Leverage big data to add value to your business Social media analytics, web-tracking, and other technologieshelp companies acquire and handle massive amounts of data to betterunderstand their customers, products, competition, and markets.Armed with the insights from big data, companies can improvecustomer experience and products, add value, and increase return oninvestment. The tricky part for busy IT professionals andexecutives is how to get this done, and that's where this practicalbook comes in. Big Data: Understanding How Data Powers BigBusiness is a complete how-to guide to leveraging big data todrive business value. Full of practical techniques, real-world examples, and hands-onexercises, this book explores the technologies involved, as well ashow to find areas of the organization that can take full advantageof big data. Shows how to decompose current business strategies in order tolink big data initiatives to the organization’s valuecreation processes Explores different value creation processes and models Explains issues surrounding operationalizing big data,including organizational structures, education challenges, and newbig data-related roles Provides methodology worksheets and exercises so readers canapply techniques Includes real-world examples from a variety of organizationsleveraging big data Big Data: Understanding How Data Powers Big Business iswritten by one of Big Data's preeminent experts, William Schmarzo.Don't miss his invaluable insights and advice.
Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.
This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges.
How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits
Author: Russell Glass
Publisher: John Wiley & Sons
Category: Business & Economics
Get the expert perspective and practical advice on big data The Big Data-Driven Business: How to Use Big Data to WinCustomers, Beat Competitors, and Boost Profits makes the casethat big data is for real, and more than just big hype. The bookuses real-life examples—from Nate Silver to Copernicus, andApple to Blackberry—to demonstrate how the winners of thefuture will use big data to seek the truth. Written by a marketingjournalist and the CEO of a multi-million-dollar B2B marketingplatform that reaches more than 90% of the U.S. businesspopulation, this book is a comprehensive and accessible guide onhow to win customers, beat competitors, and boost the bottom linewith big data. The marketplace has entered an era where the customer holds allthe cards. With unprecedented choice in both the consumer world andthe B2B world, it's imperative that businesses gain a greaterunderstanding of their customers and prospects. Big data is the keyto this insight, because it provides a comprehensive view of acompany's customers—who they are, and who they may betomorrow. The Big Data-Driven Business is a complete guideto the future of business as seen through the lens of big data,with expert advice on real-world applications. Learn what big data is, and how it will transform theenterprise Explore why major corporations are betting their companies onmarketing technology Read case studies of big data winners and losers Discover how to change privacy and security, and remodelmarketing Better information allows for better decisions, bettertargeting, and better reach. Big data has become an indispensabletool for the most effective marketers in the business, and it'sbecoming less of a competitive advantage and more like an industrystandard. Remaining relevant as the marketplace evolves requires afull understanding and application of big data, and The BigData-Driven Business provides the practical guidance businessesneed.
The internet has become embedded into our daily lives, no longer an esoteric phenomenon, but instead an unremarkable way of carrying out our interactions with one another. Online and offline are interwoven in everyday experience. Using the internet has become accepted as a way of being present in the world, rather than a means of accessing some discrete virtual domain. Ethnographers of these contemporary Internet-infused societies consequently find themselves facing serious methodological dilemmas: where should they go, what should they do there and how can they acquire robust knowledge about what people do in, through and with the internet? This book presents an overview of the challenges faced by ethnographers who wish to understand activities that involve the internet. Suitable for both new and experienced ethnographers, it explores both methodological principles and practical strategies for coming to terms with the definition of field sites, the connections between online and offline and the changing nature of embodied experience. Examples are drawn from a wide range of settings, including ethnographies of scientific institutions, television, social media and locally based gift-giving networks.
Master decision modeling and analytics through realistic examples, intuitive explanations, and tested Excel templates. Business Analytics with Management Science has been designed to help students, practitioners and managers use business analytics to improve decision-making systems. Unlike previous books, it emphasizes the application of practical management science techniques in business analytics. Drawing on 20+ years of teaching and consulting experience, Dr. Arben Asllani introduces decision analytics through realistic examples and intuitive explanations – not complex formulae and theoretical definitions. Throughout, Asllani helps practitioners focus more on the crucial input-output aspects of decision making – and less upon internal model complexities that can usually be "delegated" to software.
"This book provides an in-depth analysis of attrition modeling relevant to business planning and management, offering insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytic tools"--Provided by publisher.
This text provides a practical guide to complying with the Data Protection Act 1998. Taking the form of an audit, it leads readers through a systematic programme that should help them adopt correct procedures on data protection issues.
Automatic learning is a complex, multidisciplinary field of research and development, involving theoretical and applied methods from statistics, computer science, artificial intelligence, biology and psychology. Its applications to engineering problems, such as those encountered in electrical power systems, are therefore challenging, while extremely promising. More and more data have become available, collected from the field by systematic archiving, or generated through computer-based simulation. To handle this explosion of data, automatic learning can be used to provide systematic approaches, without which the increasing data amounts and computer power would be of little use. Automatic Learning Techniques in Power Systems is dedicated to the practical application of automatic learning to power systems. Power systems to which automatic learning can be applied are screened and the complementary aspects of automatic learning, with respect to analytical methods and numerical simulation, are investigated. This book presents a representative subset of automatic learning methods - basic and more sophisticated ones - available from statistics (both classical and modern), and from artificial intelligence (both hard and soft computing). The text also discusses appropriate methodologies for combining these methods to make the best use of available data in the context of real-life problems. Automatic Learning Techniques in Power Systems is a useful reference source for professionals and researchers developing automatic learning systems in the electrical power field.