Top 10 challenges of big data analytics in healthcare and how to overcome them

Top 10 challenges of big data analytics in healthcare

TL;DR
This article breaks down the top 10 challenges facing healthcare providers when implementing big data analytics — including data silos, quality, security, and adoption. With examples from PHNs, hospitals, and services across Australia, it offers practical solutions and real-world strategies to help teams move from fragmented reporting to actionable insight.
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Big data analytics in healthcare has become a critical capability, not just a nice-to-have. From improving patient outcomes to optimising resource use and tracking funding impacts, organisations across the healthcare system are under pressure to turn raw information into clear, actionable insight.
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Yet many teams — especially those in Primary Health Networks (PHNs), hospitals, and aged care providers, still face persistent barriers when implementing analytics solutions. Disconnected systems, limited internal capacity, and compliance challenges continue to slow progress, even where executive support exists.
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In this guide, we explore the top 10 challenges of big data analytics in healthcare, with real-world examples from Australian organisations who are solving these problems in practice.
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We also answer:

• What is data analytics in healthcare?
• What is the role of data analytics in healthcare?
• And what does success look like?
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What is data analytics in healthcare?

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Data analytics in healthcare refers to the process of collecting, transforming, and analysing health-related data to support better decisions. It can be applied at multiple levels, from patient-level clinical care to organisational performance and national policy.
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Healthcare analytics typically includes:

• Clinical data (e.g. pathology, imaging, medication, screening)
• Operational data (e.g. admissions, activity-based funding, bed capacity)
• Program data (e.g. mental health, chronic disease, aged care services)
• Population-level insights (e.g. regional trends, disease burden, access to care)
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This data is analysed using tools like Qlik Cloud Analytics, Power BI, or custom web platforms. Dashboards, visualisations, and reports help users make sense of complex information and act on it.
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The role of data analytics in healthcare
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So what is the role of data analytics in healthcare?The short answer: to make better, faster, evidence-based decisions that improve health outcomes and operational performance.

For Primary Health Networks (PHNs), that might mean:

• Understanding regional population needs to guide commissioning
• Tracking quality improvement at general practice level (e.g. PIP QI)
• Reporting back to government and funders

For a deeper look at how PHNs are automating PIP QI reporting and using platforms like Qlik Cloud and POLAR, read how PHNs streamline their reporting workflow.

For hospitals, it could involve:

• Monitoring performance against NWAU targets (linked to funding)
• Managing patient flow and staffing levels
• Tracking critical quality and safety indicators

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Making analytics useful at the local level means more than just building dashboards, it requires tailoring data to decision-makers across PHNs and hospitals.

At a system level, analytics also helps identify gaps in access, uncover patterns in disease management, and inform public health strategies.

What success looks like:
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A 2025 global survey by McKinsey found that CFOs who report sustained transformation success share a common approach:

• Prioritise people over process – 70% say securing organisational buy-in is the biggest barrier
• Assemble cross-functional teams early
• Define clear metrics to track progress
• Invest in modern IT infrastructure – organisations with outdated systems were twice as likely to fail

These findings closely align with Notitia’s experience. Long-term success isn’t just about buying the right platform, it’s about building capability, embedding collaboration, and supporting people through change.

In Australia, that means real impact for patients, especially in regional and underserved communities where health equity is a top priority.
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Top 10 challenges of big data analytics in healthcare
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Now let’s dive into the challenges — and how to solve them.
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1. Data privacy and security

Healthcare data is among the most sensitive. That makes privacy, access control, and cybersecurity critical — and complex.
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Why it matters:
Breaches of health data erode public trust, increase legal exposure, and put patients at risk.
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In Australia:

• The Privacy Act 1988 and Australian Privacy Principles apply
• Health organisations must comply with ISO 27001, NESAF, and state-specific obligations
• New mandatory data breach notifications make security a board-level concern
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Real-world example:
While not a healthcare incident, the 2018 Australian National University cyberattack exposed personal staff information and highlighted the risks of outdated infrastructure and fragmented systems. The breach was not caused by human error but by vulnerabilities in legacy networks — a reminder that even upgraded systems are still exposed without comprehensive, proactive cybersecurity practices (source).
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Solution:
Use platforms with built-in encryption, role-based access, and audit logging. Conduct regular security audits and include privacy-by-design in your data projects. Notitia supports clients in aligning projects with ISO 27001, NESAF, and Australian privacy laws to ensure sensitive health data remains protected.
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2. Data silos and interoperability

Disparate systems such as GP software, hospital EMRs, CRMs, Excel files, often don’t talk to each other. This creates blind spots.
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Why it matters:
Disconnected data leads to duplicated effort, inconsistent reports, and delayed decision-making.
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Real-world policy response:
The Australian Digital Health Agency (ADHA) released the National Healthcare Interoperability Plan in 2023, aiming to enable better data sharing across care settings.
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Solution:
Move towards platforms that integrate multiple data sources such as POLAR and Qlik Cloud Analytics. Use standards like Fast Healthcare Interoperability Resources (FHIR) to enable safe, structured sharing.
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Data quality and accuracy

Poor data quality leads to mistrust — and poor decisions. It’s one of the most cited barriers by Australian Primary Health Networks (PHNs).
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Common issues:

• Incomplete or outdated data
• Inconsistent field names or logic
• Manual entry errors
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AIHW has warned that inconsistent data practices across states limit the reliability of national health metrics (AIHW).
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Solution:
Build validation rules and cleaning steps into data pipelines. Provide data entry training for frontline users. Using Qlik Cloud Analytics, dashboards can be configured to flag missing values or anomalies.
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3. Scalability and performance

Your dashboard might work for 1,000 records — but not 1 million. And as you add more users and programs, things slow down.
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Why it matters:
If it takes minutes to load a report, users won’t use it. If it crashes mid-meeting, you lose trust.
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Solution:
Cloud platforms like Qlik Cloud Analytics are built to scale. When Gippsland Health Alliance (GHA) migrated 500 apps to the cloud, they ensured regional and metro services alike could access scalable, high-performance dashboards — regardless of size.

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4. Real-time data processing

Waiting for reports means you’re always acting on the past. But in emergency care, service planning, or outbreak response, that’s not good enough.

During COVID-19, real-time dashboards helped Victorian health services track vaccine uptake and ICU capacity in near real time.
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Solution:
Set up automated data pipelines that run daily or hourly, rather than relying on manual uploads. Outcome Health’s POLAR platform supports real-time data discovery for PHNs and practices.
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5. Cost and resource management

Budgets are tight. Talent is scarce. Yet many analytics projects require specialist skills and long build times.
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Solution:
Notitia works with PHNs and hospitals to deliver Analytics as a Managed Service reducing overhead while still lifting capability. Cloud licensing models help you scale access without huge upfront costs.
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6. Ethical and legal considerations

Beyond compliance, teams must consider ethical use of data. Who benefits? Who’s excluded? Who gets to decide?
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Solution:
Work with stakeholders to co-design analytics projects. The Centre for Big Data Research in Health at UNSW provides guidance on ethical frameworks for healthcare analytics.
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7. User Adoption and Training

A great dashboard is useless if people don’t know how to use it — or don’t trust the data.

The Australasian Institute of Digital Health (AIDH) found in 2025 that:

• Over 50% of allied health staff had no formal digital health training
• 56% didn’t know how to use My Health Record effectively
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Solution:
Invest in training — not just once, but ongoing. Notitia runs tailored data literacy sessions for all users, from executive to admin staff.

Here’s how two very different organisations built meaningful data literacy with Notitia’s support.
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West Gippsland Healthcare Group (WGHG)

WGHG partnered with Notitia to build dashboards aligned with their National Weighted Activity Unit (NWAU) targets — a critical funding stream for Victorian hospitals. But success wasn’t just about reports. It was about adoption.

Notitia delivered hands-on data literacy training for executives, finance staff and operational leaders, ensuring each group could confidently access and act on the information presented. The sessions focused on reading visualisations, asking the right questions, and understanding how reporting linked to funding performance and care delivery.

“At our fingertips, we have the ability to drill down to information levels that we’ve never had before. Quick access to critical data has resulted in improved healthcare outcomes and operational efficiency.” — Robyn Weeks, Activity Based Funding Manager, WGHG
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Laing O’Rourke — North East Link

As part of Victoria’s Big Build, Laing O’Rourke transitioned to Qlik Cloud Analytics to support project reporting on the North East Link. Notitia provided targeted, role-specific training to embed analytics into the organisation’s daily decision-making.

Workshops helped engineering and commercial teams:

• Navigate complex reporting hierarchies to compare performance against key milestones
• Use departmental dashboards to answer real operational questions
• Interpret cost forecasts and identify project risks in real time

The goal wasn’t just knowledge transfer — it was capability building. Laing O’Rourke now maintains their Qlik Cloud environment internally, with confidence and consistency.
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8. How to build data literacy in your organisation

If you want your analytics projects to stick, you need a people-first plan. Here’s what we recommend:

• Assess Current Capabilities: Start with a skills audit. Where are the gaps? Who needs support?
• Target the Training: Focus on the tools and data your people use every day. Don’t start with theory — start with what matters.
• Embed in Culture: Reinforce data as a decision-making tool, not just a reporting requirement.
• Use Trusted Partners: Organisations like Notitia work with you to design practical, tailored training that actually meets the needs of your teams.

Data literacy doesn’t mean everyone needs to be an analyst. But it does mean everyone should feel confident making decisions based on clear, accurate information.
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9. Integration with existing workflows

Analytics tools shouldn’t feel “extra”. They should be built into how teams already work.

Example:
South Western Sydney PHN integrated dashboards with Microsoft Teams so staff could collaborate around insights — not just read static reports.

Solution:
Co-design your analytics products with real users. Understand their tasks, time pressures, and habits — and meet them there.
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10. Keeping up with technology

The pace of change is rapid. Many health services still use outdated, on-premise infrastructure while peers are exploring AI and predictive modelling.

This 2025 McKinsey survey found that CFOs at companies with failed transformations were twice as likely to cite inadequate IT infrastructure as a barrier. The most successful leaders focused on modernising platforms early and building internal capability.

Solution:
Partner with trusted vendors and consultants to stay ahead. Use modular systems that allow upgrades without complete rebuilds. Invest in people, not just platforms.
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Case studies: Real-world impact

• WGHG (Hospital): Delivered co-designed dashboards in 6 weeks to track NWAU-linked funding metrics.
• SWSPHN (PHN): Migrated to Qlik Cloud in 10 days, improving reporting across 200+ commissioned services.
• GHA (Regional): Enabled 17 regional health services to securely access analytics tailored to their size and needs.

When data analytics is embedded into operations, healthcare organisations see:

• Faster, more confident decision-making
• Less duplication, more automation
• Higher engagement from staff and providers
• Better compliance with PIP QI and NWAU metrics
• More equitable access to insights, regardless of location or technical skill

Big data analytics in healthcare isn’t easy — but it’s necessary.

By addressing the 10 challenges outlined above, PHNs, hospitals and service providers can shift from reactive reporting to proactive, data-informed action.

Need support on your analytics journey? Book an exploration call with Notitia.