The sobering state? As of August 2025, over 80% of paid media data supplied by Australian customers is uploaded manually, not through dedicated API-driven automations. This isn’t a storm in a tea cup; this lack of integration is a glaring symptom of deeper data maturity issues across the industry.
Your Litmus Test: The Data Warehouse
Automation itself is not the destination, but it is a powerful diagnostic tool. Functioning automation reveals an undeniable proficiency and understanding for structured data. But building an enterprise-level data warehouse is difficult — really REALLY difficult. It requires significant time, research, and wide-scale engagement, but a functioning warehouse is the clear mark of a truly data-first organisation. One dedicated to improving performance from a verified, insights-led basis.
Critically, it is also an indicator of companies and agencies that value trust and transparency. It’s cynical, but the easiest way to avoid scrutiny is to make work so complicated it becomes impossible to interrogate. Conversely, data organised to the degree that automations are viable indicates everything is laid bare. No bespoke reporting templates, no specific filtrations, just standardised datasets ready for analysis at a moment’s notice.
The Obvious, Costly Ceiling
The worst fallout of manual data reliance is a clear and obvious ceiling. Any long-term data analysis project that fails to move toward automation inherently implies a continued dependence on people and bloated systems. This introduces escalating costs, more mistakes, and longer waiting periods. To achieve scalability and match the pace of data-led leaders, infrastructure and automation are not optional; they are fundamental.
Legacy, Speed, and the Mobius Loop
There are understandable reasons for the current position. Many foundational businesses are wrestling with decades of data across multiple legacy systems, an overwhelming challenge akin to untangling your old wired Apple headphones that have formed a Mobius loop in your pocket. Plus pressure for speed and constant agility means foundational work is often deprioritised. While Agile is the buzzword, constant iteration can hold back vital groundwork that acts as your future springboard. All this is then compounded by a drain of specialised engineering talent in Australia.
The Cost-Benefit Tipping Point
The landscape is shifting. Traditional holding companies are being aggressively challenged by a rising wave of independent agencies, all starting fresh with modern tech stacks plus a combination of seasoned and revitalised leadership. Globally, brands are rapidly adopting SAAS-based MMM tools that are utterly reliant on sourcing data effectively, efficiently, and at speed. These sophisticated global players are now measuring the numerical impact of their inefficiencies. As more of the Australian market turns to these tools, data literacy and infrastructure will transform from a ‘bonus’ into an expectation, and then an absolute necessity.
The Path Forward
The best way to start is simply to start. Prioritise solutions that lend themselves to automations and data lake integrations. Conduct thorough research into large-scale data warehousing tools and partners to upgrade your data hygiene. Most critically, begin the urgent search for specialised engineering talents: staff who will approach this with a focus on data management improvements, rather than purely bolt-on solutions to broken or bloated legacy systems.
Your data is a core product; it deserves to be placed with care on a dedicated shelf, not strewn across the ground with your laundry. No business can truly claim to be a data-led organisation while simultaneously uploading 40 CSVs a month. A mental and physical shift is needed now, before you are left decisively behind by the bolder, more dedicated data-led companies.
