What Australian startup accelerators can do to prove their worth

When Australian startup fever took hold in the mid-2010s, state governments used taxpayer money to support high-growth technology businesses with the intent to leverage the benefits these businesses could deliver to future-proof their economies.

As a result, sophisticated industry scaffolding began to emerge in support of the fearless few willing to dive in and try their hand at the elusive treasures entrepreneurship could bring.

Respected accelerators and incubators such as Startmate and Antler, as well as Australia’s key universities, came to the party with programs designed to rally investors, foster connections, nurture growth and attract new prospects.

As typical to the dynamic nature of the sector, no linear path could be mapped for an individual startup, so organisations set soft metric benchmarks to record success, like the number of program participants, attendance at pitch nights, and the value of funds raised for their cohorts.

Fast forward to today and the dust has truly settled on the innovation sector. Investor funding has tightened worldwide and governments are pulling back on much-needed funding that had been dedicated to support the growth of startups for over a decade.

While I was obviously not in the room for the inevitable discussions with the Government, I could imagine industry leaders pleaded their case for continued support.

Sadly, soft metrics against notorious startup failure rates might not have been enough to reflect the important work that ecosystem enablers do to support growth in the marketplace.

Australia needs startup enablers

As an entrepreneurship educator and cofounder of The Biz Lab, I have worked with many incubators and accelerators around the world.

Dr Yong Hsin Ning

The work these organisations do to elevate tech startups is immense – forging connections, building communities, guiding growth, and championing tech companies in an ever-shifting marketplace.

Is there room for improvement? Perhaps, but without quality data insights it is difficult to identify key areas that can make a difference.

A number of studies have recently emerged that compare incubated startups to non-incubated startups, with the latter sometimes faring better in terms of performance and growth.

These studies are not helping the enabler’s case, as they undermine the valuable work being done and the significant achievements of many in the sector.

Again, however, it is difficult to fight a strong case to the contrary or improve services to the startups without substantiated data insights.

Enter AI

Artificial intelligence, predictive technologies and how we use them to improve practices and processes in business rely heavily on the quality of available data.

Quite simply, if there is no quality data input, data insights will be limited and the basis for decision-making remains anecdotal.

Those in the innovation sector have long understood the need for quality data and systematic data building.

However, as AI becomes more prevalent, the true value of data is now becoming critical.

Putting startups at the heart

Quality insights are crucial for any startup to find its way to become a successful business enterprise. This is particularly the case at the beginning of a startup journey.

Our data shows that there is often a significant misalignment between early-stage aspirations and their business reality. If this mismatch could be identified early through insights and used to guide the startup along a different path or focus, the likelihood of success may increase.

The good news is that startup enablers can capture such quality data and provide guidance to startups through their services.

A digital suite of data-building facilitation tools such as those developed by The Biz Lab can help each startup to identify misalignment and simulate financial scenarios worthy of an investor’s attention, while also drawing critical information for their incubator or accelerator.

Dispelling implementation overwhelm

The prospect of a new systemised process – no matter how tempting the outputs – can create a sense of dread, but it does not have to be that way.

Systemised data building, resulting in a powerful store of data can be easily incorporated into automated processes within cohort communications, such as welcome emails, onboarding information, timely check-ins and program deliverables.

Insights from these simple actions can be used to:

Screen startups
Target interventions and supports
Pinpoint effective practices
Demonstrate the effectiveness of programs
Lay a foundation for AI and predictive practices (AI needs quality data)
Match mentors and investors

More significantly, however, the data captured along the way can demonstrate each organisation’s performance with success metrics and trends from longitudinal data and quality insights.

Now that has got to be more compelling than attendance numbers at a pitch night.