The STEMINISTS Podcast

Most infrastructure conversations start with budgets. The best ones start with outcomes. 

If you've ever thought Storage as a Service was just a finance conversation, this STEMINISTS podcast episode might just change your mind. Or at the very least, make storage a lot more interesting. Phoebe Goh and Victoria Lam, Solutions Architect at NetApp, unpack why Storage as a Service should be viewed as a strategic enabler rather than simply a consumption model. From cloud transformation to AI readiness, Victoria explains why the organizations getting the most value are the ones planning for change, not just managing costs.  

Drawing on her experience helping global organizations, Victoria breaks down why forward-thinking leaders are looking beyond CapEx versus OpEx debates and focusing instead on agility, risk reduction, and long-term business outcomes. From unexpected AI growth and evolving workloads to hybrid cloud ambitions that never seem to arrive on schedule, she explains how Storage as a Service can help organizations stay ready without overcommitting to yesterday's plans.  

The conversation explores why so many companies still overbuy infrastructure, how consumption-based models can keep future cloud options open, and why successful technology strategies start with outcomes rather than hardware specifications. Because the smartest infrastructure decision isn't always, "What storage should I buy?" It's, "What problem am I actually trying to solve?"  

Plus, Victoria shares how her business background helps her translate between technical and business teams, offers insights from one of the world's most diverse technology regions, and reveals the unexpected truth that learning Australian slang might be more complicated than learning AI.  

If you've ever thought Storage as a Service was just a finance conversation, this episode might just change your mind. Or at the very least, make storage a lot more interesting. 🎙️ 

 

Key Takeaways 

Storage as a Service shifts infrastructure from a capital planning exercise to a strategic flexibility play. Instead of making long-term bets on future demand, organizations can adapt as business priorities evolve.  

The real ROI isn't just lower costs, it's lower risk. Consumption-based models help organizations avoid overprovisioning, reduce technology lock-in, and respond faster to changing business requirements.  

AI and data growth are making agility a board-level concern. As workloads become less predictable, infrastructure that can scale with demand becomes a competitive advantage.  

Cloud transformation requires optionality. For many organizations, Storage as a Service provides a practical bridge to hybrid cloud, keeping future migration paths open without forcing immediate decisions.  

Leading organizations start with outcomes, not infrastructure. The most effective technology strategies are built around business objectives such as growth, resiliency, and innovation, then aligned to the right operating model. 

If you enjoyed this episode, please follow, like and share.  

 

Learn More: 

https://www.netapp.com/novus/  

https://www.netapp.com/keystone/  

 

Connect with us!

https://www.linkedin.com/in/phoebegoh/

Everyone's obsessed with getting "good data." But here's the uncomfortable question: what happens when your AI has accurate data and still makes the wrong decision? 

In this episode of The STEMINISTS podcast, host Phoebe Goh welcomes back AI data expert Darnell Fatigati for a conversation that challenges one of the biggest assumptions in AI today: that data quality is enough. Spoiler alert: it ain’t.   

As AI agents move beyond answering questions and start making recommendations, triggering workflows, and taking action, the stakes get a lot higher. A data point can be technically correct and still completely miss the bigger picture. Without critical context, AI isn't making informed decisions. It's making confident guesses. And these guesses turn into mistakes that scale fast. 

Phoebe and Darnell unpack why data context, trust, governance, and even organizational memory are becoming essential ingredients for successful AI. They explore how businesses can help AI understand not just what happened, but why it happened, so agents can act responsibly instead of accidentally creating expensive chaos at machine speed.  

Key Takeaways: 

Why clean data doesn't automatically lead to smart AI decisions  

The difference between data quality and data integrity, and why it matters more than ever  

How context turns information into understanding and understanding into action  

Why organizational memory may be your next competitive advantage in AI  

Practical steps to make your data more AI-ready without boiling the ocean  

Plus, Darnell shares the story of an AI that managed a professional baseball game surprisingly well... until one very human variable showed up and reminded everyone that context still matters. ⚾🤖  

Because in the age of agentic AI, it's not enough for your data to be correct. Your AI needs to understand the assignment.  

Like what you hear? Follow and share The STEMINISTS Podcast with your network. The future of AI won't be built on more data alone. It'll be built on better understanding. 🚀 

 

Learn more about the Symbiotic relationship between AI and Data:

https://www.netapp.com/blog/symbiotic-relationship-data-and-ai/  

 

Check out how AI is only as good as the data that fuels it: 

https://www.netapp.com/blog/ai-is-only-as-good-as-the-data-that-fuels-it/  

 

Connect with us! 

https://www.linkedin.com/in/phoebegoh/ 

https://www.linkedin.com/in/darnellfatigati/

Planning infrastructure for the next five years right now feels a lot like packing for a vacation when the weather app says: "sunny, snowing, chance of dinosaurs." 

In this episode of The STEMINISTS Podcast, host Phoebe Goh sits down with Sarah Olibah, leader of a team of modernization solution architects at NetApp, to tackle one of the biggest challenges facing organizations today: making critical technology decisions when the only certainty is uncertainty.  

From AI initiatives that seem to appear overnight to shifting business priorities, evolving demand, and unpredictable market conditions, leaders are being asked to make infrastructure bets without the luxury of a crystal ball. Sarah explains why infrastructure planning isn't really about technology. It's about managing uncertainty and building enough flexibility to thrive when assumptions inevitably change. 

Together, Phoebe and Sarah unpack the sometimes awkward, often entertaining reality of bringing IT, finance, operations, and business leaders into the same decision-making process. Everyone wants the "right" answer, but they're often optimizing for completely different outcomes. The CIO wants agility, the CFO wants financial predictability, and the operations team wants reliability. Nobody's wrong, but nobody's speaking exactly the same language either. 

The conversation explores the growing discipline of value engineering, a framework that connects technology investments to measurable business outcomes. Sarah shares how bringing stakeholders together earlier helps organizations move beyond debates about products, pricing, and technical specifications to focus on what really matters: agility, resilience, ROI, flexibility, and long-term business value.  

In this episode, you'll discover: 

Why adaptability beats perfect forecasting every time.  

How to align technology, finance, and operations around shared business outcomes.  

The risks of designing solutions before defining priorities.  

How value engineering helps organizations navigate uncertainty with confidence. 

Why flexibility may be the ultimate competitive advantage in the AI era.  

And because no STEMINISTS episode is complete without a plot twist, Sarah closes out the conversation by sharing her latest hobby: wood whittling, including a handcrafted spoon that proves precision and patience aren't reserved for infrastructure planning alone.  

If your organization is trying to balance AI ambition with business reality, this episode offers a smart, practical, and surprisingly relatable perspective on making better decisions when the future refuses to cooperate. 

🎧 Enjoyed the conversation? Like, follow, and subscribe to The STEMINISTS Podcast. Then share this episode with your colleagues, leadership team, and fellow tech enthusiasts. After all, uncertainty may be inevitable, but navigating it is a lot easier when you're comparing notes with smart people. 

 

Learn more: 

https://www.netapp.com/data-infrastructure-insights/

 

Connect with us! 

https://www.linkedin.com/in/phoebegoh/ 

https://www.linkedin.com/in/sarah-olibah-1265b59b/

What happens when the technology reshaping the future is being built without enough women in the room? 

In this episode of The Steminists Podcast, host Phoebe Goh welcomes back Cecile Kellam, Senior AI Solutions Architect at NetApp and Head of AI Circles for Women in Technology (WIT), for a candid conversation about the AI participation gap and why women in tech communities may be more important than ever.  

From failing an AI-powered resume screening despite being qualified, to leading a global initiative that helps women build AI skills with confidence, Cecile brings real-world perspective to one of the biggest challenges facing the industry today. Together, Phoebe and Cecile explore how AI can amplify existing biases, why confidence often becomes a hidden barrier to participation, and what organizations can do to ensure AI is shaped by diverse voices instead of the same old perspectives.  

You'll also hear why community might be AI's secret weapon: creating safe spaces to ask the "dumb" questions, learn by doing, and influence how AI is designed, deployed, and adopted across the workplace. Because if AI is changing everything, everyone should have a seat at the table.  

And in a fun twist during "Cool and Current," Cecile shares how the latest women's health technology is finally putting women's experiences at the center of innovation.  

Because the future of AI shouldn't be trained on half the population's perspective. 

 

Key Takeaways 

AI isn't inherently fair. Without intentional inclusion, it can reinforce existing hiring and workplace biases.  

Representation matters because the people building AI directly influence the outcomes it produces.  

Confidence can be as significant a barrier as technical skills, especially when stepping into emerging fields like AI. 

Women's tech communities are evolving from networking groups into powerful engines for AI education, adoption, and influence.  

Organizations that invest in inclusive AI learning don't just do the right thing. They drive better adoption, stronger productivity, and greater business impact.  

You do not need a traditional technology background to succeed in AI. Curiosity, experimentation, and the willingness to learn can take you surprisingly far.  

The best way to close the AI participation gap might just be... using AI itself to learn faster and build confidence. 

Because the future of AI shouldn't be trained on half the population's perspective. So as always, if you enjoyed this episode, but sure to like, follow and share with your community.  

 

Learn more: https://www.netapp.com/artificial-intelligence/  

 

Learn more about WIT (Women in Technology): https://www.womenintechnology.org/  

 

Connect with us! 

https://www.linkedin.com/in/phoebegoh/ 

https://www.linkedin.com/in/%E2%98%81-cecile-kellam-%E2%98%81/

While the AI world has spent the last year racing from hype cycle to reality check, this conversation with Kris Cornwall, Senior Director of Product Marketing for Enterprise Storage at NetApp, feels more timely than ever. Because it turns out that building AI is the easy part. Getting it into production, scaling it, securing it, and making it deliver actual business value? That's where things get interesting.  

AI has officially graduated from science project status. It's now expected to drive business outcomes, deliver ROI, and operate with the same resilience, security, and reliability as any mission-critical enterprise workload. But getting there is easier said than done. Kris joins Phoebe and Mekka to unpack why data remains the biggest hurdle for organizations trying to scale AI, and what it really takes to move from proof of concept to production.  

The conversation dives into one of the hottest infrastructure topics in AI today: disaggregation. Kris explains how separating performance from capacity can help organizations scale AI workloads more efficiently, maximize expensive GPU investments, and avoid the infrastructure bottlenecks that often slow innovation.  

You'll also hear how enterprise-grade storage is evolving to meet AI's growing demands, from built-in ransomware protection and cloud integration to intelligent data mobility and metadata-powered data discovery. The takeaway? AI success isn't just about choosing the right model. It's about building the right data foundation underneath it.  

Tune in for a fan-favorite conversation on enterprise AI, data infrastructure, and the unglamorous truth nobody puts in the keynote: if your data strategy isn't ready, your AI strategy probably isn't either. 

 

Learn more: 

https://www.netapp.com/artificial-intelligence/

 

Connect with us! 

https://www.linkedin.com/in/phoebegoh/ 

https://www.linkedin.com/in/kristinecornwall/