The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You'll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You'll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies. - Demonstrates how to leverage a technology independent data quality measurement framework for your specific business priorities and data quality challenges - Enables discussions between business and IT with a non-technical vocabulary for data quality measurement - Describes how to measure data quality on an ongoing basis with generic measurement types that can be applied to any situation

Residents in Boston, Massachusetts are automatically reporting potholes and road hazards via their smartphones. Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior. How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don't have to be Nate Silver to reap massive benefits from today's new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions. It's time to start thinking big. In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate. Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free advice for people and organizations looking to understand and leverage Big Data. Rife with case studies, examples, analysis, and quotes from real-world Big Data practitioners, the book is required reading for chief executives, company owners, industry leaders, and business professionals.

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“A Way to Garden prods us toward that ineffable place where we feel we belong; it’s a guide to living both in and out of the garden.” —The New York Times Book Review For Margaret Roach, gardening is more than a hobby, it’s a calling. Her unique approach, which she calls “horticultural how-to and woo-woo,” is a blend of vital information you need to memorize and intuitive steps you must simply feel and surrender to. In A Way to Garden, Roach imparts decades of garden wisdom on seasonal gardening, ornamental plants, vegetable gardening, design, gardening for wildlife, organic practices, and much more. She also challenges gardeners to think beyond their garden borders and to consider the ways gardening can enrich the world. Brimming with beautiful photographs of Roach’s own garden, A Way to Garden is practical, inspiring, and a must-have for every passionate gardener.

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.

Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

The president and CEO of Scandinavia Airlines (SAS) shows how to adapt to the new customer–driven economy.

This isn't just another book about anatomy or physiology – it's a straightforward, practical guide that answers all the common concerns and questions of every student nurse. How to Make It as a Student Nurse has evolved from the online advice provided to student nurses in the UK by well-known advocate and nurse Claire Carmichael. She has teamed up with experienced nursing lecturer Ann Marie Dodson to provide a complete guide to being a student nurse, from the application stage through to writing assignments, passing exams, undertaking clinical placements and working in a team. This wonderful new guide is packed full of invaluable advice, including how to handle your finances and juggle your caring responsibilities. The content is supported by real life case studies and vlogs to summarise key points. - Engaging and easy to read – ideal for busy students - Easy to navigate – takes you through each stage of the student nurse journey - Covers the whole nursing degree experience - Video vlogs to summarise key points - Real life perspectives of nursing students - Top tips on everything you will come across throughout your nursing education

This book explores the science behind intuitive decision-making in business, and shows how people's innate capacity for intuition can be nurtured and strengthened to maximize performance. We are all familiar with those perplexing situations when we think we 'just know' without knowing how or why we know. In professional life it might be the job candidate's CV that checks all the boxes but somehow doesn't stack-up: should we perform some due diligence and dig a little deeper? In personal life it could be the apartment that we're looking to rent that just felt right the minute we walked through the front door: should we trust our hunch and grab it while we can? What if time is of the essence? What if there isn't any more data to be had in the time available? In this volume, Eugene Sadler-Smith examines why situations like these often leave us in a quandary, and why these decisions so often leave us in two minds. He reveals that metaphorically speaking, we have two minds in one brain: an 'analytical mind' and an 'intuitive mind', which sometimes come to quite different conclusions about what we ought to do in those consequential decisions that permeate our professional and personal lives. Rather than thinking of our intuitive and analytical minds in constant battle with each other, we might instead think of them as two information-processing systems that have evolved to complement each other. The main idea of this book is that our analytical mind evolved to 'solve' whilst our intuitive mind evolved to 'sense'. Neither is infallible, and our intuitions can be both flawed and marvellous at the same time. The author's clear and detailed explanation of the science behind intuition reveals how we can make intelligent use of our intuition to sense and solve our way through a world that is fast-moving, complex, and uncertain.