Cultural changes as missing enablers to realising value from the data already in your organisation
Government departments and agencies across Australia hold vast quantities of data related to the infrastructure assets they plan, deliver, and manage. Roads, rail, water networks, energy systems, and social infrastructure all generate enormous volumes of data throughout the lifecycle of their assets.
Despite this abundance, the potential value of this data remains unrealised. In many instances, asset data is poorly managed, inconsistently structured, or difficult to access. It sits across multiple systems, design models, asset management platforms, maintenance records, inspection reports, sensor outputs, GIS layers, and historical performance datasets. Too often, it is used only to satisfy compliance requirements rather than to inform real decisions – for example in reducing construction program timeframes through clash identification or articulating heavy vehicle movements for site teams to improve hazard awareness. The issue is not a lack of data. It is a lack of connection, trust, and usability.
Addressing these issues requires key cultural changes within an organisation. This is exemplified through policy such as the InfrastructureNSW IDD policy which frames that, “effective change management and capability building is fundamental to achieving lasting change”. Through this paper, we outline some key cultural changes and link these to specific technical improvements in data management. Through this we highlight the strategic advantage that the right cultural settings around data can deliver.
Andrew Curthoys
Principal Consultant - Consulting & Advisory
The Opportunity: From Data-Rich to Insight-Driven
Most organisations already possess the data they need to make better decisions. The challenge and opportunity is to unlock the latent value within it to improve the risk arc and operational efficiencies. Better use of existing asset data starts with a fundamental shift in how organisations think about data: not as a by-product of delivery, but as a strategic asset that underpins performance, enabling the workforce to be more efficient and effective. This ensures that investment decisions reflect a data driven approach, and risk is better managed and understood.
Cultural change as the critical enabler
There are four technical changes that are fundamental to this change. While technology and systems are important, however, they are not the primary barrier. The real challenge is cultural.
Unlocking the value of asset data requires a “data-first” mindset and clarity on the value of data across different parts of an organisation. This requires an organisational culture that values evidence-based decision-making and holds teams accountable for data quality and use.
In simple terms, this demands some key shifts in behaviour:
- From siloed ownership to shared responsibility
- From compliance-driven reporting to insight-driven observations and action
- From short-term delivery focus to whole-of-life thinking, enabling the workforce and creating efficiencies
- From responding to a crisis to acting on insights and performance approach
Through this paper, we frame technical changes and relate these to the cultural changes that will enable these. Without this cultural change, even the best systems and tools will fail to deliver meaningful outcomes.
1. Establish a Single point of truth, with trusted Asset Information
A common challenge across government agencies is data fragmentation. Different teams manage different systems, each holding partial views of the same asset.
Engineering teams rely on design models. Operations teams depend on asset registers. Maintenance teams use work management systems. Spatial teams manage GIS platforms. Project teams generate new data during delivery; but often in formats that are not easily accessed or reused. The result is duplication, inconsistency, and a lack of confidence in the data, irrespective of where it came from.
Creating a federated data environment – or a common data environment – allows organisations to link these systems and establish a single, trusted view of asset information. This does not necessarily mean replacing existing systems but rather connecting them in a way that enables interoperability.
Building a central data source within a Common Data Environment needs broad and shared responsibility. Teams need to be responsible for the quality of data that they create, review and use. They also need to be responsible for the quality of data they provide to other teams. The key example here is that in capital project delivery, asset datasets are built to streamline handover and commissioning.
When done well, this provides a full lifecycle view of assets; from planning and design through to operations, renewal and disposal, each element supporting better coordination and more informed decision-making.
2. Shift from Data Collection to Decision Support (insight-driven action)
For many organisations, data collection has become an end in itself. Large volumes of data are gathered, but relatively little is analysed to support proactive decision-making. To improve across all the lifecycle facets, this needs to change.
Existing datasets can reveal powerful insights into asset condition, failure patterns, maintenance demand, lifecycle costs, and operational risk. When combined and analysed effectively, they allow organisations to move from reactive to predictive approaches.
Key examples of this include:
- Identifying assets that are most likely to fail before they do
- Prioritising maintenance based on risk rather than routine schedules
- Targeting investment where it will have the greatest impact
- Understanding whole-of-life cost implications of design decisions, whole-of-life performance and environmental impact, environmental factors, and carbon requirements.
Each of these requires individuals to interrogate and prioritise specific data within their domain. Undifferentiated datasets have limited value and it is critical for individuals to take a critical approach to the structure and contextualise data. This may seem like a challenging ask but digitial tools including generative AI can be of use here.
3. Embed Governance and Data Standards (whole-of-life thinking)
One of the key reasons data remains under-utilised is inconsistency. Data generated during planning, design, and construction is often delivered in formats that are not aligned with operational needs. Without clear governance and standards, organisations perpetuate a cycle where data is repeatedly recreated, pushed around, cleaned, or simply ignored.
Breaking this cycle to improve workflow efficiency and ways of working requires:
- Defined data standards and schemas
- Clear information requirements at each project stage
- Machine-readable, structured data deliverables
- Accountability for data quality and completeness
- Cultural change valuing the data flow within an organisation
- Evaluation of workflows to assess efficiencies
This requires a whole-of-lifecycle structure to data. Furthermore, it is not just a technical issue but a contractual and organisational one. Embedding data requirements into procurement and delivery processes ensures that information created during projects becomes usable operational intelligence, not static documentation as-a-deliverable.
4. Make Data Visible and Actionable (acting on insights and performance)
Even high-quality, well-structured data has limited value if it is not accessible to the people who need it. Making data visible through dashboards, digital twins, and geospatial visualisations gives engineers, planners, spatial analysts, and executives the opportunity to better understand asset performance and emerging risks in real time.
This moves the conversations from opinion-based to evidence-based. Instead of asking “What do we think is happening?” organisations can ask “What does the data tell us is happening and what steps to we need take?”
But this requires the culture in the organisation to trust the data, value the data and utilise the data.
This shift in thinking and approach leads to a reduction in operational waste, reduces risk, and improves cost overruns. The ability to visualise network condition, performance trends, and future risk scenarios is particularly powerful for articulating investment decisions and communicating with stakeholders, particularly within government, including Treasury and key decision makers.
From Storage to Strategic Advantage
The infrastructure sector does not need more data. It needs to make better use of the data it already has. It needs changes in behaviour to ensure data is structured and accessible, providing context to deliver more value.
By connecting existing datasets, applying analytics, embedding governance, and making data accessible, government organisations can transform how they manage infrastructure assets.
The result is not simply better data – it is better outcomes:
- Improved asset reliability
- Optimised investment decisions
- Reduced lifecycle costs
- Lower long-term risk
In an environment of increasing fiscal pressure and growing asset portfolios, this is not optional; it is essential.
The opportunity is already there. The data already exists.
The question is: Can your organisation make the changes that will deliver this?