Jake Blair has built IoT systems that don’t just live in the cloud, they touch the real world. From his early days in industrial settings to building smart hardware at Bastille Networks and other companies, Jake has lived the full spectrum of what "Made in America" tech means today. In this episode, he shares hard-won lessons on hardware, data pipelines, and designing for environments where things must work.

Listen for insights on engineering resilience, navigating failure, and why empathy matters even in firmware.

🎧 Available on Spotify, Apple Music, and YouTube

🌐 Explore more at www.silvercreek.ai

In this episode of the Silver Creek Insights Podcast, we sit down with Brian Green, chief AI ethics officer at Health Vision AI and founding CEO of Envision Health AI. From building tools that assist clinicians to designing data systems that prioritize empathy, Brian shares what it really takes to design the future of medicine—responsibly.

🔍 In this episode, we explore:

• How AI is being integrated into healthcare workflows

• The importance of empathy-driven design in medical tools

• Challenges of AI implementation in high-stakes clinical environments

• The role of cross-functional collaboration between data teams and medical professionals

• Why ethical AI development in healthcare must balance innovation and trust

Whether you're a health tech founder, data scientist, or clinician exploring AI in medicine, this conversation offers real insights into designing AI systems that improve care, not just efficiency.

🎧 Listen now on Spotify, Apple Music, and YouTube
🌐 Dive deeper into our work at www.silvercreek.ai

In this episode of the Silver Creek Insights Podcast, we’re joined by Jasper Croome, a seasoned data visualization engineer who’s worked across industries—from Amazon and Nike to Carbon Direct and climate tech. Jasper’s career is a masterclass in building data experiences that are not just functional, but impactful.

We talk about:

His journey from support ops to leading data design

How AI is changing the way we prototype, build, and tell stories with data

What it takes to bring empathy into analytics

Why visualization is more than aesthetics—it’s strategy

🎧 Available now on Spotify, Apple Music, and YouTube
💻 Learn more at www.silvercreek.ai

In this episode of Silver Creek Insights, Isaac Strain shares how systems thinking has guided him from building rockets at SpaceX to tackling sustainability in agtech. He breaks down how to grow your own “skill tree,” design for real-world constraints, and build ideas that don’t just launch — but last.

🌱 Whether you’re an engineer, a founder, or just curious how big ideas take root, Isaac’s story is a blueprint for resilient, purpose-driven growth.

🎙️ Now streaming on Spotify and Apple Music — just search Silver Creek Insights.
✨ And if you want to see how we help teams align people, data, and AI with purpose, visit us at www.silvercreek.ai.

#SilverCreekInsights #SystemsThinking #IsaacStrain #Leadership #Reinvention

What happens when companies stop treating data like an afterthought—and start treating it like a product?

In this episode of Fee’s Fresh Takes, host Fee Wiley talks with Daniel Pham, Co-Founder of Silver Creek, about how the “data-as-a-product” approach helps organizations move faster, align better, and unlock real business value.

💡 They explore:

How clear ownership and shared goals eliminate confusion and silos

Why data engineers need to think like product managers

The cultural shift needed to turn data from tech debt to growth engine

Simple steps to identify your highest-value data use case

✅ When data is owned, discoverable, and tied to outcomes—it stops being a burden and starts becoming your advantage.

Ready to treat your data like a product?
Start your transformation with Silver Creek: SilverCreek.ai

Is your company treating data like a thermometer, reporting after the fact, or a thermostat that sets the future?

Nachiket Mehta,  Head of Data & Analytics Engineering at Wayfair, joins Fee to unpack how integrating AI in business processes requires more than shiny tools. We cover:

Why artificial intelligence integration services fail without people/process alignment

How embedded data engineers turn silos into purpose-driven teams

Real-world wins from decentralizing architecture and embedding AI integration within tools

The shift-left mindset that makes “data as product” more than a slogan

If you’re serious about AI, culture, and measurable impact, this episode is for you.

👉 Learn how Silver Creek helps companies build purposeful, AI-ready systems at www.silvercreek.ai

Are you drowning in point‑solutions, muddled tech stacks, and “AI magic button” promises? 🌀 In this Fee’s Fresh Takes episode, host Fee Wiley sits down with Daniel Pham, Co‑Founder of Silver Creek Insights, to unpack the partnership ecosystem—the human‑first network of vendors, integrators, and champions that turns fragmented tools into real business value.

They dive into:

Why trust, relationships, and shared values beat feature checklists every time

How to simplify an over‑engineered data architecture without sacrificing scale or ROI

The role of a “team captain” consultancy in coordinating Snowflake, dbt, Omni Analytics & other top partners

Rapid‑fire “Do This, Not That” tips for activating partners, following up, and avoiding one‑size‑fits‑all pitches

Ready to turn chaos into a coordinated win? Visit SilverCreek.ai to discover how our tech‑agnostic experts streamline your data & AI journey—so your ecosystem plays (and wins) as one. 🚀

What is a modern data stack—and is yours working for you or against you?

Tristan Handy, founder and CEO of dbt Labs, joins us to explore the next evolution of analytics engineering, dbt Fusion, and the rise of AI in analytics workflows.

📌 Highlights:

dbt Fusion explained: faster compile times, SQL comprehension, cost savings

What makes a data lake technology stack valuable in the age of open standards

The impact of AI data visualization tools on insight delivery

How AI is being used in sports analytics today and what business leaders can learn from it

The real opportunity of predictive analytics AI

This is a must-watch for data leaders, engineers, and anyone building or maintaining a modern stack.

#SilverCreekInsights #dbtFusion #ModernDataStack #AIAnalytics #DataEngineering #PredictiveAnalyticsAI

What is real-time data—and does your business really need it?

In this episode Dr. Josh Simmons (Director of Engineering ) breaks down the real-world value of real-time data analytics, including where it matters—like in finance, defense tech, or trading—and where it’s overkill.

We also talk about:

How to scale engineering teams without losing quality

How AI really integrates with legacy systems

What makes a great tech leader (and a terrible one)

Wondering how to get real-time futures data on TradingView? Curious if there’s any charting service that includes real-time data? Josh shares the trade-offs, myths, and why less is often more.

🔥 One of our sharpest episodes yet.

Dr. Josh's views are his own and don't reflect the views of any employer past or present.

Welcome back to another episode of Fee’s Fresh Takes.
Today’s topic: real-time data.
And here’s my hot take—not every company needs it.

So we’re breaking it down:

What is real-time data, really?

Where it’s critical

Where it’s overkill

And how to know if your team is even ready

So Daniel—let’s jump in. What are the top two industries where real-time isn’t just helpful, but absolutely critical?

Daniel Pham:
Yeah, let’s get into it. The classic answers are finance, logistics, and healthcare.
But even then—what is real-time data analytics if the insights don’t drive decisions?
It’s not just about speed. It’s about relevance.

For example, in finance—yes, you need real-time alerts for fraud detection or when a card is swiped. You need those systems to react instantly. Same with real-time futures data on platforms like TradingView—people want second-by-second data to make trading decisions. But that’s for very specific use cases.

Fee:
Exactly. How to get real-time data on TradingView is a hot question because people think faster always means better. But it’s not universal.

Like credit scoring? That doesn’t need to be instant. That can be a weekly batch. GPA in education? That’s semester by semester. We don’t need a live GPA dashboard.

Daniel:
Totally agree. And let’s be honest—is there any charting service that includes real time data? Yes, but it comes at a cost.
Many teams chase real-time because of FOMO or buzzwords—AI, real-time, automation—but don’t assess the ROI. And those cloud compute bills add up fast.

Fee:
Right. I see it all the time—teams obsessing over data dashboards. Real-time dashboards. Executive alerts. All day. But if you’re not acting on it? What’s the point?

Daniel:
Exactly. We need to zoom out.
What is real-time data analytics if you’re just reacting instead of making smart, aligned decisions?

Sometimes, less is more. Instead of obsessing, get a consistent system.
It’s like working out—you don’t need to do CrossFit twice a day. You need the right plan that works for your body. Same with data.

Fee:
So real-time does matter—for things like sports betting, finance, or anything where users make split-second decisions based on data.

But for most orgs? Batch is still strategic.
Weekly or daily refreshes still count. It’s not outdated—it’s efficient.

Daniel:
Exactly. Think: “What decisions are we driving? Who needs this data—and when?”
That’s how you figure out if real-time is a necessity or a distraction.

Fee:
Couldn’t agree more. Final recap?
✔ Not all fast data = better decisions
✔ Real-time is expensive—use it wisely
✔ Align your stack to value, not just hype
✔ Batch is still powerful
✔ And a common data language beats 20 dashboards

Thanks for tuning in! Catch you next week on #FeesFreshTakes.