Описание: This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling.
Learn how to make business intelligence (BI) successful in your organization.
How do we enable our organizations to enjoy the often significant benefits of BI and analytics, while at the same time minimizing the cost and risk of failure? In this book, I am not going to try to be prescriptive; I won't tell you exactly how to build your BI environment. Instead, I am going to focus on a few core principles that will enable you to navigate the rocky shoals of BI architecture and arrive at a destination best suited for your particular organization. Some of these core principles include:
Have an overarching strategy, plan, and roadmap
Recognize and leverage your existing technology investments
Support both data discovery and data reuse
Keep data in motion, not at rest
Separate information delivery from data storage
Emphasize data transparency over data quality
Take an agile approach to BI development.
This book will show you how to successfully navigate both the jungle of BI technology and the minefield of human nature. It will show you how to create a BI architecture and strategy that addresses the needs of all organizational stakeholders. It will show you how to maximize the value of your BI investments. It will show you how to manage the risk of disruptive technology. And it will show you how to use agile methodologies to deliver on the promise of BI and analytics quickly, succinctly, and iteratively.
This book is about many things. But principally, it's about success. The goal of any enterprise initiative is to succeed and to derive benefit--benefit that all stakeholders can share in. I want you to be successful. I want your organization to be successful. This book will show you how.
This book is for anyone who is currently or will someday be working on a BI, analytics, or Big Data project, and for organizations that want to get the maximum amount of value from both their data and their BI technology investment. This includes all stakeholders in the BI effort--not just the data people or the IT people, but also the business stakeholders who have the responsibility for the definition and use of data. There are six sections to this book:
In Section I, What Kind of Garden Do You Want?, we will examine the benefits and risks of Business Intelligence, making the central point that BI is a business (not IT) process designed to manage data assets in pursuit of enterprise goals. We will show how data, when properly managed and used, can be a key enabler of several types of core business processes. The purpose of this section is to help you define the particular benefit(s) you want from BI.
In Section II, Building the Bones, we will talk about how to design and build out the "hardscape" (infrastructure) of your BI environment. This stage of the process involves leveraging existing technology investments and iteratively moving toward your desired target state BI architecture.
In Section III, From the Ground Up, we explore the more detailed aspects of implementing your BI operational environment.
In Section IV, Weeds, Pests and Critters, we talk about the myriad of things that can go wrong on a BI project, and discuss ways of mitigating these risks.
In Section V, The Sustainable Garden, we talk about how to create a BI infrastructure that is easy and inexpensive to maintain.
Finally, Section VI presents a case study illustrating the principles of this book, as applied to a fictional manufacturing company (the Blue Moon Guitar Company).
Автор: Thompson, John Rogers, Shawn P. Название: Analytics ISBN: 1634622375 ISBN-13(EAN): 9781634622370 Издательство: Gazelle Book Services Рейтинг: Цена: 6647.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Learn how big data and other sources of information can be transformed into valuable knowledge -- knowledge that can create incredible competitive advantage to propel a business toward market leadership. Learn through examples and experience exactly how to pick projects and build analytics teams that deliver results. Know the ethical and privacy issues, and apply the three-part litmus test of context, permission, and accuracy. Without a doubt, data and analytics are the new source of competitive advantage, but how do executives go from hype to action? Thats the objective of this book -- to assist executives in making the right investments in the right place and at the right time in order to reap the full benefits of data analytics.
Автор: Kalita Название: Recent Developments in Machine Learning and Data Analytics ISBN: 9811312796 ISBN-13(EAN): 9789811312793 Издательство: Springer Рейтинг: Цена: 18294.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents high-quality papers from an international forum for research on computational approaches to learning. Further, it features work that shows how to apply learning methods to solve important application problems as well as how machine learning research is conducted.
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
Описание: This book presents a framework for developing an analytics strategy that includes a range of activities, from problem definition and data collection to data warehousing, analysis, and decision making.
Описание: Provides a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. The book provides a comprehensive primer and overview of the field (and related fields), and discusses the field as it applies to financial institutions.
Описание: Provides a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. The book provides a comprehensive primer and overview of the field (and related fields), and discusses the field as it applies to financial institutions.
Описание: This book presents a framework for developing an analytics strategy that includes a range of activities, from problem definition and data collection to data warehousing, analysis, and decision making. The authors examine best practices in team analytics strategies such as player evaluation, game strategy, and training and performance. They also explore the way in which organizations can use analytics to drive additional revenue and operate more efficiently. The authors provide keys to building and organizing a decision intelligence analytics that delivers insights into all parts of an organization. The book examines the criteria and tools for evaluating and selecting decision intelligence analytics technologies and the applicability of strategies for fostering a culture that prioritizes data-driven decision making. Each chapter is carefully segmented to enable the reader to gain knowledge in business intelligence, decision making and artificial intelligence in a strategic management context.
Описание: Predictive analytics is an evolving field and has applications across all domains and sectors. This book will introduce to the reader the concept of predictive analytics and cover in detail the predictive analytic models, tools and techniques involved. The book will also cover the applications of predictive analytics in various domains including health care, banking, agriculture, retailing, sports and industries using smart grid and industrial drivers with real world scenarios. This book covers performance improvement and enhancement techniques with the aid of intelligent predictive analytical algorithms to predict future patterns. This would be a handy guide covering all steps from identification of the problem, preparing the data, model building and recommending solutions. Hence, the readers can experience the various types of performance improvement techniques and implement them in their specific domain.
Описание: This book describes how companies can easily and pragmatically set up and realize the path to a data-driven enterprise, especially in the marketing practice, without external support and additional investments. The PI maturity model then describes the phases in which you can build a PI ecosystem in your company.
Автор: Pablo Duboue Название: The Art of Feature Engineering: Essentials for Machine Learning ISBN: 1108709389 ISBN-13(EAN): 9781108709385 Издательство: Cambridge Academ Рейтинг: Цена: 6970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.
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