00:00 Intro02:12 Why stakeholder requests are often wrong05:09 Business discovery before solutions10:37 Treating dashboards as data products17:00 When you disagree with the stakeholder21:50 The Mom Test and better discovery meetings28:02 Teaching data professionals to really listen40:09 Can junior analysts think without AI?44:44 Management starts with listening48:06 Culture, communication and consultingGreat data professionals do not just take requirements. They listen closely enough to understand the problem behind them.In this episode of The Data Hustle, we speak with Gabriela Costa, an analytics engineer and team lead at Indicium AI, about why communication and discovery are as important to good data work as SQL, modeling or engineering.Gabriela explains why immediately accepting “I need a dashboard” can send a data team in completely the wrong direction. Instead, she separates business discovery from data discovery: understanding how somebody works today, what decisions they are trying to make, what constraints they face and what problem they are actually trying to solve before deciding what should be built.We discuss treating dashboards and other analytical outputs as living data products rather than one-off projects, measuring adoption, uncovering requirements stakeholders themselves may not know they have, and what to do when your professional judgment tells you that the solution a stakeholder is asking for is the wrong one.We also get practical about stakeholder interviews. Gabriela shares how ideas from The Mom Test influence her discovery process, why scripted question lists can make junior consultants worse listeners, how she prepares teams for client conversations, and why understanding who actually owns a decision can prevent endless cycles of conflicting requirements.Later, we get into one of the more uncomfortable questions facing junior data professionals today: are people becoming too dependent on AI? Gabriela talks about analysts presenting code they cannot explain beyond “ChatGPT told me to do it,” and why using AI effectively still requires understanding the logic well enough to question, validate and defend what it produces.Finally, we talk about management, genuinely listening to the people you lead, and how Gabriela’s background in international relations shaped the way she approaches negotiation, different cultures, competing perspectives and consulting.Find Gabriela:LinkedIn: Gabriela Costa — @gabrieladadosIndicium AI: indicium.ai

00:00 Intro01:09 From test automation to web data03:27 Trust, guardrails and AI seatbelts13:27 When agile automation becomes fragile21:39 Restoring trust in messy systems28:14 Who owns the open web?39:21 Deterministic AI for regulated workflows48:12 What’s next?AI is moving from generating answers to making decisions. That changes the stakes.In this episode of The Data Hustle, we speak with Sarah McKenna, CEO of Sequentum, about why AI needs the equivalent of seatbelts: clear guardrails, acceptance criteria, audit trails, deterministic workflows and accountable human review.Drawing on two decades across software test automation, DevOps, data quality and enterprise web extraction, Sarah explains why automation becomes fragile when teams optimise for speed without building in trust. We discuss reusable components, versioning, validation, low-code visibility, security reviews and the role of AI coding tools in maintaining large-scale data systems.We also zoom out to the growing conflict around the open web: publishers, AI crawlers, web-scraping companies, bot identification, paid data access and agentic commerce. Sarah explains why regulated organisations often reject AI at runtime, even while using it aggressively to accelerate development and strengthen governance.Find SarahLinkedIn: https://www.linkedin.com/in/sarahransommckenna/Sequentum: https://www.sequentum.com/

00:00 Intro00:49 From consulting to building a product05:00 Finding the right co-founder07:07 What data teams should delegate to AI11:21 Context engineering and the future of dashboards18:36 Avoiding the agent cemetery23:59 Do you need a semantic layer first?29:47 Inside Nao’s open-source analytics agent38:22 How data professionals create visible value44:48 What’s next for Nao?AI agents promise to let anyone analyse company data through a conversation—but reliable agentic analytics requires far more than connecting an LLM to a database.In this episode of The Data Hustle, we speak with Claire Gouze and Christophe Blefari, co-founders of Nao, about building an open-source framework for agentic analytics. They explain how their experience in consulting and data leadership exposed a recurring problem: business teams want answers faster, while data teams remain trapped as overloaded bottlenecks.We discuss which parts of data work should be automated, why analytics engineers may evolve into context engineers, whether traditional dashboards are becoming obsolete, and why companies risk replacing their dashboard graveyards with agent cemeteries.Claire and Christophe also break down the role of semantic layers, metadata and reusable context; why teams should not wait for perfect foundations before experimenting; how Nao structures its analytics agent; and how data professionals can use AI to become more visible and valuable rather than automate themselves out of the process.

Find Nao at:- https://getnao.io- https://github.com/getnao/nao- https://www.linkedin.com/in/claire-gouze/- https://www.linkedin.com/in/christopheblefari/

00:00 From theoretical physics to building Tasman05:14 The three skills of a great data consultant13:03 Hiring data talent in the AI era21:11 Why companies should not hire a data superstar28:45 What an AI-native data team really looks like37:18 Context layers, product thinking and the modern data stack46:42 Data teams as internal consultancies54:49 Where to find ThomasIn this episode of The Data Hustle, we speak with Thomas in ’t Veld, CEO and co-founder of Tasman Analytics, about what makes a genuinely effective data consultant and how companies should build lean, capable data teams. Thomas explains why the strongest consultants combine technical depth, business understanding and communication skills—and why the best specialists still need a broad, T-shaped understanding of the wider data landscape.We also discuss how AI is changing recruitment, consulting and analytics delivery; what it really means to operate an AI-native data team; why strong data modelling and human-curated semantic layers still matter; and how small teams can use modern tools to produce the output that once required much larger departments.The conversation covers product thinking, context layers, data governance, stakeholder communication, consultancy career paths and the importance of focusing relentlessly on the business problems that actually move the needle.Find Thomas and Tasman at:Tasman Analytics: https://tasman.aiThomas in ’t Veld: https://www.linkedin.com/in/thomasintveld/

In this episode of The Data Hustle, we sit down with Noel Gomez, co-founder of DataCoves, to discuss what it really takes to build and operate a modern data platform at scale.Having dbt, Apache Airflow, Snowflake and a collection of open-source tools does not automatically give you a platform. Noel explains why the difficult part is often the “glue” between those tools: governance, security, CI/CD, conventions, ownership and repeatable ways of working.We also explore what happens when AI dramatically accelerates data development. A team might go from 500 to 5,000 dbt models—but did it actually need those models? And who validates the output when AI is capable of being confidently wrong?Noel shares why AI should be treated as an efficiency tool rather than a replacement for human judgment, what junior data professionals should learn to remain valuable, and why curiosity, fundamentals and the ability to challenge AI matter more than simply generating more code.We discuss:

• Why a collection of tools is not the same as a data platform
• The challenge of running dbt and Airflow at enterprise scale
• Why AI makes governance and conventions more important
• How AI can accelerate technical debt
• The risks of trusting AI-generated work without validation
• What happens to junior engineers when companies automate entry-level work
• Why data professionals need to understand the “why,” not only the implementation
• Why strong teams go slower initially so they can move faster over time
• How saying “no” prevents unnecessary products and features from becoming permanent liabilities

Find Noel at:

• / noelgomez
• https://datacoves.com/

In epsiode #7 of The Data Hustle, Tony sits down with Kingsley Hall to talk about how AI is changing sales, cybersecurity, and the future of work.We get into the real shift happening in sales right now: why AI is not just another productivity tool, how top sellers are using it to multiply output, and why the future seller may need to become more technical, more curious, and more human at the same time.Kingsley shares how enterprise sales teams are adopting AI, what technical people often misunderstand when moving into sales, why selling pain relief beats selling features, and how cybersecurity becomes even more important as companies rush toward AI-first operating models.We also talk about the rise of solopreneurs, AI agent armies, the UAE’s future-first business culture, and why storytelling, charisma, curiosity, and trust may become the real differentiators in a world full of AI-generated slop.

In this episode, I’m joined by Christopher Gambill, a data strategy and engineering leader with more than 25 years of experience helping organizations turn data into real business impact.We talk about what it actually takes to unlock your data potential — not just by learning tools, syntax, or the latest buzzwords, but by understanding how data work connects to business value.Chris shares his journey from customer service and legacy on-prem systems to data leadership, consulting, architecture, and coaching. We also dive into the future of data engineering in the age of AI, how aspiring data engineers can stand out, why “strategy over syntax” matters, how to build portfolio projects hiring managers actually care about, and how senior data professionals can move from implementation to trusted advisor.Whether you’re trying to break into data engineering, grow into a senior role, or become more valuable as a consultant, this conversation is packed with practical advice.

In this episode, we speak with Zharko Cekovski about rescuing broken data projects, working with clients when everything is on fire, and what it actually takes to get companies unstuck.

Zharko shares stories from data engineering and consulting projects where the real problems were not just technical, but organizational: unclear priorities, weak communication, missing documentation, bad deployment practices, and platforms nobody knew how to maintain.

We also talk about remote consulting, pricing, Git as an underrated collaboration skill, cultural communication differences, “death by DevOps,” when automation helps versus when it becomes overkill, and how AI can support engineering work without replacing real judgment.A practical conversation for data consultants, data engineers, analytics engineers, and anyone who has ever walked into a messy project and had to turn it into something maintainable.

You can find Zharko at:https://www.linkedin.com/in/zharko-cekovski/