Insights
Product, design, AI, and engineering perspectives from our team.
Composable Data Stack: Reverse ETL and CDPs Explained
Growing companies often outgrow dashboards before they realise it — the business wants warehouse data acting inside the CRM, ad platforms and support tools, not just summarised in a report. This guide compares reverse ETL and customer data platforms as activation options within a composable data stack.
Measuring Data Engineering Team Productivity: A Framework
Most engineering teams track DORA metrics for software delivery, but data engineering needs a different lens. This article sets out a practical framework covering throughput, pipeline reliability, and stakeholder satisfaction for growing data functions.
GS1 Standards and AI-Powered Supply Chain Visibility
GS1 barcoding and traceability standards give Australian logistics and manufacturing businesses a common language for identifying products and tracking custody events. This article explains how those standards work and where AI-powered visibility tools genuinely add value on top of them — and where they can't compensate for weak data foundations.
From Spreadsheets to Data Infrastructure: A Migration Path
Many growing Australian companies still run finance and operations reporting on spreadsheets long after they've outgrown them. This guide covers the signs it's time to move, what a first data infrastructure build should include, and how to sequence the migration without disrupting month-end.
Data Clean Rooms for Regulated Australian Industries
Data clean rooms let regulated Australian organisations run joint analysis with partners without exposing raw records. Here's how the architecture works, and when it's worth the investment over a simpler data-sharing agreement.
Data Product Management: Treating Datasets Like Products
Most internal datasets have no owner, no documented SLA, and no versioning discipline — which is why downstream teams stop trusting them and start duplicating them. This piece lays out a lightweight framework for treating a handful of critical datasets as products, without committing to a full data mesh rebuild.
Data Monetisation: Turning Internal Data Into Revenue
A practical look at how growing Australian companies can turn internal data into an external revenue stream — covering commercial models, technical foundations, and the governance considerations that come first.
Change Data Capture for AI-Ready Data Pipelines
Change data capture (CDC) keeps analytics, search, and AI features in sync with source systems in near real time — but it's not free. This explainer covers how CDC works, where it fits in an AI-ready data stack, and when the operational overhead is actually worth it for an Australian engineering team.
How to Build a Data Science Function From Scratch
A practical guide to standing up a data science function from zero — who your first hire should report to, whether to start with a data scientist or analytics engineer, and how to sequence early wins before scaling the team.