Book review — Data-driven Decision-making for Product Managers, Gabriel Steinhardt

Data-driven Decision-making for Product Managers — A Primer to Data Literacy in Product Management Author — Gabriel Steinhardt

Publisher — Springer

Page count — XV, 101 numbered pages + 5 blank pages in a print copy First published — November 2024

Disclaimer: I bought a copy of this publication myself. Views presented in this review are subjective. I review the book “as is” — I focus on what I see, read, touch, etc. Photos published here are under the “for review” purposes.

Preview is available on Springer and Amazon websites.

Some backstory — I’d purchased this book for two reasons, first being that the author’s previous two books (The Product Manager’s Toolkit? and Market-Value Pricing) were practical & useful for (not only) people working in product management and the second reason is that data-literary is a critically important skill to poses.

As I sometimes say — it’s data-driven or data-drivel. Your pick. I prefer the first option.

Overview

This is a short and practical primer on data-literary and concepts related to data-driven decision-making that can be read in about 1–2 days.

I have read the print copy in full. I think that one should read this publication in full, then consult it when necessary.

The publication is divided into a preface, acknowledgements, five parts, afterword, and a DDDM glossary of terms:

Preface

Acknowledgements

Part 1 — The Data Age

Part 2 — Data-driven Decision-making (DDDM)

Part 3 — Data — Information — Knowledge

Part 4 — The DDDM Process

Part 5 — Peers and Environment

Afterword

DDDM glossary

There are plenty of retention drills in every part of this book. A retention drill here means a set of close ended ABCDE questions, that one can attempt to answer to check their retention of knowledge gained by reading.

There are also ample example throughout this publication that serve to underscore a point made by the author, those examples are brief, yet helpful in anchoring a concept in reality.

Preface & acknowledgements are short, just 2 pages.

Part 1 sets the context for this book. The author describes there the timeframe in regards to reliance on data, the concept of data literacy and a brief introduction into data science.

Part 2 introduces the Data-driven Decision-making approach. Author presents various concepts and provides definitions. Important concepts from this part are — intuition vs data driven, ethics, privacy and legal issues regarding to data-driven approach.

Part 3 covers the elements of the DDDM approach — Data, information, knowledge, fundamentals of statistics as well as patternicity. Parts 2 and 3 serve as a build-up to what is described in the biggest part of this book, namely…

Part 4 — which describes in full the DDDM process, from query formulation to making decisions. This is the most practical part of this publication.

Part 5 delves into the Data Analyst role, data-driven culture and additional concepts related to those topics.

There is a useful glossary of terms, that one can consult when needed.

My opinion

In my experience as a consultant and trainer I encounter many types of people. Some claim that they are data-driven. Claims are rather cheap. Some just are driven by data.

To quote Jim Barksdale, a former Netscape executive:

“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”

This is an important highlight when it comes to data vs opinion (or intuition) as a basis of decision making in my opinion.

This short publication is in my view superbly useful for various types of decision makers like product managers, while also may be of use to people working in other product related roles such as product owners.

What is important to mention here — this publication is about data-literacy, not about how to gather, filter, clean and process data, it’s not a handbook for data scientists or other types of analysts, though surely a person who would like to practice data science or related concepts can benefit from this primer on data literacy.

So if you are looking for a publication that goes in depth into data science then you may need to look elsewhere.

Aside of that, I see this publication as valuable to people who would like to know more about the data-literacy concept so that they can base their decisions on data, not pure opinion/intuition alone.

Well, after all people do have opinions, but not everyone have data to back up their decisions.

I liked many different examples given in the publication, to name a few:

- the fall of Purdue Pharma and OxyContin drug

- how eating ice cream relates to using cellular minutes during summer

- different types of queries and metrics

Retention drills were also useful, there were many of them and they surely help in anchoring various concepts, definitions into memory.

Physical vs Digital copy

I’ve read the print copy. It’s brief, concise, readable.

There are 9 black and white illustrations in the publication.

Minor gripes

I’ve found about two sentences that did not make much sense in the whole publications.

Well… damn gremlins doing their gremlinly job!

Aside of that, the publication is very well edited.

Summary & Closing thoughts

In summary I’d say that this publication is useful to many individuals:

- product managers — this publication is focused on product managers after all

- decision-makers of various sorts, who are interested in leveraging data in decision-making process

- people interested in learning about data-literacy

Concepts presented in this publication are clear and to the point, there is not one page wasted on irrelevant content.

I have found examples & retention drills useful, I will use some of those examples in my own work.

To conclude, I can surely recommend this publication. Go and read it if you’d like to be more data-driven in your work, life.

Thanks for reading and untill next one,

MJ