Thinking with Data - Deepstash

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First why, then how

First why, then how

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.


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Creating a scope for a data problem

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.

  • Context
  • Needs
  • Vision
  • Outcome

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.


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Context (Co)

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.


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Needs (N)

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?


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Vision (V)

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:

  • 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.
  • A sketch of the argument we're going to make: It is a loose outline of what we need to do to be convincing.


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Outcome (O)

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..


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I’ve got 99 problems and I’m not dealing with any of them.

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.

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