Creating a scope for a data problem - Deepstash

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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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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.

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:

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.

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?

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.

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