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Product details
File Size: 409 KB
Print Length: 94 pages
Simultaneous Device Usage: Unlimited
Publisher: O'Reilly Media; 1 edition (January 20, 2014)
Publication Date: January 20, 2014
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00HX91CGM
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I love this book. I really, really, REALLY love this book!In just six chapters and 94 pages, Thinking with Data: How to Turn Information into Insights by Max Shron, a data scientist, fills in several pieces in the process of creating insights from data. According to the author, "What is missing from most conversations is how important the 'soft skills' are for making data useful."The book provides a framework for defining the problem to be solved, not just "what can we do with this pile of data". In the first chapter, we learn the four parts necessary to scope a problem: context, needs, vision, and outcome, with the catchy acronym CoNVO. The book provides examples of scoping problems from multiple domains such as higher education, public policy, and retail. The subtitle of chapter one is "why before how" and as a business intelligence professional I have often found the "why" missing from the requirements gathering. So many times I've been told "just put this data on a report or dashboard- they know what they want" without a careful investigation of the business problem to be solved. I've been in business intelligence long enough to know that in many cases they really don't know what they want or the tedious requirements gathering of the project management office has choked the life out of any true customer requirement. Regarding needs, the author writes: "Not that the need is never [author's emphasis] something like 'the decision makers are lacking in a dashboard,' or predictive model, or ranking, or what have you. These are potential solutions, not needs... So if someone comes to you and says that her company needs a dashboard, you need to dig deeper."Crafting a vision for what a solved problem looks like then defining the outcome of what will happen after our work is done are two steps that are frequently missing from our projects. I believe defining these two pieces will take us from "Fred wants to see this data on a report or dashboard" to "this is what we need to monitor threats and opportunities that will help us be more successful". We can then create something of lasting value to the organization that can still be useful when Fred is no longer part of the picture.In subsequent chapters, Mr. Shron then deals with creating evidence, crafting arguments that defend one's analysis, and preparing for objections to our conclusions. "Our evidence is rarely raw data. Raw data needs to be transformed into something else, something more compact, before it can be part of an argument: a graph, a model, a sentence, a map. It is rare that we can get an audience to understand something just from lists of facts."The final chapter, Putting It Altogether, provides two extended examples, so you can see how the author crafts the context, need, vision and outcome as well as the arguments to defend his analysis. Because I'm a consultant, I wish that the book had one or two pictorial visualizations of the author's methodology and some proposed templates or worksheets for deliverables. But this book puts into words something that I often felt was missing from my own requirements gathering and I'm looking forward to writing my first CoNVO. Analysts and designers will find a lot to like in this book. Read it with a highlighter in hand.Did I mention that I really love this book?
This book is no longer than it needs to be and the title for 1 of it's 6 chapters is 'Causality' - how could I NOT like this book!I highly recommend this book to anyone who is involved in the development of software products. This is because above all else, it's book about critical thinking within the context of product - and even more specifically, how to use Data to improve our products.This book sits in a sweet spot of being high level enough to keep the content flowing as well as peppering it with pin point examples that succinctly illustrate the author's point. The author doesn't waste words overemphasizing points or tying concpets to any specific engineering or project management discipline. This should be appreciated as it respects both the reader's intelligence and time.If your a product manager, engineer, designer...or anyone else involved in creating and growing products, I recommend this book to you.Here is an excerpt which conveys my point. This is from Chapter 1 - Scoping: Why Before How:"...Rather than saying, "The manager wants to know where users drop out on the way to buying something," consider saying, "The manager wants more users to finish their purchases. How do we encourage that?" Answering the first question is a component of doing the second, but the action-oriented formulation opens up more possibilities, such as testing new designs and performing user experience interviews to gather more data.If it is not helpful to phrase something in terms of an action, it should at least be related to some larger strategic question. For example, understanding how users of a product are migrating from desktop to mobile versions of a website is useful for informing the product strategy, even if there is no obvious action to take afterward..."Also, Amazon doesn't have a table of contents for this book so here it is:1. Scoping: Why Before How2. What's Next?3. Arguments4. Patterns of Reasoning5. Causality6. Putting It All TogetherA. Further Reading
This book was suggested as further reading material in a Data Analysis course I took online.The book has very few pages, but provides lots of useful information and serves as, as the book’s last sentence indicates, “...a clear place to start for every beginner.â€I learned a lot from the book and will be going through it a second time. I did not take notes during this first pass but will do so during the next.While I found this book to be quite good (to the point and perfect for my needs), I completely agree w/ what 2-star reviews are saying: This is not for people who already have a background in solving problems or advancing products w/ data.This book is for anyone jumping into data science or a role that requires critical thinking or use of data to solve a problem. But if you already know how to structure problems, how to identify needed pieces of data to reach a solution, or have already successfully managed data-related projects, this book may be a little too elementary for you.The book touches on basics of scoping a project, making arguments, reasoning, and causality. In the final chapter it applies all that it has taught on 2 realistic use cases and summarizes the process in the last page.Again, if new to the field, read it. If already seasoned, skip it.
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