Most people approach data the wrong way: They start with a data set, then use their favourite tools and techniques on it. This produces a narrow set of unsurprising results.
When we want to gain knowledge from the data, we should first do some thinking. Before we can answer how we first need to ask why. But this can be surprisingly challenging. The answer is to have a structure to think through all the aspects of a problem.

First why, then how

Vision is when we take a glimpse of what it will look like to meet the need with data. The vision consist of the following:<ul><li>A mockup explaining the intended results: It could take the form of reporting the outcome of the analysis in a few sentences, a graph, or a user interface sketch that shows how people might use a tool.</li><li>A sketch of the argument we're going to make: It is a loose outline of what we need to do to be convincing.</li></ul>

Vision (V)

We find context when we know who we are working with and why they're doing what they are doing. We learn the context when we talk to them about their long-term goals. The context provides a project with larger goals and helps to keep us on track. An example of context: A news organisation produces stories and editorials for a wide audience. It gets money from advertising and through premium subscriptions to its content. The final decision-maker is the head of online business.

Context (Co)

We can find structure when we create the scope for a data problem. A scope is the outline of a story of why we are focusing on this problem. The scope of a project consists of four parts, which is expressed as the mnemonic CoNVO.<ul><li>Context</li><li>Needs</li><li>Vision</li><li>Outcome</li></ul>When a problem is well-scoped, we will be able to talk about the project easily. The story will have context, conflict (the need), a resolution (the vision) and a happy ending (the outcome.) Once we have done the thinking using the CoNVO structure, we can go and collect the data.

Creating a scope for a data problem

The data scientist wants to know how the data and/or insights will be used. How will it be integrated into the organisation? Who will use the data, and why?An example of outcome: The marketing team needs to be trained in using the model (or software) to guide their decisions, and the success of the model needs to be gauged in its effect on the sales..

Outcome (O)

What needs could be fixed by intelligently using data? When we clearly explain a need, we are showing what could be improved by better knowledge. An example of needs:  We want to place our ads in a smart way. What should we be optimising?

Needs (N)

Thinking With Data is not about how to do data analysis but about first working out what the problem is that you're trying to solve.

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 projects Understand how to pin down the details of an idea, receive feedback, and begin prototyping Use the tools of arguments to ask good questions, build projects in stages, and communicate results Explore data-specific patterns of reasoning and learn how to build more useful arguments Delve into causal reasoning and learn how it permeates data work Put everything together, using extended examples to see the method of full problem thinking in action

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Outcome (O)

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