tests, and handle special survey questions such as Check-all-that-Apply. In addition, the basics of Bayesian data analysis and its Python implementation are presented. Since surveys are widely used as the primary method to collect data, and ultimately information, on attitudes, interests, and opinions of customers and constituents, these tools are vital for private or public sector policy decisions. As a compact volume, this book uses case studies to illustrate methods of analysis essential for those who work with survey data in either sector. It focuses on two overarching objectives: Demonstrate how to extract actionable, insightful, and useful information from survey data; and Introduce Python and Pandas for analyzing survey data.

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Business decisions in any context—operational, tactical, or strategic—can have considerable consequences. Whether the outcome is positive and rewarding or negative and damaging to the business, its employees, and stakeholders is unknown when action is approved. These decisions are usually made under the proverbial cloud of uncertainty. With this practical guide, data analysts, data scientists, and business analysts will learn why and how maximizing positive consequences and minimizing negative ones requires three forms of rich information: Descriptive analytics explores the results from an action—what has already happened. Predictive analytics focuses on what could happen. The third, prescriptive analytics, informs us what should happen in the future. While all three are important for decision-makers, the primary focus of this book is on the third: prescriptive analytics. Author Walter R. Paczkowski, Ph.D. shows you: The distinction among descriptive, predictive, and prescriptive analytics How predictive analytics produces a menu of action options How prescriptive analytics narrows the menu of action options The forms of prescriptive analytics: eight prescriptive methods Two broad classes of these methods: non-stochastic and stochastic How to develop prescriptive analyses for action recommendations Ways to use an appropriate tool-set in Python

Integrating intelligent systems and internet of things (IoT) into clinical health is crucial for enhancing patient care and operational efficiency. These technologies enable real-time data collection and analysis, facilitating personalized treatment plans and improving diagnostic accuracy. Together innovations can streamline workflows, reduce costs, and ultimately lead to better health outcomes for patients. It is essential to explore how these technologies can be implemented into healthcare. Intelligent Systems and IoT Applications in Clinical Health explores and elucidates the integration of AI, IoT, and blockchain technologies in healthcare. It advances current research by providing comprehensive insights into how these technologies can be leveraged to enhance patient care, improve operational efficiency, and ensure data security. Covering topics such as clinical healthcare, digital health experience, and monitoring systems, this book is an excellent resource for researchers, academicians, medical professionals, medical administrators, educators, graduate and postgraduate students, and more.

“Refreshingly thought-provoking...” – The Financial Times The essential playbook for the future of your business What To Do When Machines Do Everything is a guidebook to succeeding in the next generation of the digital economy. When systems running on Artificial Intelligence can drive our cars, diagnose medical patients, and manage our finances more effectively than humans it raises profound questions on the future of work and how companies compete. Illustrated with real-world cases, data, and insight, the authors provide clear strategic guidance and actionable steps to help you and your organization move ahead in a world where exponentially developing new technologies are changing how value is created. Written by a team of business and technology expert practitioners—who also authored Code Halos: How the Digital Lives of People, Things, and Organizations are Changing the Rules of Business—this book provides a clear path to the future of your work. The first part of the book examines the once in a generation upheaval most every organization will soon face as systems of intelligence go mainstream. The authors argue that contrary to the doom and gloom that surrounds much of IT and business at the moment, we are in fact on the cusp of the biggest wave of opportunity creation since the Industrial Revolution. Next, the authors detail a clear-cut business model to help leaders take part in this coming boom; the AHEAD model outlines five strategic initiatives—Automate, Halos, Enhance, Abundance, and Discovery—that are central to competing in the next phase of global business by driving new levels of efficiency, customer intimacy and innovation. Business leaders today have two options: be swallowed up by the ongoing technological evolution, or ride the crest of the wave to new profits and better business. This book shows you how to avoid your own extinction event, and will help you; Understand the untold full extent of technology's impact on the way we work and live. Find out where we're headed, and how soon the future will arrive Leverage the new emerging paradigm into a sustainable business advantage Adopt a strategic model for winning in the new economy The digital world is already transforming how we work, live, and shop, how we are governed and entertained, and how we manage our money, health, security, and relationships. Don't let your business—or your career—get left behind. What To Do When Machines Do Everything is your strategic roadmap to a future full of possibility and success. Or peril.

Organizations need to stay competitive and transition from a linear make-use-dispose supply chain model to a sustainable one. This book covers techniques and basic principles, historical developments and recent issues facing the adoption of a circular supply chain model. The Circular Supply Chain: Basic Principles and Techniques presents the key principles and techniques for the effective integration of a circular economy into supply chains. It discusses sustainability, digitization and the application of blockchain to enhance operations within the realm of Industry 4.0. Principles to assist managers in effectively adopting circularity business models for sustainability improvements are provided, along with the historical background, so the reader can have a better understanding for implementation. Case studies and reading comprehension questions are also offered to help with the effective integration of a circular economy into supply chains. This book is written to assist students, practicing engineers and business professionals that work in the industrial and manufacturing sectors, supply chain management, and with advanced technologies associated with Industry 4.0, sustainability, blockchain and digitalization integration techniques of circular supply chains.

Oracle Endeca Information Discovery Best Practices Maximize the powerful capabilities of this self-service enterprise data discovery platform. Master Competitive Analytics with Oracle Endeca Information Discovery reveals how to unlock insights from any type of data, regardless of structure. The first part of the book is a complete technical guide to the product's architecture, components, and implementation. The second part presents a comprehensive collection of business analytics use cases in various industries, including financial services, healthcare, research, manufacturing, retail, consumer packaged goods, and public sector. Step-by-step instructions on implementing some of these use cases are included in this Oracle Press book. Install and manage Oracle Endeca Server Design Oracle Endeca Information Discovery Studio visualizations to facilitate user-driven data exploration and discovery Enable enterprise-driven data exploration with Oracle Endeca Information Discovery Integrator Develop and implement a fraud detection and analysis application Build a healthcare correlation application that integrates claims, patient, and operations analysis; partners; clinical research; and remote monitoring Use an enterprise architecture approach to incrementally establish big data and analytical capabilities