Developing aggregated data insights for LDL

Summary

Livestock Data Link (LDL) is an online application that facilitates improved information sharing in the supply chain. LDL enables feedback to be received, analysed, and compared to other results in an efficient way.

This project identified and evaluated the value of LDL to provide examples of candidate aggregated data insights ('insights') that would be useful to producers and where appropriate processors that utilise LDL.

Objectives

The primary objectives of this project were to:

• develop a comprehensive list of aggregated data insights that might be considered as candidates
• provide a shortlist of candidate insights following consultation with ISC, LDL User Advisory Committee, key stakeholders and producers
• provide example results for shortlisted insights, with documentation. Estimate value of 'insights' in improving compliance. 

Key findings

• More than two thirds of producers surveyed indicated that they were prepared to share data and that the key insights that they required were geographical (location) and time-based benchmarks of carcase attributes.
• For single trait or factor insights, a combination of tables, histograms and line graphs is the optimum combination, particularly when benchmarking against time.
• For insights that had two or more factors and a range of production characteristics, a standard combination chart of a scatterplot and a histogram would be most appropriate and best understood by livestock producers.

Benefits to industry

LDL is a platform for accessing and benchmarking carcase data, animal health performance and for reporting on non-compliance rates in the Australian red meat industry.

Unsurprisingly 71% of producer respondents to that survey indicated that obtaining insights on carcase compliance from LDL would be useful in making informed management decisions.

MLA action

This project was designed to support ISC in making informed decisions on what insights would be the most useful for producers and processors based on LDL data sets, what formats those insights should be displayed in and finally what types of interactive dashboards could be created.

Future research

Future research should focus on including:

• charts to visualise traits when aggregated against production characteristic or time series
• factors drawn in hierarchical order from most to least abundant to enhance visualisation over time or location time series
• dynamic dashboards with three sub-types implemented to enable end-user customisation of data
• grid functionality and control.