The Analytics Lifecycle Toolkit

A Practical Guide for an Effective Analytics Capability

Gregory S. Nelson

• Publisher: Wiley
• Year: 2018
• Print ISBN:
  9781119425069
• eBook ISBN:
  9781119425106
• Edition: 1
• Topic: Commerce
• Subtopic: Management

An evidence-based organizational framework for exceptional analytics team results

The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground.

Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to:

• Master fundamental concepts and practices of the analytics life cycle
• Understand the knowledge domains and best practices for each stage
• Delve into the details of analytical team processes and process optimization
• Utilize a robust toolkit designed to support analytic team effectiveness

The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results.

PART I
The Foundation of Analytics

CHAPTER 1
Analytics Overview

…what enables the wise commander to strike and conquer, and achieve things beyond the reach of ordinary men, is foreknowledge. Now, this foreknowledge cannot be elicited from spirits…

The Art of War, Sun Tzu (as seen in Giles, 1994)

FUNDAMENTAL CONCEPTS

Peter Drucker first spoke of the “knowledge economy” in his book The Age of Discontinuity (Drucker, 1969). The knowledge economy refers to the use of knowledge “to generate tangible and intangible value.” Nearly 50 years later, organizations have virtually transformed themselves to meet this challenge, and data and analytics have become central to that transformation.

In this chapter, we highlight the “fundamentals” of analytics by hopefully creating a level playing field for those interested in the moving from the concept of analytics to the practice of analytics. The fundamentals include defining both data and analytics using terms that I hope resonate. In addition, I think it is important to consider analytics in the wider context of how it is used and the value derived from these efforts. Finally, in this chapter, I relate analytics to other widely used terms as a way to find both common ground and differentiation with often-confused terminologies.

Data

Data permeates just about every part of our lives, from the digital footprint we leave with our cell phones, to health records, purchase history, and utilization of resources such as energy. While not impossible, it would require dedication and uncanny persistence to live “off-the-grid” in this digital world. Beyond the pure generation of data, we are also voracious consumers of data, reviewing our online spending habits, monitoring our fitness regimes, and reviewing those frequent flyer points for that Caribbean vacation.

But what is data? At its most general form, data is simply information that has been stored for later use. Earliest forms of recording information might have been notches on bones (Sack, 2012). Fast forward to the 1950s, and people recorded digital information on Mylar strips (magnetic tape), then punch cards, and later disks. Modern data processing is relatively young but has set the foundation for how we think about the collection, storage, management, and use of information.

Until recently, we cataloged information that wasn't necessarily computable (e.g., videos, images); but through massive technological change, the class of “unstorable” data is quickly vanishing. Stored information, or data, is simply a model of the real world encoded in a manner that is usable, or for our purposes “computable” (Wolfram, 2010).

The fact that data is a persistent record or “model” of something that happened in the real world is an important distinction in analytics. George Box, a statistician considered by many as “one of the greatest statistical minds of the 20^th century” (Champkin, 2013) was often quoted as saying: “All models are wrong, but some are useful.” All too often, we find something in the data that doesn't make sense or is just plain wrong. Remember that data has been translated from the real, physical world into something that represents the real world—George's “model.” Just as the mechanical speedometer is a standard for measuring speed (and a pretty good proxy for measuring velocity), the model is really measuring tire rotation, not speed. (For those interested in a late-night distraction, I refer you to Woodford's 2016 article “Speedometers” that explains how speedometers work.) In sum, data is stored information and serves as the foundation for all of analytics. In visual analytics, for example, we make sense out of the data using visualization techniques that enable us to perform analytical reasoning through interactive, visual interfaces.

Analytics

Analytics may be one of the most overused yet least understood terms used in business. For some, it relates to the technologies used to “beat data into submission,” or it is simply an extension of business intelligence and data warehousing. And yet for others, it relates to the statistical, mathematical, or quantitative methods used in the development of models.

According to Merriam-Webster (Merriam-Webster, 2017), analytics is “the method of logical analysis.” Dictionary.com (dictionary.com, 2017) defines analytics as “the science of logical analysis.” Unfortunately, both definitions use the root word of analysis in the definition, which seems a bit like cheating.

The origin of the word analysis goes all the way back to the 1580s, where the term is rooted in Medieval Latin (anal

ticus) and Greek (anal

tikós), and means to break up or to loosen. Throughout this book, I frame analytics as a structured approach to data-driven problem solving—one that helps us break up problems through careful consideration of the facts.

What Is Analytics?

There has been much debate over the definition of analytics (Rose, 2016). While the purpose of this book is not to redefine or challenge anyone's definition, for the current discussion I define analytics as:

a comprehensive, data-driven strategy for problem solving

I intentionally resist using a definition that views analytics as a “process,” a “science,” or a “discipline.” Instead, I cast analytics as a comprehensive strategy, and as you will see in Part II of this book, it encompasses best practice areas that contain processes, along with roles and deliverables.

Analytics uses logic, inductive and deductive reasoning, critical thinking, and quantitative methods—along with data—to examine phenomena and determine its essential features. Analytics is rooted in the scientific method (Shuttleworth, 2009), including problem identification and understanding, theory generation, hypothesis testing, and the communication of results.

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Inductive Reasoning

Inductive reasoning refers to the idea that accumulated evidence is used to support a conclusion but with some level of uncertainty. ...

Table of contents

• Cover
• Title Page
• Table of Contents
• Preface
• Acknowledgments
• PART I: The Foundation of Analytics
• PART II: Analytics Lifecycle Best Practices
• PART III: Sustaining Analytics Success
• About the Author
• About the Companion Web Site
• Index
• End User License Agreement