- Taschenbuch: 91 Seiten
- Verlag: O'Reilly and Associates; Auflage: 1 (31. Januar 2014)
- Sprache: Englisch
- ISBN-10: 1449362931
- ISBN-13: 978-1449362935
- Größe und/oder Gewicht: 15,2 x 0,5 x 22,9 cm
- Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
- Amazon Bestseller-Rang: Nr. 267.345 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Thinking with Data: How to Turn Information into Insights (Englisch) Taschenbuch – 31. Januar 2014
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Über den Autor und weitere Mitwirkende
Max Shron runs a small data strategy consultancy in New York, working with many organizations to help them get the most out of their data. His analyses of transit, public health, and housing markets has been featured in The New York Times, Chicago Tribune, Huffington Post, WNYC, and more. Prior to becoming a data strategy consultant, he was the data scientist for OkCupid.
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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 How
2. What's Next?
4. Patterns of Reasoning
6. Putting It All Together
A. Further Reading
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?
The tools and techniques of data science are important to know, but without a strong foundation in how to think using data, those tools will be ineffective. The failures you are going to have when working with data are more likely going to be because of trying to solve the wrong problem, compared to a mistake in the technical application of an algorithm.
This book is a quick read without a lot of overly technical language or ideas. The concepts in the book are presented simply enough that any person can benefit, not just data scientists.
Sounds familiar? If yes, then this book is a must read, regardless of where you sit on the table (analyst or requester). This book is about the importance of collaboration and planning for a data analysis project. Sure. Many businesses will swear that such a process exists, but how well is it working? This book provides useful guidelines and suggestions that can help in evaluating your business's data culture so hat meaningful outcomes are derived that will lead to purposeful action.