The modern enterprise is often characterized as “data rich, but information poor.” This challenge is exacerbated by the pure volume and variety of data generated at the point of interaction (e.g., customers, patients, suppliers) and careening outward. Whether you are preparing, analyzing, presenting, or consuming data, having a strong foundation in data and analytics is paramount for conveying ideas effectively.

In this book, I translate the world of big data, data science, and analytics into a practical, comprehensive guide where you can explore the art and science of analytics best practices through a proven framework for managing analytics teams and processes.

The focus of the book is on creating effective and efficient analytics organizations and processes in order to strengthen the role of data and analytics in producing organizational success.

When I started thinking about writing about this specific topic, it was primarily in response to the lack of information about “the people and process” side of analytics. That is, for over a decade, authors have written about the concept of analytics, its importance in business, and specific implementations of technologies such as Python, R, or SAS, among others. However, those resources generally do not address the tactics of analytics model development or business case development, nor do they address the impact of analytics on operational processes.

The issues that organizations have grappled with over the past 10 years since Tom Davenport and Jeanne Harris published their seminal work Competing on Analytics (Davenport & Harris, 2007) have shifted from “What problems can we solve with analytics?” to “How do we find, nurture, and retain analytics professionals?” This shift from the “what” to the “how” supports the basic premise of this book. I also think the timing is right for the book, as entire industries are transforming themselves with the use of data and analytics. While many organizations have solved the barriers of effectively using analytics in everyday operations as well as strategic decision making, other industries are just now getting on the “analytics bandwagon,” and they see the promise of analytics without a clear roadmap for getting there. For the former, the challenge is one of effectiveness and improved efficiencies. For the latter, the real struggle can often be with creating an organizational culture—or mindset—for analytics, justifying the development of an analytics capability, and organizing for success.

My personal inspiration for this book came from the works of Ralph Kimball. I remember reading his first edition of the Data Warehouse Toolkit (Kimball, 1996) and thinking to myself, “This makes sense.” It was so very different from the conceptual treatments often found in business and technology books, in that Kimball gave us the language, tools, and processes to actually do data warehousing. He provided a solid overview of the areas relevant to someone who was either familiar with or completely new to data warehousing, along with a framework for the data warehousing lifecycle and key process areas. I hope that you will find that The Analytics Lifecycle Toolkit lives up to this inspiration and that it provides a comprehensive and practical guide to the Analytics Lifecycle with focus on creating an effective analytics capability for your organization.

This book differs from other “how-to” books in that it is not designed as a cookbook of analytics models, but rather, is a primer on the best practices and processes used in analytics. It is intended for:

• Organizational leaders and analytics executives who need to understand what it means to build and maintain an analytics capability and culture, including those in newly minted chief analytics officer or chief data officer positions.
• Analytics teams on the front lines of designing, developing, and delivering analytics as a service or as a product. This group includes analytics product managers, team leads, analysts, project managers, statisticians, scientists, engineers, data scientists, and the “quants” who build analytics models.
• Aspiring data champions, those who use data or consume analytics products in their role as fact-based, problem solvers. The data champion is anyone who wishes to use data to improve performance, support a decision, or change the trajectory of some business process.

This book is organized in three sections:

1. The Foundation of Analytics: Starts by outlining what analytics is and how it can be applied to a number of problems in the organization. The focus shifts to analytics as an organizational capability, outlining a different perspective on how analytics can serve the organization's purpose, and how analytics (and data) strategy informs what we do and how we deliver those capabilities. Then this section will address how to deliver analytics capabilities through resources—that is, people, processes, technology, and data.
2. Analytics Lifecycle Best Practices: Introduces analytics products and how to support the design, development, and delivery of analytics products and/or services. The lifecycle is then broken down into five best practice areas with specific processes that support analytics product development.
3. Sustaining Analytics Success: Rounds out the discussion of how to ensure that analytics products have the greatest impact on the organization and sustain improvements. The discussion includes how to measure effectiveness and efficiency for analytics programs and apply lessons learned from other disciplines such as behavioral economics, social psychology, and change management.

In the first chapter, you will see that the language of analytics can be confusing and even down right daunting. Terms like the science of, the discipline of, and the best practice of generally refer to the usual manner in which analytics are conceptualized.

However, terms like method, methodology, or approach typically mean the processes used in common practice.

One of my goals in writing The Analytics Lifecycle Toolkit is to assume nothing and to clarify things along the way. To that end, I will do my best to make analytics accessible by providing explicit examples and using precise language wherever possible.

You've made it this far, so perhaps you agree that this topic is interesting and worth the price of admission. But if you need 10 more reasons, here they are:

This book:

1. Offers a practical guide to understanding the complete analytics lifecycle and how to translate that into organizational design and efficient processes.
2. Provides a framework for building an analytics team in the organization, including functions and team design.
3. Explores the people and process side of analytics with a focus on analytics team effectiveness and design thinking around the creation of analytics products.
4. Discusses the analytics job families and roles needed for a successful analytics program.
5. Includes case studies from real-world experiences.
6. Bridges concepts appropriate to an analytics culture such as data-centrism and numeracy with data and technology strategies.
7. Creates understanding and awareness for analytics leaders and a toolbox for practitioners.
8. Provides access to a library of tools and templates that include areas of best practice that support leadership, process improvement, and workforce enablement.
9. Begins with fundamentals of the analytics lifecycle, discusses the knowledge domains and best practice areas, and then details the analytics team processes.
10. Was written by someone who does analytics for a living and has seen hundreds of unique customer perspectives and applications across multiple industries.

Hopefully, this book will provide some useful guidance for those just starting their analytics journey and some tips for those more experienced. Happy trails!