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Thinking With Data: How To Turn Information Into Insights

Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills.Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved.Learn a framework for scoping data projectsUnderstand how to pin down the details of an idea, receive feedback, and begin prototypingUse the tools of arguments to ask good questions, build projects in stages, and communicate resultsExplore data-specific patterns of reasoning and learn how to build more useful argumentsDelve into causal reasoning and learn how it permeates data workPut everything together, using extended examples to see the method of full problem thinking in action

Paperback: 94 pages

Publisher: O'Reilly Media; 1 edition (February 3, 2014)

Language: English

ISBN-10: 1449362931

ISBN-13: 978-1449362935

Product Dimensions: 6.4 x 0.2 x 9 inches

Shipping Weight: 4.8 ounces (View shipping rates and policies)

Average Customer Review: 3.9 out of 5 stars  See all reviews (15 customer reviews)

Best Sellers Rank: #142,518 in Books (See Top 100 in Books) #15 in Books > Computers & Technology > Programming > Algorithms > Data Structures #25 in Books > Computers & Technology > Programming > Software Design, Testing & Engineering > Structured Design #77 in Books > Computers & Technology > Databases & Big Data > Data Modeling & Design

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.

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.

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