The View on Data

How AI is reshaping the way data practitioners work

Sam Ferguson, staff product designer at dbt Labs, joins Faith, Paige, and Ric to talk about what AI is actually changing in the day-to-day work of data practitioners and designers alike. From inline code comments as agent context to the collapse of build costs in product design, Sam shares how constraints that have long defined both design and data work are starting to reorganize. The group also gets into collaboration, governance, and why a lot of the hardest questions AI is surfacing are ones the data industry has been asking for years. In this episode: * Why inline code comments could become one of the most valuable inputs for AI agents working in your dbt project * The typist analogy: how constraints disappear and roles reorganize around new ones * What outcome-driven workflows actually mean for data practitioners and how it connects to state-aware orchestration * Why human attention is becoming the real constraint in a world of infinite answers and infinite features * How to think about delegating the parts of your job you like least to AI, and protecting the parts that matter most * Why governance and coherence at scale are the next big challenges as agentic workflows become the norm

2026 ins & outs (data edition)

In this episode of The View on Data, Faith, Ric, and Jerrie discuss their intentions for the new year, focusing on what is in and out in the data world. They emphasize the importance of simplification in data processes, the need for strong documentation, and the value of making learning engaging. The conversation also touches on the pitfalls of over-engineering and the importance of embracing technical challenges. Takeaways: * Simplification is key in data work. * Strong documentation enhances understanding and collaboration. * Learning should be engaging and relatable. * Over-engineering complicates processes unnecessarily. * Embracing technical challenges is essential for growth. * Curiosity and the willingness to learn are vital in the workplace. * Making work fun can improve productivity and morale. * Frameworks help in organizing and simplifying workflows. * AI tools should assist, not replace human thought. * It's important to communicate clearly and avoid jargon.

The power of community in data

Finding your people in data can change everything. Not just for your career, but for your confidence, your learning, and your sense of belonging in the work. In this episode of The View on Data, Jerrie, Faith, and Ric talk about what community has looked like for them across the data space, from Coalesce and local meetups to Slack groups, mentorship networks, and the (now legendary) era of Data Twitter. They talk about: • What "community" actually means in the data world, beyond networking • Where to find your people, from conferences and meetups to online spaces • Why mentorship is often "piecemeal" and skills-based, not one perfect guide • How to ask for advice in a way that's specific, respectful, and effective • How community ebbs and flows over time, and why that's normal • The long-term value of relationships for thought partnership, support, and growth

Data and the art of balance

In data, there's always more to do. More metrics, more dashboards, more to learn. But what happens when "more" becomes too much? In this episode of The View on Data, Ric, Faith, Paige, and Jerrie share how they've learned to slow down, set limits, and define success on their own terms. They talk about: • Why burnout can sneak up on even the most passionate people • Boundaries that protect deep work and personal time • Redefining what "growth" means in different seasons of your career • The guilt of saying no (and why it's okay) • Finding joy outside of work through creative outlets and rest A real conversation about balance, burnout, and what it means to build a career you can actually enjoy.

The art of data-driven decision making

In this episode of The View on Data, hosts Faith McKenna, Paige Berry, and Erica 'Ric' Louie, along with new co-host Jerrie Kenny, explore the intricacies of data-driven decision-making, the importance of data quality, and effective communication of data insights. They discuss the challenges of working with imperfect data, the significance of asking the right questions, and the balance between intuition and data analysis. The conversation also delves into visualization techniques and the importance of tailoring data presentations to different audiences. The episode concludes with valuable advice for data practitioners on navigating their careers and improving their data communication skills. Takeaways * Data-driven decisions vary in definition across organizations. * Understanding the purpose of data quality is crucial for effective decision-making. * Asking the right questions can lead to better data insights. * It's important to recognize when data doesn't need to be perfect. * Empowering stakeholders to make decisions with available data is key. * Experimentation with data can lead to innovative solutions. * Effective communication of data requires understanding the audience. * Visualizations should tell a clear story and highlight key insights. * Data practitioners should not shy away from pushing back on unrealistic expectations. * Summarizing data insights effectively is a valuable skill.