Julia-fying Your Data Team: the Ultimate Upgrade

This talk is designed to give data/insights/decision intelligence team leads a better understanding of the potential of Julia and how it can be effectively adopted in their teams. I'll be discussing the advantages and disadvantages of adopting Julia, drawing on my own experience and sharing some of the lessons I've learned along the way. I'll also be sharing several examples of Julia's unreasonable effectiveness that have supercharged our small team.

This talk covers using Julia in data, insights, and decision intelligence teams. As a leader in a small data organization, you have to be careful about adopting new technologies, because you don't have any capacity to spare. That's why I'm here today to share my experiences with adopting Julia and give you a better understanding of its potential for your team.

First, I'll cover the advantages of using Julia in your data team. You've heard that Julia is faster and easier for setting up your projects (and replicating your results in the future). But it's the Julia design philosophy and its ecosystem that makes it so productive to use. In addition, its tooling and community provide tons of learning opportunities, so you'll keep growing and improving just from your everyday work. Additionally, Julia has an unbeatable time to insight - not just the execution time, but end-to-end, from starting the project to sharing the insights with stakeholders, I'll demonstrate what I mean and why that is.

Of course, no technology is perfect. I'll also be discussing some of the downsides to using Julia, such as internal challenges, the difficulty in finding talent with Julia skills and some red flags when you shouldn't adopt Julia (eg, large existing code, strict cross-dependencies, or deployment requirements.)

But the most compelling reason to consider Julia is its unreasonable effectiveness. I'll be sharing a few examples of how Julia has supercharged our small team and how you can benefit from it too (eg, workflow setup, re-use via small packages, documentation system, composability of tools, user-friendly APIs on custom types)

In conclusion, while there are certainly some downsides to adopting Julia, the advantages and its unreasonable effectiveness make it a worthy consideration for data, insights and decision intelligence teams. I hope that by sharing my experiences and examples, I've been able to give you a better understanding of whether Julia is the right choice for your team and how you can effectively adopt it.