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!
