Data project checklist

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As we discussed in Designing Great Data Products, there’s a lot more to creating useful data projects than just training an accurate model! When I used to do consulting, I’d always seek to understand an organization’s context for developing data projects, based on these considerations:

The analytics value chain
The analytics value chain

I developed a questionnaire that I had clients fill out before a project started, and then throughout the project I’d help them refine their answers. This questionnaire is based on decades of projects across many industries, including agriculture, mining, banking, brewing, telecoms, retail, and more. Here I am sharing it publicly for the first time.


Data scientists

Data scientists should have a clear path to become senior executives, and there should also be hiring plans in place to bring data experts directly into senior executive roles. In a data-driven organization data scientists should be amongst the most well-paid employees. There should be systems in place to allow data scientists throughout the organization to collaborate and learn from each other.


All data projects should be based on solving strategically important problems. Therefore, an understanding of business strategy must come first.

Some of the kinds of things that may be important profit drivers at an organization
Some of the kinds of things that may be important profit drivers at an organization


Without data, we can’t train models! Data also needs to be available, integrated, and verifiable.


Data scientists need to be able to access up to date tools, based on their own particular needs. New tools should be regularly assessed to see if they significantly improve over current approaches.


IT constraints are often the downfall of data projects. Be sure to consider them up front!


Unless you track your models carefully, you may find them leading you to disaster.


For each project being considered enumerate potential constraints that may impact the success of the project, e.g.: