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.
Through partnership Charles Sturt University and Meat and Livestock Australia will deliver the NextGen Data Capability Program. A program that accelerate Data Strategy initiatives for the red meat industry, all while developing the critical knowledge, skills, confidence and capabilities of the future workforce needed to drive and solve future Australian red meat industry data opportunities.
ISC has access to an extensive and continually expanding dataset of customer insights consisting of numerous verbatim comments from a broad base of account users, sourced from a wide range of channels. This presents an exciting opportunity for a student to work with a substantial real world dataset and contribute to meaningful industry outcomes.
With rapid advancements in AI and natural language processing, there is significant potential to analyse sentiment, uncover key themes, and generate actionable insights from this customer feedback. A student undertaking this project would have the opportunity to apply modern analytical techniques, explore cutting edge AI tools, and help translate complex data into insights that inform product strategy, enhance user experience, and strengthen engagement across the livestock supply chain.
Overall, this project offers a unique opportunity for a student to gain hands on experience in data analysis, AI driven sentiment modelling, and industry-focused research, while contributing to initiatives that deliver tangible benefits across the red meat industry.
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
Data code development
AI (large language models) prompt engineering and API
MLA is in a strong position to improve how research data is captured, catalogued, and shared across the red meat industry. As a not for profit organisation overseeing a wide range of R&D investments, MLA collects diverse research outputs from multiple projects and partners. The current variability in how this data is received and organised limits its discoverability, its potential for reuse, and its long term value to industry. This project will inform the development of enhanced data capture and onboarding processes designed to create a more accessible, consistent, and future ready research data environment.
A key opportunity lies in improving data accessibility and reuse. By strengthening the way project data is structured and catalogued, MLA can make it easier for teams to locate and draw insights from existing work. This will help reduce duplication across projects and support faster, more meaningful outcomes for industry.
This project will contribute to MLA’s data capture and onboarding processes, helping build a more accessible, consistent, and future ready research data environment. Involvement in this project offers hands on experience in data governance and research management within a national scale industry environment, providing practical skills that are highly transferable across the data and research sectors.
Business process analysis
Stakeholder engagement & communication
Data governance & policy understanding
Data management & vataloguing
A review of program data has highlighted an opportunity to better leverage the information collected through participant self assessments and supporting planning documents. These materials contain rich and structured data that, if analysed more systematically, could provide valuable insights to strengthen program oversight and support continuous improvement.
Enhancing the analytical approach would help build a clearer picture of broad compliance patterns across different production contexts, highlight areas where additional support or education may be beneficial, and improve visibility of developing trends within the broader operating environment. This would enable more informed decision making, ensure resources are directed effectively, and reinforce the long term integrity and resilience of the program.