Giving up on the minimum viable data stack
Exploring why universal advice on startup data stacks is challenging, and the importance of context-specific decisions in data infrastructure.
Data Engineering
Your first Data-to-AI hire: Run a lovable process
Video and key points from the second part of a webinar on a startup’s first data hire, covering tips for defining the role and running the process.
Learn about Dataland to avoid expensive hiring mistakes
Video and key points from the first part of a webinar on a startup’s first data hire, covering data & AI definitions and high-level recommendations.
Plumbing, Decisions, and Automation: De-hyping Data & AI
Three essential questions to understand where an organisation stands when it comes to Data & AI (with zero hype).
My experience as a Data Tech Lead with Work on Climate
The story of how I joined Work on Climate as a volunteer and became its data tech lead, with lessons applied to consulting & fractional work.
The data engineering lifecycle is not going anywhere
My key takeaways from reading Fundamentals of Data Engineering by Joe Reis and Matt Housley.
Building your startup's minimum viable data stack
First post in a series on building a minimum viable data stack for startups, introducing key definitions, components, and considerations.
Substance over titles: Your first data hire may be a data scientist
Advice for hiring a startup’s first data person: match skills to business needs, consider contractors, and get help from data people.
Supporting volunteer monitoring of marine biodiversity with modern web and data tools
Summarising the work Uri Seroussi and I did to improve Reef Life Survey’s Reef Species of the World app.
You don't need a proprietary API for static maps
For many use cases, libraries like cartopy are better than the likes of Mapbox and Google Maps.
Lessons from reluctant data engineering
Video and summary of a talk I gave at DataEngBytes Brisbane on what I learned from doing data engineering as part of every data science role I had.