Analytics the Right Way: A Business Leader's Guide to Putting Data to Productive Use

by Tim Wilson, Troy Allan (Narrator), Dr. Joe Sutherland

Organizations have more data at their fingertips than ever, and their ability to put that data to productive use should be a key source of sustainable competitive advantage. Yet, business leaders looking to tap into a steady and manageable stream of "actionable insights" often, instead, get blasted with a deluge of dashboards, chart-filled slide decks, and opaque machine learning jargon that leaves them asking, "So what?"

Analytics the Right Way is a guide for these leaders. It provides a clear and practical approach to putting analytics to productive use with a three-part framework that brings together the realities of the modern business environment with the deep truths underpinning statistics, computer science, machine learning, and artificial intelligence. The a pragmatic and actionable guide for delivering clarity, order, and business impact to an organization's use of data and analytics.

The book uses real-world examples from the authors' direct experiences—working inside organizations, as external consultants, and as educators—to empower the listener to put foundational analytical and statistical concepts to effective use in a business context.

PLEASE When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

Genres: Nonfiction

Audible Audio

Published April 8, 2025

About the author

Librarian Note: There is more than one author by this name in the Goodreads database.

Reviews

[Mark · Rating 4 out of 5]

I think this would be a useful refresher or introduction if you’ve been away or are new to incorporating better analytics into proactive, strategic decision making. I found it helpful in remembering things I had forgotten or let slide.

[Robert Petkovic · Rating 5 out of 5]

Finally we have a book on digital analysis and not just on setup!
I don’t usually take notes but for me almost every paragraph in the book contains a sentence or two that I would print out in capital letters on the walls of every data analyst office in the effin world! I think I took almost 100 notes and highlights on my Kindle while reading this book :)
For us in the digital analytics world this is the book we’ve been waiting for since Avinash wrote his “Analytics 2.0” in 2009, because most books so far have been dealing with analytical setup, differences among various tools, upgrading to new tools, various reporting tools, GDPR and stuff.
This book is tool agnostic and has zero or a few sentences on analytical setup and a few important pages on reporting (dashboards). It shows the importance of data gathering but covers the more important “OK, now what?” part of having the data. You will learn what KPIs are, how to get them, how to present them and how to use them for predicting the project’s success. You will learn the importance of hypotheses and not just justifications in the digital marketing, how to validate hypothesis, how to make decisions upon them and how never to be sure that you made the best decision based on the best data, but to always doubt and retest. You will learn WHY do we need the data and why do we need to understand the data and its structure, results and implications in order to become or be good data analysts and not (just) data engineers or data scientists (not that those positions are less important!)
After each chapter you will have the opportunity to give a two-question feedback on how that chapter changed the way you look at the data. It's an online questionnaire. Why? Because analyst love collecting data anyways :)))
Even if you are not an analyst, but a team leader or a business leader who need to make choices and decisions based on data, you will find this book more than useful. This book might help you look at the data the whole new ways.
Tim Wilson is a quintessential analyst with a broad experience in helping clients make data driven decisions - the right person to write such book. That’s why there are so many real life examples and witty remarks, not just plain theory and technical stuff.
If you are or want to be an analyst, you’ll love this book! Tell your friends, family and loved ones that this is a perfect birthday gift or give it to yourself, because you deserve it :) Why? As noted on the last page - because Analytics is not an engineering task; it’s a thinking task.
Ah yes, I almost forgot. Happy analyzing!

[Dimitri · Rating 5 out of 5]

📕 Why (Not) to read this book (Target Audience)

Must read, when you work with data for performance or hypothesis

👀 How this book changed my daily live (Takeaways)

Data is like oil, without refinements it is useless

⁉ Spoiler Alerts (Highlights)

Data

Data is like oil, without refinements it is useless, concepts:
1. Gathering generic data is easy, getting insight is complex, due to its infinite possibilities
2. Just because data is available in our backyard, it does not mean it is the right data for the job (drunkard principle)
3. Torturing data will confirm to anything
4. Effectively using data requires strategic and creative thinking, less technical experience

Analytics can only reduce uncertainty not eliminate it

3 Usages of data:
• Operational enablement use (erp/crm)
• Trigger based use (automated actions)
• Model based use (prediction, hypothesis)

Performance is backward looking

The performance management time machine: where we are today, where we expected to be today, not how or why we got here.

KPI are the cornerstone of performance management, the difficulty lies in selecting the metrics that matter.

Effective performance measurement: 2 Magic questions
• What are we trying to achieve (critical success factors)
• How will we know if we have done that (Metrics & Target)

1. Must be asked before the start of the project / initiative
2. Must be asked in order
3. Answers must be written down


Metrics should be outcome focused as output do not always lead to outcome.
• Output: calorie intake per day
• Outcome: body weight

Target
1. Back of a napkin: Let the stakeholders individually think on targets
2. Bracketing to remove infinity
a. Start with: extremely low & high target
b. Reduce to: Base target & Stretch target

Hypothesis validation: forward looking

Hypothesis is a decision support tool based on human ideas

Hypothesis Library: identifying, tracking, ranking
1. We believe that .... because ... dependent variable ... if we are wright we will ...
2. Originator
3. Date

Ranking for validation
1. Level of effort
2. Strength of theory
3. Alignment with business

Methodologies: The ladder off evidence:
1. Anecdotal: biased user survey
2. Descriptive: Methodical and qualifiedly, large number off
a. Correlation is not causation
b. Unit of analysis
c. independent (impacted dependent) & dependent (outcome, only one, evidence in hypothesis) variables
3. Scientific;

[Joe · Rating 5 out of 5]

A practical, insightful, and surprisingly entertaining guide to making data actually work for your business — grounded in sound mathematics, yet free of jargon and complexity that get in the way of understanding the concepts. One of the things I appreciate most about the book is how complete it is. It doesn’t just scratch the surface of analytics. The authors lay out a structured framework for turning data into decisions, covering performance measurement, hypothesis validation, and operational enablement in depth. And yet, it never feels overwhelming. Wilson and Sutherland balance technical depth with clear explanations, making even topics like confounding bias, counterfactuals, and causal inference approachable.

One of my favorite parts? The humor. Let’s be honest—analytics books aren’t usually entertaining, but this one is. The wit and personality woven throughout make it a genuinely enjoyable read, proving that analytics doesn’t have to be dry or boring. Illustrator Paul Lyren did a fantastic job with the cartoon graphics, which are both funny and informative. His illustrations bring the text to life and make complex ideas more accessible.

If you’re looking for a clear, practical guide to making data work for your business, this is the book to read. Pick up a copy of Analytics the Right Way — you’ll not only learn a lot, but you might even have some fun along the way.

Full review here -> https://blog.marketingdatascience.ai/...

[Lee · Rating 3 out of 5]

Some useful frameworks introduced in chapters 3-5 that I found useful and intend to use at work as an analytical leader. However, the book really suffers in the second half with being too high-level. The content and advice is not technical enough for analytics and data leaders, and too vague to be very useful for business leaders. The foundations for a good book are here, I just wish the authors weren’t afraid to go into more technical detail.

[David · Rating 5 out of 5]

Usually any book title mentioning “analytics” is all I need. Knowing who the authors were was more than enough this time. I got a bit closer in my understanding of statistics and data science along with better seeing some data biases. Definitely worth the read and worth revisiting.