Executive summary
New Zealand (NZ) wine is a premium, export-led success story, within the food and fibre sector, with exports totalling ~$2.4b in 2024 (New Zealand Winegrowers, 2024). Yet, despite steady historic growth, today’s operating environment is tougher than ever; wine-buyers are increasingly cost-conscious, inventory cycles are more volatile, and producers face rising input and compliance costs. To remain competitive in the global marketplace, the industry must look to drive greater efficiency across all operations, while leveraging market insights to meet price-quality expectations.
The future success of the industry will be underpinned by data and insights that enable sharper, faster decision-making. While NZ wine has been inherently innovative and collaborative throughout its history, and the tools to collect, store, and analyse data are widely adopted, data interoperability remains a critical gap. Moving high-value data securely and reliably across vendors and organisations is still difficult, mirroring a broader agri-tech challenge (Dyckhoff, 2020; Loder, 2023; Skinner, 2023).
This study draws on two main sources of evidence; a targeted review of international and New Zealand literature on data interoperability in agri-tech and the wine sector, and semi-structured interviews with stakeholders across the New Zealand wine value chain, and broader agri-tech sector, with thematic analysis to link key ideas. Importantly, the findings from this report indicate that the limiting factors are not inherently technical in nature. APIs, cloud platforms, and open specifications are all viable solutions to the technical challenges posed by data interoperability, in theory. The real barriers lie in governance, trust, and commercial alignment (Douma, 2023; Dyckhoff, 2020; Noura et al., 2018). Encouragingly, few seem to oppose interoperability in principle, though the challenge remains making it work in practice.
This report sets out practical recommendations to bridge software vendors and software users, enabling connected data that improves traceability, strengthens market-access claims, and lays a sound foundation for emerging tools. AI can deliver advanced analytics when data is clean, portable, and well-governed; blockchain can anchor integrity and auditability across organisational boundaries but does not solve data quality, semantics, or ownership; those remain governance challenges. Accordingly, blockchain is an optional enabler to consider only after interoperable identifiers and profiles are established (Bellavista et al., 2021; de Lange et al., 2025).
The recommendations of this report aim to focus less on inventing new technical solutions, and more on aligning incentives, rules, and capability to make the existing technology work at scale, with three key action points:
- Intentional, pragmatic, iterative standardisation: Start small with minimum-viable interoperability and reuse what already exists; harden profiles collaboratively.
- Review data-ownership and incentive models: Clarify rights over raw vs derived data; make portability the default; align pricing and value flows.
- Build sector digital capability: Role-based training, named data stewards, and simple how-to patterns that lift everyday practice.
Together, these actions prioritise governance, incentives, and capability, rather than new technical standards to unlock safe, reliable data flow that improves efficiency, de-risks compliance, and strengthens market access, and through the enablement and use of interoperable data, serves to support continued industry growth and prosperity.
Zac Howell
