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: Ralph Hughes Название: Agile Data Warehousing for the Enterprise ISBN: 0123964644 ISBN-13(EAN): 9780123964649 Издательство: Elsevier Science Рейтинг: Цена: 6230.00 р. Наличие на складе: Поставка под заказ.
Описание:
Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines:
Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked.
Data engineering receives two new "hyper modeling" techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs.
Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines.
Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way.
Learn how to quickly define scope and architecture before programming starts
Includes techniques of process and data engineering that enable iterative and incremental delivery
Demonstrates how to plan and execute quality assurance plans and includes a guide to continuous integration and automated regression testing
Presents program management strategies for coordinating multiple agile data mart projects so that over time an enterprise data warehouse emerges
Use the provided 120-day road map to establish a robust, agile data warehousing program
Автор: Ralph Hughes Название: Agile Data Warehousing Project Management, ISBN: 0123964636 ISBN-13(EAN): 9780123964632 Издательство: Elsevier Science Рейтинг: Цена: 6230.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Offers an introduction to the method as you would practice it in the project room to build a data mart. This title helps to prepare you to join or lead a team in visualizing, building, and validating a single component to an enterprise data warehouse. It includes strategies for getting actionable requirements from a team`s business partner.
Автор: Brackett Michael Название: Data Resource Design ISBN: 1935504339 ISBN-13(EAN): 9781935504337 Издательство: Gazelle Book Services Рейтинг: Цена: 10937.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Are you struggling with the formal design of your organisations data resource? Do you find yourself forced into generic data architectures and universal data models? Do you find yourself warping the business to fit a purchased application? Do you find yourself pushed into developing physical databases without formal logical design? Do you find disparate data throughout the organisation? If the answer to any of these questions is Yes, then you need to read Data Resource Design to help guide you through a formal design process that produces a high quality data resource within a single common data architecture. Most public and private sector organisations do not consistently follow a formal data resource design process that begins with the organisations perception of the business world, proceeds through logical data design, through physical data design, and into implementation. Most organisations charge ahead with physical database implementation, physical package implementation, and other brute-force-physical approaches. The result is a data resource that becomes disparate and does not fully support the organisation in its business endeavours. This book describes how to formally design an organisations data resource to meet its current and future business information demand. It builds on "Data Resource Simplexity", which described how to stop the burgeoning data disparity, and on "Data Resource Integration", which described how to understand and resolve an organisations disparate data resource. It describes the concepts, principles, and techniques for building a high quality data resource based on an organisations perception of the business world in which they operate. Like "Data Resource Simplexity" and "Data Resource Integration", Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data in both public and private sector organisations. He leverages theories, concepts, principles, and techniques from a wide variety of disciplines, such as human dynamics, mathematics, physics, chemistry, philosophy, and biology, and applies them to properly designing data as a critical resource of an organisation. He shows how to understand the business environment where an organisation operates and design a data resource that supports the organisation in that business environment.
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.
Автор: Santos Maribel Yasmina, Costa Carlos Название: Big Data: Concepts, Warehousing, and Analytics ISBN: 8770221847 ISBN-13(EAN): 9788770221849 Издательство: Taylor&Francis Рейтинг: Цена: 14851.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Big Data is a concept of major relevance in today’s world, sometimes highlighted as a key asset for productivity growth, innovation, and customer relationship, whose popularity has increased considerably during the last years. Areas like smart cities, manufacturing, retail, finance, software development, environment, digital media, among others, can benefit from the collection, storage, processing, and analysis of Big Data, leveraging unprecedented data-driven workflows and considerably improved decision-making processes.
The concept of a Big Data Warehouse (BDW) is emerging as either an augmentation or a replacement of the traditional Data Warehouse (DW), a concept that has a long history as one of the most valuable enterprise data assets. Nevertheless, research in Big Data Warehousing is still in its infancy, lacking an integrated and validated approach for designing and implementing both the logical layer (data models, data flows, and interoperability between components) and the physical layer (technological infrastructure) of these complex systems.
This book addresses models and methods for designing and implementing Big Data Systems to support mixed and complex decision processes, giving special attention to BDWs as a way of efficiently storing and processing batch or streaming data for structured or semi-structured analytical problems.
Описание: The final edition of the incomparable data warehousing and business intelligence reference, updated and expanded
The Kimball Group Reader, Remastered Collection is the essential reference for data warehouse and business intelligence design, packed with best practices, design tips, and valuable insight from industry pioneer Ralph Kimball and the Kimball Group. This Remastered Collection represents decades of expert advice and mentoring in data warehousing and business intelligence, and is the final work to be published by the Kimball Group. Organized for quick navigation and easy reference, this book contains nearly 20 years of experience on more than 300 topics, all fully up-to-date and expanded with 65 new articles. The discussion covers the complete data warehouse/business intelligence lifecycle, including project planning, requirements gathering, system architecture, dimensional modeling, ETL, and business intelligence analytics, with each group of articles prefaced by original commentaries explaining their role in the overall Kimball Group methodology.
Data warehousing/business intelligence industry's current multi-billion dollar value is due in no small part to the contributions of Ralph Kimball and the Kimball Group. Their publications are the standards on which the industry is built, and nearly all data warehouse hardware and software vendors have adopted their methods in one form or another. This book is a compendium of Kimball Group expertise, and an essential reference for anyone in the field.
Learn data warehousing and business intelligence from the field's pioneers
Get up to date on best practices and essential design tips
Gain valuable knowledge on every stage of the project lifecycle
Dig into the Kimball Group methodology with hands-on guidance
Ralph Kimball and the Kimball Group have continued to refine their methods and techniques based on thousands of hours of consulting and training. This Remastered Collection of The Kimball Group Reader represents their final body of knowledge, and is nothing less than a vital reference for anyone involved in the field.
Описание: Data science has a huge impact on how companies conduct business, and those who don`t learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers.
Описание: Deliver enterprise data analytics success by following Prashanths prescriptive and practical techniques. Today, organizations across the globe are looking at ways to glean insights from data analytics and make good business decisions. However, not many business enterprises are successful in data analytics. According to Gartner, 80% of analytics programs do not deliver business outcomes. Mckinsey consulting says, less than 20% of the companies have achieved analytics at scale. So, how can a business enterprise avoid analytics failure and deliver business results? This book provides ten key analytics best practices that will improve the odds of delivering enterprise data analytics solutions successfully. It is intended for anyone who has a stake and interest in deriving insights from data analytics. The three key differentiating aspects of this book are: Practicality. This book offers prescriptive, superior, and practical guidance. Completeness. This book looks at data analytics holistically across the four key data analytics domains - data management, data engineering, data science, and data visualization. Neutrality. This book is technologically agnostic and looks at analytics concepts without any reference to commercial analytics products and technologies.
Автор: Kent William Название: Data & Reality ISBN: 1935504215 ISBN-13(EAN): 9781935504214 Издательство: Gazelle Book Services Рейтинг: Цена: 10723.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Let's step back to the year 1978. Sony introduces hip portable music with the Walkman, Illinois Bell Company releases the first mobile phone, Space Invaders kicks off the video game craze, and William Kent writes Data and Reality. We have made amazing progress in the last four decades in terms of portable music, mobile communication, and entertainment, making devices such as the original Sony Walkman and suitcase-sized mobile phones museum pieces today. Yet remarkably, the book Data and Reality is just as relevant to the field of data management today as it was in 1978.
Data and Reality gracefully weaves the disciplines of psychology and philosophy with data management to create timeless takeaways on how we perceive and manage information. Although databases and related technology have come a long way since 1978, the process of eliciting business requirements and how we think about information remains constant. This book will provide valuable insights whether you are a 1970s data-processing expert or a modern-day business analyst, data modeler, database administrator, or data architect.
This third edition of Data and Reality differs substantially from the first and second editions. Data modeling thought leader Steve Hoberman has updated many of the original examples and references and added his commentary throughout the book, including key points at the end of each chapter.
The important takeaways in this book are rich with insight yet presented in a conversational and easy-to-grasp writing style. Here are just a few of the issues this book tackles:
Has "business intelligence" replaced "artificial intelligence"?
Why is a map's geographic landscape analogous to a data model's information landscape?
Where do forward and reverse engineering fit in our thought process?
Why are we all becoming "data archeologists"?
What causes the communication chasm between the business professional and the information technology professional in most organizations, and how can the logical data model help bridge this chasm?
Why do we invest in hardware and software to solve business problems before determining what the business problems are in the first place?
What is the difference between oneness, sameness, and categories?
Why does context play a role in every design decision?
Why do the more important attributes become entities or relationships?
Why do symbols speak louder than words?
What's the difference between a data modeler, a philosopher, and an artist?
Why is the 1975 dream of mapping all attributes still a dream today?
What influence does language have on our perception of reality?
Can we distinguish between naming and describing?
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