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2 weeks ago
Data Project Checklist

 
Originally published in Fast.ai, January 7, 2020.

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:

  • Strategy: What is the organization trying to do (objective) and what can it change to do it better (levers)?
  • Data: Is the organization capturing necessary data and making it available?
  • Analytics: What kinds of insights would be useful to the organization?
  • Implementation: What organizational capabilities does it have?
  • Maintenance: What systems are in place to track changes in the operational environment?
  • Constraints: What constraints need to be considered in each of the above areas?

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.

Organizational

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.

  • What data science skills are currently in the organization?
  • How are data scientists being recruited?
  • How are people with data science skills being identified within the organization?
  • What skills are being looked for? How are they being judged? How were those skills selected as being important?
  • What data science consulting is being used? In which situations is data science outsourced? How is this work transferred to the organization?
  • How much are data scientists being paid? Who do they report to? How are their skills kept current?
  • What is the career path for data scientists?
  • How many executives have strong data analysis expertise?
  • How is work for data scientists selected and allocated?
  • What software and hardware do data scientists have access to?

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