Commercial analytics and data analysis insights.

Replacing a two-day monthly Excel report with automated Power BI dashboards cut the manual work, but the bigger win was how much less there was to maintain.

GA4 shows what happened and Clarity shows how people behaved. Neither can tell you whether a change caused a better result, which is the gap SplitPea was built to fill.

My partner and I kept texting shopping lists back and forth. When shared-list apps wanted a subscription, I built Pantrify instead.

Keeping a handful of pages consistent is manageable. Keeping 58 landing pages, 17 products and 104 blog articles sounding like the same brand is a different problem.

AI hasn’t made me an expert in every discipline. It has made me competent enough across several of them to keep building, learning and moving.

Candidates use AI to tailor applications while employers use AI to screen them. Everyone is acting rationally, and the system is getting worse.

I built synthetic analytics tasks with hidden data traps to test whether AI could spot bad joins, missing data, misleading uplift and changing metric definitions before making confident recommendations.

At Bakers Delight, bread campaigns produced limited incremental growth, while pizza and scone promotions changed transaction size and delivered stronger uplift.

AI can speed up analysis, coding and exploration, but a plausible answer is not automatically a trustworthy one. This is the technical validation process I use before relying on AI-assisted analytical work.

Sales falling is a result, not an explanation. Start with distribution, rate of sale, price, product mix and the wider category.

Before choosing a metric or chart, name the decision the dashboard needs to improve.

A granular projection produced an estimate of 52 million Australians. Rolling the result back up to the national total exposed the problem.

AI can write code, test hypotheses and speed up analysis, but it cannot see all the domain knowledge, internal context, relationships and organisational constraints that determine whether a recommendation makes sense.

Repeated questions, changing inputs, and valuable context are signs that an analysis may deserve an interface.

Before checking nulls and duplicates, make sure the metric, population, and time period mean what you think they mean.

Two years of City of Melbourne pedestrian-sensor data show foot traffic recovering unevenly: strongest at the residential fringe, after dark, and around the events calendar, while cafe and restaurant seating still clusters around the old daytime pattern.

The third city in the series. Brisbane's demographic profile is structurally different from the southern capitals, and the data shows a city in transition.

The second city in the multi-city census series. Sydney's migration footprint is different from Melbourne's in ways the data makes clear - from China's sustained lead in birthplace to Arabic as the standout language.

This is the first in a multi-city Census series. Melbourne is diversifying quickly: India has overtaken England as the largest overseas-born population, while Christianity remained narrowly ahead of secular identification in 2021 and the linear projection places secular identification ahead by 2026.

Before I build a dashboard, automate a report, or start analysing trends, I try to answer one question first: can this dataset actually be trusted? Here's what I check before relying on data for decisions.

Australian census geography changes over time, which makes suburb and LGA trend analysis much harder than it looks. Here's how I used SA1 data, ABS allocation files, and correspondence files to build a consistent 2011–2021 time series and a 2026 projection.

Since 2005, Lego has tripled the number of sets it launches each year, while the catalogue has shifted towards larger, higher-priced adult and licensed products. Brickset data makes the range change visible, although it cannot show which shift contributed most to revenue growth.

A core product range declined 48% in one month. No single dataset could explain why. Here’s how combining scan data, consumer panels and custom research identified the root cause and helped return the range to 18% growth the following year.