Preparing your business for data science

Target Audience:

Senior folks who’d be responsible for or involved in a data science initiative

Abstract:

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!

Why I Want to Present This Session:

Additional Resources:

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5 Comments. Leave new

You’ve got a solid topic here. I’m really excited about watching this presentation. I think you could add a little bit of pizzazz to attract people. Some questions you may want to answer to flesh out your abstract:

Why is this an issue to begin with? Why is data science different than any other type of data projects? Why do we need special prep? Is there something unique about Data Science?

Are the investments in data science projects more substantial or more risky than normal projects? For example, one could argue that because it’s more of a niche topic, there is a bigger risk of being completely off-track.

Are these specific risks of starting an unprepared data science project? Or is it largely just avoiding wasted time and money, like anything else?

Overall, I think you can communicate why data science needs more due diligence than any other normal project.

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Great point – I guess it’s equivalent to having no developers and wanting to build an android app. You can invest a lot trying to go from 0 skills to lots of skill and you’ll still fail if you didn’t plan 🙂

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