“The best data teams aren’t built around superheroes. They’re built around trust.”
In Series 4, Episode 3, we sit down with Toby Henley Smith, Senior Analytics Engineering Manager at Moneybox, to talk about what makes data teams genuinely valuable to a business, and why some accepted “best practices” might actually be getting in the way.
We get into:
→ Why great analysts bring trust and rationality to decisions, not just reports
→ Why startups need psychological safety to experiment, get things wrong and learn quickly
→ How over-engineering and “best practice” stacks can become expensive distractions
→ Whether AI agents writing SQL could make heavyweight semantic layers less relevant
→ How AI is shifting engineering from writing code to defining problems and reviewing solutions
→ Why AI could accelerate the development of junior engineers rather than replace them
→ What tighter budgets and deliberate constraints can teach data teams about building better systems
→ Why cutting cloud costs starts with deciding what the business genuinely cannot live without
At the heart of the conversation is a simple question: are we building better data systems, or just more complicated ones?
What is Data Matas?
A show to explore all data matters.
From small to big, every company on the market, irrespective of their industry, is a data merchant. How they choose to keep, interrogate and understand their data is now mission critical. 30 years of spaghetti-tech, data tech debt, or rapid growth challenges are the reality in most companies.
Join Aaron Phethean, veteran intrapreneur-come-entrepreneur with hundreds of lived examples of wins and losses in the data space, as he embarques on a journey of discovering what matters most in data nowadays by speaking to other technologists and business leaders who tackle their own data challenges every day.
Learn from their mistakes and be inspired by their stories of how they've made their data make sense and work for them.
This podcast brought to you by Meltano - "Unlock the Insights in your Data"