Computing Predictive Analytics, Business Intelligence, and Economics, 

• 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).

Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people's imaginations as to what a fully connected world can offer.

Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions.

The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies.

1. What does quantitative analysis of a system really mean?
2. What is a system?
3. What are big data and analystics?
4. How do you know your numbers are good?
5. What will the future data science environment look like?
6. How do you determine data provenance?
7. How do you gather and process information, and then organize, store, and synthesize it?
8. How does an organization implement data analytics?
9. Do you really need to think like a Chief Information Officer?
10. What is the best way to protect data?
11. What makes a good dashboard?
12. 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.