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).