In this episode of How I Met Your Data, Christina Hois, a data leader with over 30 years in financial services, joins Anjali and Junaid to explore why data challenges rarely stem from technology — and almost always come down to people.

Christina shares why breaking down cultural silos, building stakeholder trust, and prioritizing data tied to real business outcomes matter more than deploying new tools. The conversation highlights the critical role of emotional intelligence, credibility, and collaboration in making data governance actually work.

The takeaway is clear: technology enables scale, but trust and teamwork determine success.

In this episode of How I Met Your Data: The Prompt, Anjali and Karen dig into one of the fastest-emerging patterns in development today: vibe coding - the practice of describing what you want and letting an LLM generate the code. It’s new. It’s evolving. And right now, it’s causing as much frustration as it is excitement.

Karen breaks down what vibe coding actually looks like in practice: developers prompting AI to produce entire features or files, navigating the wildly different “personalities” of today’s LLMs, and learning how to guide systems that might generate brilliant structure… or unintended chaos. Together, they talk through the real friction points - overly eager model behavior, unexpected file changes, incomplete suggestions, and the creeping loss of hands-on debugging skills that used to tie engineers closer to their code.

But underneath the surface is a bigger enterprise theme. The rise of vibe coding speaks to deeper issues: end users who still aren’t getting what they need, bottlenecks in IT and data teams, and the rapid expansion of citizen development as people search for faster paths to outcomes. Anjali and Karen unpack the operational and governance implications, from maintainability and handoff challenges to compliance blind spots and the need for standards that can coexist with AI-assisted creation.

They also dive into where AI does shine today - those repetitive, operational workflows that quietly save teams hours - and why focusing on value, ownership, and workflow design matters far more than chasing the next flashy LLM demo.

This episode is an honest, grounded look at how AI-assisted development is taking shape: what’s promising, what’s painful, and what it means for teams trying to build responsibly, collaboratively, and at scale.

In this episode of How I Met Your Data, Anjali and Junaid sit down with Tony Shaw, Founder & CEO of DATAVERSITY - the force behind Enterprise Data World (EDW) and DGIQ. Tony traces the early origins of a “metadata conference” that became a global learning platform, then gets candid about what actually moves the data profession forward: cycles, culture, and community.

We dig into how conference content evolves (remember when data modeling was the headliner?), why governance remains a business function first, and how AI is reshaping both programming and the attendee experience; think smarter discovery of talks, better content matching, and, perhaps someday, intentional networking that beats hallway serendipity. Tony also shares the story behind DATAVERSITY’s Women in Data focus and why younger, more global audiences are changing the room—for the better.

• The origin story: buying a tiny “metadata” event and building DATAVERSITY into a global education platform

• Surviving economic cycles: training, travel, sponsorship, and how digital finally scaled during COVID

• What’s changed (and what hasn’t): the rise, fall, and return of semantics; AI’s pull on modeling and governance

• Governance as a business sport: why DGIQ draws nearly 50% of non-IT leaders

• Global signals: banks in Uruguay winning best-practice awards; Saudi Arabia’s push on data & AI capability

• AI at conferences: from content discovery to future attendee matchmaking (and the privacy guardrails we’ll need)

• Women in Data: mentorship, career design, and programming that’s open to everyone, but designed to meet real gaps

You lead data/AI programs, run governance in the messy middle, or care about how our field learns—together. Also useful if you’re deciding whether to bring your non-data peers to a data conference (short answer: yes).

In this episode of How I Met Your Data Today, hosts Anjali and Junaid sit down with financial services industry veteran Julia Bardmesser about the significance of political capital in data leadership.

Julia shares insights from her 25-year career, working across major institutions such as Bloomberg, Citi, Deutsche Bank, and Voya Financial, before founding her strategic advisory firm. She clarifies what political capital is (and isn't) and how it affects the ability to drive data and AI initiatives within organizations.

The discussion covers identifying key relationships, managing obstructionists, the importance of high EQ, and tactical advice on when and how to spend political capital effectively. Julia emphasizes that delivering real value to the organization is the cornerstone of building lasting political capital. The conversation is filled with real-life examples and lessons learned, making it a must-listen for data professionals and leaders navigating corporate landscapes.

In this episode of The Prompt, hosts Anjali and Karen dive into the latest headlines about AI adoption and ROI, unpacking why 95% of AI pilots are reportedly failing while a select 5% succeed.

Drawing parallels to the dot-com era, they explore the real reasons behind AI project failures, the importance of vendor partnerships, and the pitfalls of unrealistic expectations in proof-of-concept initiatives.

The conversation highlights the need for clear business objectives, data governance, and a pragmatic approach to technology adoption. Tune in for candid insights, lessons learned, and a fresh perspective on what it really takes to drive value with AI in today’s organizations.

In this episode of The Prompt, Anjali, Karen and Junaid unpack the unsettling news that shared ChatGPT conversations—via the platform’s “share” feature—were indexed by Google, making sensitive prompts and personal details publicly searchable.

Topics explored include:

• How the share link works, and why “public” really means public

• Why even seemingly benign prompts can build an unnervingly detailed profile of you

• The cultural differences in privacy expectations between the US and Europe—and why drones over your backyard might make you rethink your stance.

• The personal responsibility side of AI use: knowing what not to put into a chatbot.

From governance implications to everyday “would you want your search history read out loud?” moments, this conversation is a reminder that in the digital age, once it’s out there, it’s out there.

In this episode of How I Met Your Data, hosts Anjali, Junaid, and guest Jay Krish dive deep into the rapidly evolving world of Agentic AI—a paradigm shift from rule-following automation to systems capable of autonomous decision-making. Jay, a seasoned financial services leader and AI thinker, breaks down what Agentic AI really is: a network of large language models working together to reason, adapt, and act independently toward a goal.

Together, they explore:

• 🧠 How Agentic AI differs from traditional automation and ML

• 🚸 Why AI autonomy should be earned like parental trust—and the stages of building that maturity

• ⚖️ The escalating risk matrix: Who owns the risk when AI goes rogue?

• 🛡 Why human-in-the-loop design must persist, even in autonomous environments

• 🌎 The unspoken costs: environmental impact and the power-hungry infrastructure behind AI innovation

• 🔮 What the rise of agent-based systems means for the future workforce—and how to prepare for what's next

Jay offers practical advice on getting started, reframing fear into forward motion, and bringing ethical, human-centered thinking into the AI build process.

If you're curious about AI's next frontier, the risks and rewards of autonomous systems, and how to stay resilient in the face of transformation—this one's for you.

In this episode, Junaid reflects on the CDOIQ Symposium in Boston, emphasizing the overwhelming focus on AI, especially AI agents, and their impact on white-collar jobs. We discuss the immense value of networking at conferences and debate whether CDOs overemphasize data quality at the expense of other critical areas like culture and literacy. And finally, we explore where CDO's oversteer and what they under value.