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Dr. Ryan Ries here, back in your inbox.
This week, I wanted to share some insights from my recent interview on the Care to Lead Podcast with Cynthia Corsetti.
We had a great conversation and dove deep into the world of AI, data science, how businesses can stay ahead, and what the future of business looks like.
You can think of this week’s Matrix as your “TLDR” of the pod episode.
The Power of Working Backwards
One concept that really stood out during our chat was the idea of "working backwards" — a strategy that's been championed by AWS and other tech leaders.
It's a deceptively simple approach: start with your end goal, identify what needs to be in place to achieve it, and then create a roadmap working back from that vision.
Key things to consider when working backward are all of the different personas that you will need to accommodate.
Each persona will have a different end goal, but many of the infrastructure and data pieces will overlap. Just the final presentation will be different.
Often the more senior the role, the more likely they care about a roll-up of the data from other areas.
The CEO cares about the overall business health, while the department manager cares about their department’s KPIs.
This method has been invaluable in my work, ensuring clarity and focus even in the most complex AI and data science projects.
AI Across Industries
We talked about how AI is disrupting so many industries right now.
When we hear about AI’s disruption, it can feel scary, but I would say the majority of AI use cases are disrupting for good and reducing painful tasks that we all hate doing anyway! This includes things like unit testing.
In healthcare, for instance, AI is changing everything from X-ray analysis to patient record summarization.
I saw this article pop up on my LinkedIn feed the other day. MIT’s deep learning model can predict cancer up to five years in advance.
It’s an older article from 2019, but it's a great illustration to show how AI can be used to make things like cancer detection more equitable.
Additionally, I’ve talked a few times in the Mission Matrix about sustainability AI use cases.
Even in energy management, AI is tackling complex challenges like predicting power grid demands and optimizing gas pipeline pressure.
AI's impact is far-reaching, and the potential applications are limitless.
Think about where we were with AI just one year ago… Can you imagine where we’ll be this time next year?
The Data Dilemma
A recurring theme in our discussion was the critical importance of data.
I can't stress this enough: the quality of your data trumps the sophistication of your algorithms every time.
Clean, relevant data is the foundation of any successful AI initiative.
Sometimes, the data reveals truths about a business that challenge long-held assumptions.
That's why I'm a big advocate for bringing in outside perspectives — fresh eyes can often uncover insights that internal teams might overlook.
Ensuring Clarity in Complex Projects
When it comes to managing complex AI and data science projects, clarity is king.
I always strive to define the customer's needs clearly, use agile methodologies for regular check-ins, and keep the end vision front and center.
Remember, if you don't know what you're trying to build, you'll never build it successfully. I often tell my team, "Never build to build!"
Advice for Aspiring Data Scientists
For those of you looking to break into the field of data science, I shared some advice that I hope resonates.
Be open to challenges, even if they seem outside your comfort zone. Consider targeting larger product companies for your first role, as they often have more resources for training new talent.
And above all, network and be patient. This field is competitive, but persistence pays off.
Lastly, a fun fact about me
On a lighter note, I shared a personal tidbit during the interview — I've recently rediscovered my love for LEGO!
They're now sprawled across my desk, and they often serve as a reminder to me that sometimes the best solutions come from piecing things together in new and unexpected ways.
Let me know your thoughts and if you listen to the full podcast episode.
Until next time,
Ryan
PS. Na Yu and Jonathan LaCour are hosting our next generative AI Ask Us Anything event on September 14th. Will I see you there? Here’s the link to sign up. Bring all your questions!
Now, time for this week’s AI-generated image and the prompt I used to generate it.
"Generate an image of a man working at his desk on an AI project on AWS. The angle of the image should be from behind the man, and you can see what is going on on his computer screen. He is architecting on AWS. You should also see legos all over his desk, but he is working on building a shark with the legos."
This one gave me a good laugh because I see how the AI got confused with my prompt and added the shark on screen versus on my desk… oops!
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