So you're thinking about doing implementing data science project in your business?

You might be considering one or all of these options:
  • Hiring a data scientist
  • Using existing staff
  • Engaging a consultant
Like with most things in business, if you fail to plan, you plan to fail.

Starting out on a project without adequate planning, risks wasted time and money when you hit unexpected roadblocks. Additionally, putting a data science project into production without sufficient testing, monitoring, and due diligence around legal obligations, can expose you to substantial problems.

I want to help you avoid as much as risk as possible by taking you through my data science readiness checklist, including topics like:
  • Application development processes and capabilities
  • Data platform maturity
  • Use of data products within the business
  • Skillsets of existing business intelligence and other analytical teams
  • Analytical teams processes and capabilities
  • IT and analytical teams alignment to business goals
  • Recruitment, induction, and professional development processes
  • Legal, ethical, and regulatory considerations
Armed with the checklist, there'll be fewer "unknown unknowns" that could derail your project or cause extra cost. Let's get planning!
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