The Red Meat Industry identifies data as "one of the industry's most powerful tools" and commits to a journey of digital transformation in data, artificial intelligence (AI) and digital tools leading to new opportunities for growth, efficiency and insight. This transformation will improve traceability, transparency, and decision-making across the supply chain. The red meat industry however, lacks the skills, expertise and human capacity to effectively drive industry wide transformation, now and into the future. There is currently no pipeline to create the workforce of the future so industry can realise the value of digital transformation.
Charles Sturt University has partnered with Meat & Livestock Australia (MLA) to deliver the NextGen Data Capability Program, a strategic initiative that brings graduate students into the fold to work on real-world data challenges. The program accelerates data strategy initiatives for the red meat industry while building the knowledge, skills, confidence and capabilities of the future workforce needed to solve emerging industry opportunities.
Help design the data foundations that power Australia's livestock traceability systems.
This project offers an opportunity to work with large-scale agricultural datasets across key industry platforms, including LPA, NLIS and eNVD, to improve how Property Identification Code (PIC) information is connected, managed and utilised.
Students will analyse existing data sources, document data flows and relationships, assess data quality, and design a unified data model that enables more efficient access to PIC information across systems. Working alongside industry and data specialists, you will gain hands-on experience in data modelling, SQL, data warehousing, metadata management, data quality analysis, and solution design within a cloud-based data platform environment.
The project will contribute to creating a more connected and accessible data ecosystem, improving the user experience of industry applications and supporting better data-driven decision-making across Australia's livestock sector.
Data analysis / query: SQL, visualisation dashboard design and development, Data model design
Business process analysis: Translate the understanding of customer feedback process into data and insights solution.
Help shape the future of AI-assisted decision-making in the agricultural industry. This project will explore how agentic AI and Large Language Models (LLMs) can be used to improve the quality and consistency of project proposal development and assessment within MLA.
Working with real organisational processes and datasets, you will design and build an AI workflow capable of reviewing project proposals against strategic, investment, and governance requirements, providing actionable feedback to support stronger submissions and more efficient review processes.
The project offers a unique opportunity to gain hands-on experience with LLMs, multi-agent AI systems, workflow automation, dataset development, and evaluation of AI outputs against expert human reviewers.
The outcome will be a proof-of-concept AI capability that demonstrates how intelligent systems can enhance decision-making, streamline business processes, and create value across MLA's innovation and investment portfolio.