Clearer signals, earlier action: Using data to make better decisions for children

Introduction

Every child in Australia deserves the opportunity to thrive. Yet despite decades of investment, inequities in child development persist (Goldfeld et al., 2024; Mitchell Institute, 2025). Inequities are driven by the circumstances in which children live, learn and grow (Commission on Social Determinants of Health, 2008). These inequities are not inevitable; they are the result of unfair and unjust differences caused by preventable social, economic or geographic factors (World Health Organization, 2025). For a high-income country, this is a significant system failure that demands urgent, coordinated action.

Early childhood (from conception to 8 years of age) is a critical period for growth, with rapid brain maturation laying the foundations for lifelong health, development and wellbeing (Woolfenden et al., 2013). When inequities emerge during these years, they generally persist into adulthood, resulting in substantial costs for individuals and society (Heckman & Masterov, 2007; Woolfenden et al., 2013).

Despite significant investment, inequities persist in part because Australia’s early years systems lack a coherent approach to collect, connect and use data across parts of the system – limiting the ability to see need clearly, coordinate responses and act proportionately.

Equity in early childhood is not just a moral imperative – it is a measurable, actionable goal. To achieve equity, we must first be able to see where inequities exist: who is missing out, where there are service gaps and which interventions are making a difference. Data are central to this task – with opportunities to learn from practices in sectors like business (see Box 1). When connected and interpreted with purpose, data can reveal patterns, track change and guide investment.

Despite strong policy momentum to strengthen and better use data to improve early years services and early years partnerships – including the Early Years Strategy (Australian Government Department of Social Services, 2024), the Productivity Commission inquiry ‘A path to universal early childhood education and care’ (Productivity Commission, 2024) and the Entrenched Disadvantage package (Australian Government Department of Social Services, 2023) – Australia still lacks a cohesive early years data logic: a shared model that sets out what data are needed, for what purpose and how data should be used. The result is a system that is data rich, but insight poor (Molloy et al., 2025).

At the Australian 2025 Early Years Policy summit, ‘harness data, evidence and outcomes’ and ‘elevate the voices of children, families and communities’ were identified as top priorities for reform (National Early Years Policy Summit, 2025). This paper responds with a practical proposal: an early years data logic (Figure 1) to guide better data, better decisions and, ultimately, better, more equitable outcomes for children. The data logic is a structured approach to thinking through which types of data can support which purposes. It highlights different layers of data that, when collected and interpreted strategically, can ensure that the right data are collected, interpreted and used to inform the right actions at the right time.

A data logic offers utility to the national data ecosystem, enabling shared goals, coordinated action and system-wide learning. This approach is grounded in radical pragmatism – doing what works, guided by evidence and adapting as knowledge evolves (Gertz & Kharas, 2020; Goldfeld et al., 2024).

This paper draws from work on Restacking the Odds (see Box 2; Restacking the Odds, 2025), including research evidence from five key early years service areas: antenatal care; sustained nurse home visiting; early childhood education and care; parenting programs; and the early years of school. The data logic, therefore, aims to deliver a structured approach to data use, particularly for these types of services, with an additional focus on community-led initiatives (local early years partnerships). We anticipate the data logic would have application for secondary and tertiary services more broadly, and this would be worth testing further.

The data logic is designed to give governments, service providers and local early years partnerships a practical process for generating timely, purposeful and actionable insights – and for making better use of the data they already hold. Unlike existing frameworks or dashboards, which often present static, fragmented or lag measures, the data logic provides a coherent way to layer different types of data, each with a distinct purpose: showing how children are faring, identifying what drives outcomes and tracking whether efforts are working. The data logic addresses key gaps in current approaches, including fragmented reporting, inconsistent stewardship and the absence of a national strategy for sharing and applying data. Crucially, the data logic does not treat data as an endpoint, but as the foundation for evidence that enables better decisions, more effective services and continuous improvement.

Public health is one sector that provides examples of data informing decision making at every level, from the everyday choices of individuals to the national strategy. Families use data to decide when to seek care; doctors use data to diagnose and treat; service providers depend on data for quality improvement; and researchers analyse data to design medicines and test their efficacy. At a population scale, epidemiologists and biostatisticians monitor data to track immunisation coverage, while policymakers interpret data to shape funding and set priorities. Some data serve multiple purposes, while other data are tailored to specific decisions. What matters is that health systems have some sort of logic that connect data to decision needs at every level. Early childhood policy requires the same clarity – without data, decisions remain fragmented, reactive and less effective in meeting the needs of children and families.

If we are serious about equity, we must invest now in the data infrastructure required for timely decisions and meaningful action.

Early years data in Australia

Data are used for many purposes in early years systems, from program design and service delivery to local priority setting and national accountability. Across Australia, however, these data systems are fragmented and uneven in their design and governance, reflecting both differing views on which data matter, and practical challenges in collecting data that are meaningful and fit for purpose. System capacity and decision makers’ ability to use data effectively also vary widely. As a result, there is little shared understanding of the value of early childhood data ecosystems or their role in supporting evidence-informed decisions and better outcomes for children.

Many services collect large amounts of meaningful data for their clients, but legacy systems and processes mean they are not always aggregated and reported in ways that inform decisions that matter for service improvement (Beatson et al., 2025). The real challenge is not gathering more data, but aligning data with decisions, priorities and specific contexts (Australian Education Research Organisation, 2022; Shepherd et al., 2015). Working backwards from decisions – asking what evidence different decision makers need, and in what form – creates smarter, more accountable systems that build trust and resilience (Fischer et al., 2025; OECD, 2022; Verhulst et al., 2024). 

Yet, as noted, Australia’s data systems remain fragmented. National collections such as the Australian Early Development Census (AEDC) provide important outcome measures, and service providers generate large volumes of compliance and reporting data, but these rarely flow back in ways that help families, services and local early years partnerships act with precision and speed (Beatson et al., 2025).

Another clear example of inconsistent data systems in Australia occurs in child and perinatal data collection. Each state and territory maintains its own perinatal and maternal child health datasets, with different reporting requirements, data items and governance structures (demonstrated in Australian Institute of Health and Welfare’s Australia’s mothers and babies reporting; AIHW, 2025a). There are no nationally consistent guidelines or a federally endorsed practice framework (Price et al., 2025). Despite the existence of the National Perinatal Data Collection (AIHW, 2025b), structural, governance and technical barriers limit cohesion and utility of these data. Victoria’s Maternal and Child Health dataset – one of the most comprehensive in the country – still cannot be routinely linked with other key health or social datasets, limiting its utility for population-level monitoring and early childhood policy development (Price et al., 2025). These fragmented systems result in inconsistent child health monitoring across jurisdictions, hindering the ability to build a unified national picture of maternal and child health outcomes (Shipton et al., 2025).

Research also highlights the barriers to data use identified by local early years partnerships: limited data literacy, constrained access and decision processes that sit outside a coherent data or evidence ecosystem (Sherker et al., 2025; Villanueva et al., 2024). In the UK, promising initiatives seek to create more robust data ecosystems, including the Nuffield Family Justice Observatory’s comparative examination of data gaps and opportunities across the child, youth and family service data ecosystems (Saied-Tessier & Gamboa, 2025), and NESTA’s work bringing together data types to better centre children and local insights in data-driven decision making (Wilcock & Symons, 2023).

At the same time, inequities in availability of, and participation in, high-quality, culturally safe, well-coordinated services persist (McClean, 2025; Tham et al., 2025). Workforce shortages, geographic isolation and uneven attention to quality uplift mean that those who would benefit most from these supports often have the least access – reflecting the ‘inverse care law’ (Hart, 1971; Mercer et al., 2021).

Australia holds substantial data assets, from robust national collections such as the AEDC (Australian Early Development Census, 2025) and the National Assessment Program Literacy and Numeracy (NAPLAN; The Victorian Curriculum and Assessment Authority, 2025), to immunisation registers and linked datasets such as the Person Level Integrated Data Asset (PLIDA; Australian Bureau of Statistics, 2023). Administrative data are reshaping research and policy (Gray et al., 2026). These assets focus on providing or linking to population-level outcome (lag) data (Table 1).

Recent work (Gray et al., 2026, 2023; Pham et al., 2024) has illustrated the potential of linked administrative data but also highlighted its limitations when these data are repurposed to inform population-level equity-focused action. Lead indicators (see Table 2) are often missing, incomplete or of variable quality, reflecting the fact that many data elements were not designed to support timely system-level improvement. These constraints reflect system-level factors, including: how assessments are structured across sectors; practical limits of data capture; limited monitoring of data quality; and poor interoperability between systems. One dimension that is missing is a broader suite of data and indicators that provide the stepping stones to lag data through early and effective action. These data include the following.

• Timely, repeatable (lead indicator) data that signal risks and opportunities earlier;
• Local context data to explain why outcomes look the way they do;
• Experience data that capture the perspectives of families, practitioners and communities to highlight barriers and enablers;
• Regular improvement data that track what actions are being taken and support adaption in a timely way; and
• Impact data that track whether changes are sustained over time.

Without these stepping stones for action, decision makers at all levels do not have a complete picture. Inconsistent definitions, siloed governance, limited access to timely information and burdensome reporting requirements compound the problem. Even basic data points such as a child’s Aboriginal and Torres Strait Islander status, language background or disability status may be defined or collected differently across systems, making it difficult to track participation and outcomes consistently. Children and families who fall into multiple priority groups are often tracked separately, leaving holistic needs invisible.

This fragmentation prevents services from recognising overlaps and coordinating support, potentially leaving needs unmet and often placing the burden on individual families to navigate support. Also missing is clear responsibility for identifying children who are missing out on beneficial supports altogether. Without a coherent data logic, these efforts remain isolated and difficult to scale.

The bottom line: Australia doesn’t need more data. It needs data that are purposeful, timely, accessible and designed to support equity and improvement at every level of the system (Cripps et al., 2025; Shipton et al., 2025).

Why data matter for equity

Inequities in early childhood outcomes are shaped by complex social determinants across the environments in which children live, learn and develop. Improving equity requires we move beyond ‘silver bullet’ solutions and embrace a more complex, stacked approach that combines essential early childhood health, education and social services – guided by timely and relevant evidence (Goldfeld et al., 2024). These inequities are evident in AEDC outcomes, where children in the most disadvantaged communities have almost three times the rates of developmental vulnerability in two or more domains compared with those in the most advantaged areas (20.3% vs 7.6%; Australian Government Department of Education, 2025).

Social determinants span from individual health and development to family resources, service quality and accessibility and the broader policy and system settings that shape children’s environments. They disproportionately affect priority groups (Restacking the Odds, 2025). No single dataset can capture this complexity. Addressing inequities requires data that make disparities visible and guide action. Sometimes this means linked, cross-sector data; at other times, timely experience data or regular improvement data are what matter most.

There is widespread agreement across early years sectors that data are an essential component for achieving equity, providing the fuel for system change. Without data, reform efforts risk being blind, fragmented and directionless. From the Early Years Strategy (Australian Government Department of Social Services, 2024) to the Productivity Commission Inquiry into early childhood education and care (ECEC) (Productivity Commission, 2024), the South Australian Royal Commission into ECEC (Government of South Australia, 2023), and the NSW Government Data Strategy (New South Wales Government, 2021), there is strong policy momentum recognising that better use of data is critical to identifying who is missing out, where gaps exist, and what works to improve outcomes. Despite this consensus, Australia does not yet have a coordinated approach to using early childhood development data in ways that recognise their value as a driver of action and continuous improvement, rather than an endpoint.

An early years data logic can help

A data logic clarifies how information supports change, ensuring the right data are collected, interpreted, and used to inform the right actions at the right time. It provides a practical guide for governments, local early years partnerships, service providers and community organisations to map the pathways from data collection and interpretation to decision making and improvement.

It also creates a clear structure that links data to purpose, making information more usable and reducing fragmentation. It establishes consistent definitions and standards so data can be compared and aggregated across services and jurisdictions. By including service-level, system-level and experience data, it provides a more timely and complete picture of access, quality and equity. Most importantly, it turns data into actionable insights, enabling those closest to children and families to drive meaningful change that they can control.

Other sectors demonstrate how a structured data approach drives coordinated action when embedded in systems. In the housing sector, the Australian Bureau of Statistics (ABS) housing data are combined with state social housing waitlist information to track affordability and inform homelessness service planning. The energy sector integrates provider data through the Australian Energy Market Operator’s (AEMO) National Electricity Market dashboard, which uses real-time consumption and generation data to manage pricing, reliability and emissions reduction targets. In each of these sectors, data are the foundation for regulation, resource allocation and continuous improvement.

Early years systems can benefit from a similar approach, using a structured data logic to guide decisions and drive continuous improvement. This means identifying the outcomes we want for children, the behaviours or practices we want services to adopt and the evidence those services need to act effectively.

A strong data logic moves beyond describing what is measured. It defines the purpose of different forms of data – outcomes, lead indicators, experience data – and shows how each enables decision making, accountability and continuous improvement. It also strengthens the ability to understand intersectionality, revealing patterns that siloed systems might otherwise obscure.

Early years data logic overview

The early years data logic (Figure 1) illustrates how various types of information can be combined to support decisions in early years settings. It provides a structured approach for decision makers in the early years systems to systematically consider what data already exist, what gaps remain and who else might hold pieces of the puzzle. By mapping these connections, partnerships can identify opportunities to work together and tackle different parts of the problem.

The early years data logic is not just a conceptual framework – it is a practical tool for guiding action. It clarifies what evidence is needed, for what purpose, and how different data types can be used together to build a fuller picture of early years systems and services, enabling each decision maker to answer the questions most relevant to their role. This structured approach ensures that evidence is both timely and actionable, enabling continuous improvement and equitable outcomes.

Decisions that shape children’s development are made by many different decision makers. No single decision maker holds all the levers; outcomes depend on how choices at different levels interact. Each decision maker asks different questions and is interested in different parts of the logic, but when aligned through a shared logic, they create a stronger foundation for action and learning.

Researchers already demonstrate what this looks like in practice: they draw on cohort studies and linked administrative datasets to explore the complex, non-linear ways in which evidence contributes to societal impact over time (Gray et al., 2026; O’Connor et al., 2025). An example is Generation Victoria (GenV), a prospective whole-of-state (i.e. Victoria) multipurpose birth and parent cohort (Wake et al., 2022). Enhanced through extensive linkage to state and federal administrative data, GenV will have the richness and breadth of information needed to generate evidence across all aspects of children’s lives (Goldfeld et al., 2024). Building on these approaches, the logic helps align data assets across the system (e.g. using AEDC data both to prioritise action and to evaluate impact). In this way, the logic creates a coherent structure that benefits all decision makers.

Table 2 summarises the six key data types, outlining purpose, examples, and how each can be used by decision makers. While each has a distinct role, their value is strengthened when used together. The purpose of the data logic is to support efforts to address inequities in childhood outcomes by clarifying what data are needed to understand where inequities exist, why they persist, and how action can be targeted. Equity markers, including indicators that capture priority groups in meaningful and consistent ways, therefore need to cut across all data types. These markers make inequities visible and inform more proportionate and coordinated responses to persistent gaps in services and quality.

Population-level outcome (lag) data are widely used, providing crucial signals of how children are faring and where inequities exist (Molloy et al., 2025). However, they only reflect what has already happened – often too late for timely action. By contrast, evidence-informed lead indicators offer earlier signals of risk and opportunity because they measure the conditions and drivers that research and practice show to predict outcomes.

By showing whether children and families are accessing the right services at the right amount and quality, these indicators turn abstract goals into concrete signals for action. Despite their importance, evidence-informed lead indicators are often missing from routine reporting, even though they can often be calculated from existing data. For example, most early years services will capture attendance records, but these are rarely reported or analysed to identify whether children or families participated enough to benefit, or whether priority cohorts were over-represented in patterns of under-attendance. Governments collect workforce census data, but local early years partnerships and services cannot access the data in ways that support practical planning for how many services are needed or how quality should be improved.

Embedding relevant, repeated and timely indicators into routine practice is essential for driving equitable outcomes. Mapping them makes inequities visible, aligns needs with resources and supports decisions that reflect local priorities and cultural safety (Restacking the Odds, 2025). With fit-for-purpose tools, training and strong leadership, data become a powerful enabler of continuous improvement.

The logic ultimately provides a structure for different decision makers to cooperate and ensure data are utilised to inform key decisions that improve children’s outcomes. The logic provides a shared structure for decision makers to align and use data to inform action, increasing the value of existing data, much of which may be high-quality and purposeful at the individual level, by enabling it to be used more effectively at scale. Box 3 provides a hypothetical example of how the early years data logic could be applied in an early years partnership.

Discussion and implications

A pathway for better decisions, not just more data

Early years systems generate vast amounts of data, but without a clear logic, these data can be overwhelming, fragmented and under-used. A shared data logic provides signposting to follow.

The logic is designed for use by government, local early years partnerships and service providers, offering a clear structure that links data to purpose: which data we need, why we need the data and how data should be used.

It provides a discipline for digging deeper – clarifying which data matter to support action and improvement. This also ensures data flows quickly back to practice, enabling timely adaptation and continuous improvement.

When decision makers have a common understanding of data utility, it provides a platform to establish processes and agreements for appropriate data sharing. For example, it provides greater clarity on what data are needed by frontline services to calculate lead indicators, and an impetus for governments to facilitate access to population data necessary to do so. This also aids understanding of what capabilities are required to analyse and use data (Verhulst et al., 2024).

The data logic is consistent with good data governance, where data are treated as an asset at each point, supported by a framework of policies, processes and responsibilities that ensure proper management, quality, security and compliance.

Implications for government

Multiple government departments at federal, state, territory and local levels have remits relevant to early years partnerships and service provision. Fragmented responsibilities and differing approaches to data collection, stewardship and sharing are key reasons a consistent approach – flexible enough to meet the purposes – is needed.

For governments, adopting a shared early years data logic has wide-reaching benefits. It enables a more coherent national and state approach, where definitions, governance and integration are consistent across services and jurisdictions rather than fragmented. This coherence supports better monitoring of access, quality and equity, and allows reforms to be targeted where the need is greatest.

Governments can use the logic to review existing data, identify gaps and ensure decision making is supported by the right balance of evidence, including timely, actionable lead indicators and experience data capturing the perspectives of families, practitioners and communities. By strengthening system oversight of quality, the logic enables governments to identify emerging safety risks and better ensure safeguarding is built into the system. Common definitions – such as identifying priority groups – further ensure that governments, funders and local early years partnerships operate with a shared understanding of who they are trying to reach and support, reducing duplication and improving the utility of data collection.

While fully integrated, whole-of-government data stewardship remains an ambitious objective, a shared data logic provides a practical rubric as a starting point. It establishes a consistent foundation, clarifies where gaps exist, and complements existing outcomes frameworks used across jurisdictions (e.g. National Early Years Strategy Outcomes Framework, Australian Government Department of Social Services, 2024; New South Wales Wellbeing and Performance Framework, New South Wales Government, 2025; and the Decadal Plan for Social Science Research Infrastructure 2024–33, Academy of the Social Sciences in Australia, 2024). The logic enables oversight of cross-system initiatives, such as the early intervention initiative Thriving Kids (Australian Government Department of Health, Disability and Ageing, 2025), to ensure they operate effectively and reach all children who need them.

A shared approach also lays groundwork for future system integration, supporting more connected service responses across early childhood, family support, health and protection systems (Australian Government Department of Social Services, 2024). There is conceptual work underway in Australia’s child protection and domestic and family violence spaces to better connect systems to provide more holistic support such as through ‘Safe and Supported: The National Framework for Protecting Australia’s Children 2021–31’ (Australian Government Department of Social Services, 2021). However, we note that none of these sufficiently joins up the different levels of data necessary for actual system change. We believe the data logic goes some way to being explicit about what is really needed.

Implementing a shared data logic also has budgetary and operational implications. It requires sustained investment in infrastructure, platforms, dashboards, workforce capability and governance to enable consistent data collection and reporting. An explicit equity lens is essential, ensuring data highlight and respond to gaps for priority groups.

The Australian Government is well placed to steward the logic, linking it to national initiatives such as the Early Years Strategy and early health, learning and social support investments, while national data agencies provide guidance. State and territory governments – including departments of education, health, family and children and social services – play critical roles in implementing standards, contributing data and ensuring data are accessible and comparable across jurisdictions.

Implications for research

Further research is needed to identify which data matter most for decision making across key stakeholders, and how they link directly to outcomes for children and families. Initiatives like GenV make it possible to establish an evidence base for identifying early life factors impacting outcomes, showing how cohorts can function as population-scale research infrastructure (Gray et al., 2026). This will allow us to define the most common and useful indicators to guide action and investment. Similar work has been completed in the youth justice system in England and Wales’, where coordinated, cross-sector work involving the Ministry of Justice, Youth Justice Board, frontline services, inspectorates and other government departments led to the development of new Key Performance Indicators (KPIs; Youth Justice Board, 2025). These KPIs use lead indicator data to support decision making in the multi-agency model and system-level performance management.

Implications for funders

For philanthropic funders, a shared data logic provides both a discipline and an opportunity. It enables them to direct resources more strategically, backing interventions that are demonstrably linked to improved outcomes rather than spreading investments thinly. Beyond this, funders can adopt the data logic as part of their own evaluation frameworks, embedding a common structure into how they monitor grants, track progress across portfolios and assess impact. Importantly, funders can also take on a catalytic role, supporting pilot sites to build skills and infrastructure that support collective interpretation and continuous improvement – through data systems that track long-term change and provide timely feedback.

Philanthropic and government funders should additionally ensure funding supports building relevant capabilities of local early years partnerships and services – such as fit-for-purpose software and tools, and required training in data literacy and continuous improvement.

Implications for local early years partnerships

For local early years partnerships, the introduction of a data logic strengthens local ownership and agency. Instead of being passive recipients of policy or service delivery, communities gain access to timely, relevant information that allows them to set priorities based on their own contexts and advocate for resources accordingly. This transparency builds trust, making it clearer what is working and where gaps remain. Communities are also empowered to coordinate action across services more effectively, with data providing a common reference point. In practice, this means partnerships, local government or backbone organisations can adapt their strategies quickly, rather than waiting years for outcome data to tell them what they already know anecdotally. Local early years partnerships should ensure data are used for decision making, and engage children, families and communities in translating insight to action.

Implications for early years services

For early years services including ECEC, child and family health, or parenting programs, the implications are both practical and cultural. Practically, services can shift from relying on lag outcome data to working with timely lead indicators and improvement data, which allow them to adjust practice in a timely way (Molloy et al., 2025). Culturally, the use of a shared logic encourages services to see their role within a broader ecosystem, understanding not only what happens within their walls but how they contribute to community-wide goals.

Many communities have limited access to high-quality services or have services that lack the resources and remit to effectively adapt and overcome accessibility barriers (e.g. outreach), an enduring manifestation of the inverse care law (Hart, 1971). Shared measurement of services, who is attending them, and why or why not, enables early years services and governments to work in partnership to reach underserved children and families. 

This requires investment in professional capability, ensuring that data are not seen as compliance but as tools for reflection, learning and continuous improvement. Building local capacity in data collection and use strengthens local ownership, improves engagement and leaves a legacy of skills (Kavanagh et al. 2025) In this way, services become more agile, collaborative and accountable to the children and families they serve.

Implications for data governance

The opportunities and challenges of using linked administrative data are well documented. Key challenges reflect both data limitations and system constraints, including: variable data quality and completeness; bias and data lags; governance, ethical and privacy barriers; limited technical capability and collaboration; and gaps in population coverage, reliance on proxy reporting, and missing social and structural indicators (Gray et al., 2026). In early childhood contexts, data validity and reliability are variable, reflecting heterogeneous assessment practices and administrative systems not designed for population monitoring.

Even where technical quality is high, large-scale individual-level linked data carry risks of misuse or misinterpretation, underscoring the importance of strong governance and ethical safeguards (Gray et al., 2026). To mitigate these risks and realise the potential of the data logic model, governance must extend beyond access control to include clear accountability for data stewardship, transparency of decision making, protection of confidentiality, appropriate consent arrangements and mechanisms to ensure data quality and security.

Governance needs may also vary with data types – with publicly available lag outcome data having a much lower requirement compared with types requiring service or individual consent. Governance must also comply with Indigenous data sovereignty (Maiam nayri Wingara Indigenous Data Sovereignty Collective, 2024) – ensuring Aboriginal and Torres Strait Islander Peoples and services have the right to govern data that pertain to them.

Conclusion

Australia stands at a pivotal moment in early childhood reform. Despite substantial investment, inequities persist in health, development and wellbeing outcomes for children. The missing link is not more data, but better data – data that is connected, purposeful and used to drive change.

This paper has outlined a practical data logic that clarifies what data are needed, for what purpose and by whom. When embedded across early years systems, this logic enables a shift from fragmented efforts to coordinated action, from reactive responses to proactive support, and from isolated initiatives to shared goals. Initiatives such as Restacking the Odds show what is possible when different types of data along the pathway are connected and used to guide improvement. They demonstrate how timely, actionable insights can strengthen service delivery, support accountability and build trust with communities.

Just as businesses thrive by turning data into timely decisions, early years systems must move beyond collecting data to using it with purpose. This is not just a technical fix; it is a moral and strategic imperative. It enables us to see who is missing out, understand what works, support service improvement and track progress over time. Most importantly, it centres children and families in the data story, ensuring their voices and experiences shape the systems designed to support them.

This paper presents a starting point to build upon. When governments set the conditions, funders invest in capability and early years partnerships lead local learning, the system begins to generate its own evidence – born of real improvement efforts – that can inform better policy, service delivery and outcomes for children. The opportunity is clear. The tools exist. What’s needed now is leadership, collaboration and commitment to build a data ecosystem that delivers on the promise of equity and better outcomes for all children.

Funding statement

This work was supported by the Paul Ramsay Foundation, Minderoo Foundation and Victorian Government’s Operational Infrastructure Support Program. Prof. Goldfeld is supported by an Australian National Health and Medical Research Council (NHMRC) 2023 Investigator Grant [2026263]. The funding sources had no involvement in: study design; collection, analysis or interpretation of data; writing of the report; or the decision to submit the article for publication.

Conflicts of interest

The authors declare no conflicts of interest.

AI statement

During the preparation of this work, the authors used Microsoft Copilot (M365) to improve language clarity and readability. The authors reviewed and edited all content generated with this tool and take full responsibility for the final manuscript.