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eBooks / Books
The analytics lifecycle toolkit: A practical guide for an effective analytics capability. by
An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.
Analyzing qualitative data: Systematic approaches by
The fully updated Second Edition presents systematic methods for analyzing qualitative data with clear and easy-to-understand steps. Real examples drawn from social science and health literature along with carefully crafted, hands-on exercises at the end of each chapter allow readers to master key techniques and apply them to their own disciplines.
Big data: An art of decision making by
Manipulating and processing masses of digital data is never a purely technical activity. It requires an interpretative and exploratory outlook - already well known in the social sciences and the humanities - to convey intelligible results from data analysis algorithms and create new knowledge. Big Data is based on an inquiry of several years within Proxem, a software publisher specializing in big data processing. The book examines how data scientists explore, interpret and visualize our digital traces to make sense of them, and to produce new knowledge. Grounded in epistemology and science and technology studies, Big Data offers a reflection on data in general, and on how they help us to better understand reality and decide on our daily actions.
The decision maker's handbook to data science: A guide for non-technical executives, managers, and founders by
Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don't realize is that data science is in fact quite multidisciplinary--useful in the hands of business analysts, communications strategists, designers, and more. With the second edition of The Decision Maker's Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker's Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will Learn Understand how data science can be used within your business. Recognize the differences between AI, machine learning, and statistics. Become skilled at thinking like a data scientist, without being one. Discover how to hire and manage data scientists. Comprehend how to build the right environment in order to make your organization data-driven. Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science.
Information, knowledge and agile creativity by
Information, Knowledge and Agile Creativity will enlighten entrepreneurs, and is ideal for facilitating an organization's ability to react and adapt to its environment. Creativity is a system that engenders innovation. While integral at the conception stage, it is also important before and after this phase. This book offers a collection of tools, as well as a methodology, to estimate the agility of an organization to generate and transform ideas into solutions that are not only new but also adapted to their users. To this end, this book presents strategic foresight and problem comprehension methods; tools of sharing and visual information formatting; animation tips for creativity workshops; techniques for generating ideas; and tools for visualizing and mapping ideas, information, and knowledge.
Making sense of field research: A practical guide for information designers. by
Learn how to use field research to bring essential people-centred insights to your information design projects. Information design is recognized as the practice of making complex data and information understandable for a particular audience, but what's often overlooked is the importance of understanding the audience themselves during the information design process. Rather than rely on intuition or assumptions, information designers need evidence gathered from real people about how they think, feel, and behave in order to inform the design of effective solutions. To do this, they need field research. If you're unsure about field research and how it might fit into a project, this book is for you. This text presents practical, easy-to-follow instructions for planning, designing, and conducting a field study, as well as guidance for making sense of field data and translating findings into action. The selection of established methods and techniques, drawn from social sciences, anthropology, and participatory design, is geared specifically toward information design problems. Over 80 illustrations and five real-world case studies bring key principles and methods of field research to life. Whether you are designing a family of icons or a large-scale signage system, an instruction manual or an interactive data visualization, this book will guide you through the necessary steps to ensure you are meeting people's needs.
The organisation of tomorrow: How AI, blockchain and analytics turn your business into a data organisatio by
The Organisation of Tomorrow presents a new model of doing business and explains how big data analytics, blockchain and artificial intelligence force us to rethink existing business models and develop organisations that will be ready for human-machine interactions. It also asks us to consider the impacts of these emerging information technologies on people and society. Big data analytics empowers consumers and employees. This can result in an open strategy and a better understanding of the changing environment. Blockchain enables peer-to-peer collaboration and trustless interactions governed by cryptography and smart contracts. Meanwhile, artificial intelligence allows for new and different levels of intensity and involvement among human and artificial actors. With that, new modes of organising are emerging: where technology facilitates collaboration between stakeholders; and where human-to-human interactions are increasingly replaced with human-to-machine and even machine-to-machine interactions. This book offers dozens of examples of industry leaders such as Walmart, Telstra, Alibaba, Microsoft and T-Mobile, before presenting the D2+ A2 model - a new model to help organisations datafy their business, distribute their data, analyse it for insights and automate processes and customer touchpoints to be ready for the data-driven and exponentially-changing society that is upon us This book offers governments, professional services, manufacturing, finance, retail and other industries a clear approach for how to develop products and services that are ready for the twenty-first century. It is a must-read for every organisation that wants to remain competitive in our fast-changing world.
Principles of strategic data science: Creating value from data, big and small by
Principles of Strategic Data Science is created to help you join the dots between mathematics, programming, and business analysis. With a unique approach that bridges the gap between mathematics and computer science, this book takes you through the entire data science pipeline. The book begins by explaining what data science is and how organizations can use it to revolutionize the way they use their data. It then discusses the criteria for the soundness of data products and how to best visualize information. As you progress, you'll discover the strategic aspects of data science by learning the five-phase framework that enables you to enhance the value you extract from data. The final chapter of the book discusses the role of a data science manager in helping an organization take the data-driven approach. By the end of this book, you'll have a good understanding of data science and how it can enable you to extract value from your data. What you will learn Get familiar with the five most important steps of data science Use the Conway diagram to visualize the technical skills of the data science team. Understand the limitations of data science from a mathematical and ethical perspective. Get a quick overview of machine learning. Gain insight into the purpose of using data science in your work. Understand the role of data science managers and their expectations.
Radical solutions and learning analytics: Personalised learning and teaching through big data by
Learning Analytics become the key for Personalised Learning and Teaching thanks to the storage, categorisation and smart retrieval of Big Data. Thousands of user data can be tracked online via Learning Management Systems, instant messaging channels, social networks and other ways of communication. Always with the explicit authorisation from the end user, being a student, a teacher, a manager or a persona in a different role, an instructional designer can design a way to produce a practical dashboard that helps him improve that very user's performance, interaction, motivation or just grading. This book provides a thorough approach on how education, as such, from teaching to learning through management, is improved by a smart analysis of available data, making visible and useful behaviours, predictions and patterns that are hinder to the regular eye without the process of massive data.
The real work of data science: Turning data into information, better decisions, and stronger organizations by
The essential guide for data scientists and for leaders who must get more from their data science teams The Economist boldly claims that data are now "the world's most valuable resource." But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. The Real Work of Data Science explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that spells the difference between a good data scientist and a great one, between a team that makes marginal contributions and one that drives the business, between a company that gains some value from its data and one in which data truly is "the most valuable resource." "These two authors are world-class experts on analytics, data management, and data quality; they've forgotten more about these topics than most of us will ever know. Their book is pragmatic, understandable, and focused on what really counts. If you want to do data science in any capacity, you need to read it." --Thomas H. Davenport, Distinguished Professor, Babson College and Fellow, MIT Initiative on the Digital Economy "I like your book. The chapters address problems that have faced statisticians for generations, updated to reflect today's issues, such as computational Big Data." --Sir David Cox, Warden of Nuffield College and Professor of Statistics, Oxford University "Data science is critical for competitiveness, for good government, for correct decisions. But what is data science? Kenett and Redman give, by far, the best introduction to the subject I have seen anywhere. They address the critical questions of formulating the right problem, collecting the right data, doing the right analyses, making the right decisions, and measuring the actual impact of the decisions. This book should become required reading in statistics and computer science departments, business schools, analytics institutes and, most importantly, by all business managers." --A. Blanton Godfrey, Joseph D. Moore Distinguished University Professor, Wilson College of Textiles, North Carolina State University
Sensemaking: What makes human intelligence essential in the age of the algorithm by
We live in the age of algorithms and big data. But what happens when number crunching fails to solve a company's problems? Christian Madsbjerg argues that many of today's biggest success stories stem not from "quant" thinking, but rather from a company's deep, nuanced engagement with the culture, language, and history of its customers. Madsbjerg calls this technique "sensemaking," and through captivating stories, he illustrates how business leaders, entrepreneurs, and individuals can use human science tools to innovate and find solutions to their thorniest problems. In a time when liberal arts graduates fear for their prospects in a data-obsessed economy, SENSEMAKING is a welcome, transformative vision for business and success in the twenty-first century.
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