The STEMINISTS Podcast

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/ 

Everyone's obsessed with AI right now. Bigger models. Faster training. More GPUs. More data centers. More everything. But there's one small problem: none of that AI infrastructure exists without semiconductors. And the companies designing those chips are suddenly competing for the exact same resources.  
Welcome to the ultimate tech plot twist. 
In this episode of The STEMINISTS Podcast, host Phoebe Goh sits down with Diane Patton, Senior Product Manager at NetApp, to unpack how the AI boom is reshaping the silicon supply chain and putting unprecedented pressure on Electronic Design Automation (EDA), the critical technology behind modern chip design. 
As AI demand drives a global scramble for GPUs, RAM, and high-performance compute resources, chip designers are facing a new challenge. The infrastructure required to design semiconductors is becoming just as difficult to obtain as the infrastructure needed to run AI itself.  
Diane breaks down why EDA workloads are among the most demanding in enterprise IT, requiring massive compute power, low-latency storage, and the ability to process enormous volumes of data at scale. She also explains why waiting months for hardware procurement is no longer a viable strategy when innovation cycles are moving at AI speed and today's "latest and greatest" GPU can become yesterday's news before it's even installed.  
The conversation explores how semiconductor companies are turning to cloud and hybrid-cloud architectures to bypass infrastructure bottlenecks, accelerate chip development, and gain access to scalable compute resources on demand. From cloud bursting and intelligent data caching to storage tiering and AI-powered workflows, you'll hear how organizations are modernizing chip design for a world where flexibility is becoming a competitive advantage.  
Key Takeaways: 
What Electronic Design Automation (EDA) actually is 
Why AI is reshaping semiconductor infrastructure requirements 
GPU and RAM shortages facing chip design teams 
High-performance storage for EDA workloads 
Cloud vs. on-premises semiconductor design environments 
Hybrid cloud strategies for semiconductor companies 
Data management challenges in chip development 
AI-enabled opportunities for EDA workflows 
The future of semiconductor innovation and the silicon supply chain 
Whether you're a semiconductor professional, infrastructure architect, cloud strategist, or simply curious about the technology powering the AI revolution, this episode offers an insider's look at the future of chip design, EDA infrastructure, cloud computing, and the evolving silicon supply chain.
 
Learn More:
https://www.netapp.com/industries/electronic-design-automation-eda/  
https://www.netapp.com/blog/semiconductor-design-netapp-all-flash-portfolio/  
 
Connect with us!
https://www.linkedin.com/in/phoebegoh/ 
https://www.linkedin.com/in/diane-patton-7519634/ 

Security teams have more data than ever before. More dashboards. More alerts. More telemetry. More AI. So why is answering the simple question, "Are we actually secure?" still so difficult? 
In this episode of The STEMINISTS Podcast, Phoebe Goh sits down with Cole Neumark, Security Analytics professional at NetApp and Vice Chair of Proud, NetApp's LGBTQ+ employee resource group, to unpack why cybersecurity isn't just a technology challenge, it's a people challenge.  
With a career path that started in English and art history rather than computer science, Cole brings a refreshingly different perspective to cybersecurity. Together, Phoebe and Cole explore the reality of security analytics: turning mountains of signals, alerts, and vulnerability data into meaningful decisions that leaders can actually use. Because data alone doesn't create security. Context does. And context is still a very human skill.  
The conversation dives into the promises and pitfalls of AI in security, why faster isn't always smarter, and why the most valuable skill in an AI-powered future may be asking better questions. They also discuss the often-overlooked role of communication, creativity, and storytelling in helping organizations understand risk and take action.  
Cole shares how they transformed a cybersecurity training exercise into an immersive fictional world called Pineland, complete with fake social media posts, characters, and realistic incident scenarios. The goal? Make security memorable. Because people don't remember dashboards. They remember stories. And when security awareness feels more like solving a mystery than surviving a PowerPoint presentation, people pay attention. 
The episode also explores the importance of belonging, diverse perspectives, and community in cybersecurity. Through their work with Proud, Cole reflects on how creating spaces where people feel seen, valued, and empowered ultimately leads to better collaboration, stronger teams, and smarter security decisions.  
Key takeaways: 
Why security analytics is as much about communication as it is about data 
How storytelling can turn cybersecurity from forgettable to unforgettable 
Where AI accelerates security teams, and where human judgment remains essential 
Why humanities skills are surprisingly powerful in technical careers 
How diverse perspectives help organizations solve complex security challenges 
Why "security is defended by people" may be the most important cybersecurity lesson of all  
Plus: a conversation about metal music, live concerts, and why even cybersecurity professionals need a great soundtrack. 
 
Learn more: 
https://www.netapp.com/cyber-resilience/  
 
Connect with us!
https://www.linkedin.com/in/cole-neumark/
https://www.linkedin.com/in/phoebegoh/ 

We were promised that AI would make information easier to find. Nobody mentioned we'd also need a PhD in figuring out what to trust. 
In this episode of The STEMINISTS Podcast, Phoebe Goh sits down with Julia Fedorova, who leads Analyst Relations at NetApp, to tackle one of the biggest challenges in the AI era: making smart decisions when everyone, everything, and every chatbot seems to have an opinion.  
As generative AI floods our feeds with answers, recommendations, and hot takes, access to information is no longer the problem. The real challenge is knowing which sources are credible, which insights are backed by evidence, and which recommendations deserve a healthy dose of skepticism.  
Julia pulls back the curtain on the often-misunderstood world of industry analysts and explains why many of the biggest technology purchasing decisions still rely on research grounded in rigorous methodologies, market validation, and years of expertise. Together, she and Phoebe explore the idea of "pools of trust," the risks of AI-powered echo chambers, and why trusting the first answer you get is rarely a winning strategy.  
The conversation also dives into how enterprise buyers can avoid blind spots, why getting a second opinion matters just as much in technology as it does in healthcare, and how the smartest leaders are balancing AI-driven insights with human judgment. Along the way, Julia shares a surprisingly simple piece of advice for navigating a rapidly changing market: pause. Before you click, buy, migrate, modernize, or panic. Just pause.  
In this episode: 
Why more information doesn't automatically lead to better decisions.  
How industry analysts build credibility and trust through research and methodology.  
The growing challenge of AI-generated echo chambers and personalized information bubbles.  
Why enterprise buyers should seek multiple perspectives before making major technology decisions.  
How to evaluate whether a source is genuinely trustworthy.  
Why storage and data infrastructure have suddenly become central to AI conversations.  
The surprisingly underrated power of slowing down before making a decision.  
Plus, Julia weighs in on an age-old debate: do audiobooks count as real books? (Spoiler: yes, and she's willing to defend that position.)  
Because in a world overflowing with AI-generated answers, the real competitive advantage might just be knowing which questions are worth asking in the first place. 
As always, if you enjoyed this conversation please gives us a follow to stay in the loop for all things STEMINISTS. We would also love to hear what topics would you like for us to discuss. Feel free to share what you want to hear next.  
 
Learn More: 
https://www.netapp.com/artificial-intelligence/generative-ai-solutions/  
 
Connect with us!
https://www.linkedin.com/in/julia-fedorova-she-her-012a9624/  
https://www.linkedin.com/in/phoebegoh/ 

Everyone loves to say RAG is the answer to AI hallucinations. But what if the real answer is a little more complicated? 
In this episode of The STEMINISTS Podcast, Phoebe Goh sits down with Ramya Ravi, AI Developer Advocate at NetApp Instaclustr, to unpack what Retrieval-Augmented Generation (RAG) actually does, why it's become a cornerstone of enterprise AI, and where organizations are still getting it wrong.  
Together, they demystify the mechanics behind RAG, from embeddings and vector search to semantic search and hybrid search. Ramya explains why RAG isn't magic (it's math), how it helps AI systems find relevant business knowledge, and why simply implementing RAG doesn't automatically eliminate hallucinations.  
The conversation also explores one of the biggest challenges facing AI projects today: data quality. From duplicate documents and stale content to weak evaluation practices, Ramya shares practical advice on what organizations should tackle before rushing into a RAG implementation.  
Looking ahead, Phoebe and Ramya discuss long context windows, the next evolution of enterprise AI, and why the future may not be a choice between RAG and large context models, but a powerful combination of both.  
In this episode: 
What RAG actually is and how it works behind the scenes 
Why RAG reduces hallucinations but doesn't eliminate them 
The difference between keyword, semantic, and hybrid search 
Why data quality is often the hidden factor in AI success 
How to properly evaluate a RAG implementation 
When to use long context windows versus retrieval systems 
Why the future of enterprise AI will likely rely on both approaches  
If you've been told that RAG is the silver bullet for enterprise AI, this episode offers a smarter, more practical perspective on what it can do, what it can't do, and how to make it work in the real world. 
 
Connect with us! 
https://www.linkedin.com/in/ramya-ravi19/
https://www.linkedin.com/in/mekkacodes/ 
https://www.linkedin.com/in/phoebegoh/ 

Fan Favorite Replay: As AI continues to reshape how organizations work, lead, and grow, we're bringing back one of our most popular conversations on preparing people and businesses for the future. The insights in this episode are just as relevant today as when it first aired, making it a must-listen for leaders navigating the age of AI. 
Are you ready to lead in a world transformed by AI? You'd better be, and this episode will help you get there. 
Hosts Phoebe Goh and Mekka Williams sit down with Sarah Bartel, Accenture's Global Cloud Talent and Organizational Change Lead, for an engaging discussion about what it takes to prepare teams, cultures, and organizations for the future of work. Drawing on her extensive experience helping organizations navigate technology-driven transformation, and her role co-teaching MIT's Leading the AI Driven Organization course, Sarah offers practical strategies for leading through change while keeping people at the center. 
Together, they explore how leaders can build more adaptable organizations, why traditional approaches to talent management are evolving, and what it takes to create a workforce ready to thrive alongside AI. You'll learn why skills-based hiring and team design are becoming critical competitive advantages, how trust serves as the foundation for successful transformation, and why AI works best when it's used to elevate human potential rather than replace it. 
Key Takeaway: The future belongs to organizations that invest in both technology and people. Leaders who embrace continuous learning, build cultures of trust, and focus on skills over job titles will be best positioned to succeed in an AI-powered world. 
Whether you're leading a large-scale transformation, building the next generation of talent, or simply looking to become a more effective leader in a rapidly changing environment, this fan-favorite episode is packed with actionable insights and fresh perspectives. 
Tune in and discover how to lead with confidence, adaptability, and a people-first mindset in the AI era. 
 
Work, Workforce, Workers Age of Generative AI Report 
https://www.accenture.com/content/dam/accenture/final/accenture-com/document-2/Accenture-Work-Can-Become-Era-Generative-AI.pdf  
 
Leading the AI Driven Organization MIT Course  
https://executive.mit.edu/course/leading-the-ai-driven-organization/a054v00000r9U5cAAE.html  
 
Develop your skills with NetApp  
https://www.netapp.com/support-and-training/netapp-learning-services/learning-paths/  
 
Connect with us! 
https://www.linkedin.com/in/sarah-gottry-bartel/  
https://www.linkedin.com/in/mekkacodes/ 
https://www.linkedin.com/in/phoebegoh/ 

DORA may be a financial services regulation, but don’t let the name fool you, it’s rapidly becoming everyone’s problem. 
In this episode of The STEMINISTS Podcast, Phoebe Goh sits down with Katy Rankin, Cybersecurity Counsel at NetApp, to unpack the Digital Operational Resilience Act (DORA) and why technology vendors are suddenly finding themselves at the center of some very detailed and very urgent customer conversations.  
From supply chain risk and subcontractor transparency to regulatory scrutiny and personal liability, Katy explains why the days of simply pointing to a compliance certificate and calling it a day are long gone. As financial institutions race to meet DORA requirements, they're asking tougher questions of their vendors and expecting far more specific answers. The catch? Those answers aren't always easy to find.  
Phoebe and Katy explore what DORA is really trying to achieve, why regulators are worried about a single cyber incident taking down multiple financial institutions at once, and how organizations can move beyond box-ticking exercises toward meaningful operational resilience. They also discuss the growing role of cybersecurity counsel, the challenges of managing third-party risk at scale, and why asking better questions is often the key to getting better answers.  
In this episode: 
Why DORA is much more than "just another compliance requirement" 
The rising importance of cybersecurity counsel in modern organizations 
How third-party and supply chain risk have become board-level concerns 
What financial institutions should be asking their technology providers 
The balance between transparency, security, and practical risk management 
Why operational resilience is ultimately about keeping critical services running when things go wrong  
And because no STEMINISTS episode is complete without an unexpected twist, Katy shares a fascinating archaeological discovery. History, it turns out, may have had its own STEMINISTS.  
As always, make sure to like, follow and share with your inner circle.  
 
Learn More:
https://www.netapp.com/industries/financial-services/dora/
 
Connect with us! 
https://www.linkedin.com/in/katyrankin/  
https://www.linkedin.com/in/mekkacodes/ 
https://www.linkedin.com/in/phoebegoh/