Navigating Australia's evolving data landscape under new compliance pressures

AU Financial Review

3 hours ago

In addition, fragmented systems create blind spots. This not only undermines compliance, but also makes it harder to detect security threats, innovate with confidence, or respond quickly to operational disruptions.
'Australian organisations are flooded with data. Data runs through every system [and] decision,' says Craig Bates, senior vice-president and general manager of Asia Pacific at Splunk, a leader in cybersecurity and observability. 'But the reality is, they're drowning in data but starving for insights.'
Aurélie Jacquet, chair of the Standards Australia committee representing Australia at the International Standards on Artificial Intelligence, says many of these issues stem from a lack of continuous quality oversight.
'In the age of AI, ongoing data-quality management is inescapable,' she says. 'It is key for organisations [to] demonstrate how they manage data quality in a safe and responsible manner.'
Governance gaps and regulatory pressure
Poor enforcement of basic data policies remains a major vulnerability. According to the same Splunk report, many organisations still struggle to enforce key controls like where data should be stored, who should have access, and how long it should be retained.
Bates says the pressure isn't just coming from local regulators.
'Global policies like the EU's General Data Protection Regulation [GDPR] are also shaping expectations, particularly for multinationals and any business working with customer data or deploying AI models.'
Jacquet adds that regulatory pressure is accelerating a needed shift, pushing organisations to take a more deliberate, end-to-end approach to operational risk management, especially as AI becomes more deeply embedded in business operations.
'[Organisations need to be asking:] What is good enough data quality that is appropriate to build data products or train AI systems safely and responsibly?'
'What are our data blind spots? How can we address them to ensure we deliver quality products and services?'
Bates adds that leading organisations are finding ways to balance control and agility. 'They've put the right guardrails in place – and this includes clear policies, data quality standards, and visibility across environments.'
Rising costs, slower decisions
'Today's biggest challenges – service disruptions, security incidents, flawed AI outputs – are all symptoms of poor data management,' says Bates. Disjointed data environments are costing Australian organisations in more ways than they realise.
According to Splunk's report, 88 per cent of ANZ respondents say their data-management spend has increased in the past year.
Bates says the cost burden goes beyond dollars and cents; it's also about speed and resilience.
'Compliance still matters, but it's not the full picture,' he says. 'More organisations are recognising that if they can't access reliable data quickly and securely, they're unable to respond effectively to threats, disruptions or even to change.'
That's because fragmented systems obscure critical signals and force teams to work in silos. This slows down detection, delays recovery efforts, and makes it harder to launch or scale new initiatives. Practices like data federation — enabling organisations to access and analyse data without migrating — offers a path forward. Despite its benefits, only 20 per cent of ANZ respondents say they've fully implemented such capabilities. Those who have are seeing measurable gains including faster access to data. In fact, Australian organisations with a federated strategy have saved an average of AUD $1.9 million.
Data governance in daily operations
Clearly, navigating these fault lines successfully isn't just about technology. It's also about strategy and discipline.
The organisations making real progress have moved beyond surface-level fixes. Governance is meaningfully embedded into daily operations.
Visibility and data quality are also central. They prioritise trusted access to support confident decisions – and faster, more resilient responses.
They also invest in modern data management practices like data reuse and tiering: global organisations that employ reuse are 46 per cent less likely to face hurdles with high data volumes, while 50 per cent of those using tiering report reduced storage costs.
'[They've] made a clear decision to get their data house in order,' says Bates. 'Teams can get the right data at the right time, without delays or second-guessing.'
Jacquet says positive progress is underpinned by intentionality and rigour.
'The more mature organisations have developed data-quality models,' she says. 'When they create or acquire datasets, these organisations set data-quality goals, data requirements and measurements that are specific to their use case.'
From compliance to capability
For leading organisations, compliance is only part of the puzzle.
'Make data your priority,' says Bates. 'If there's one thing to get right, it's building a trusted, usable data foundation that supports how your business actually runs. Without that, you can't scale AI responsibly, respond to data breaches quickly, or recover from downtime with confidence' he adds.
'Start small if you need to. But start.'