Project 3: Smart Services for Maori Primary Industry
This spearhead project, Smart Services for Maori Primary Industry, will focus on developing digital decision models to help Maori-led primary industry create greater value. It will involve analytic tools, including those that incorporate effects of risk, international competition, and remoteness from markets.
Core to this project is a strong Vision Matauranga (VM) ethos, which is being defined through collaborative research efforts with Maori collaborators. Examples of how this might influence the research outcomes include:
1. Understanding the role of sustainability in decision making, whereby inter-generational outcomes are given a greater importance than occurs in traditional business decision making;
2. Consideration, analysis and modelling of environmental outcomes;
3. Development of formal models that help quantify and explore the trade-offs between a broad set of traditional and less-common outcomes, such as financial gain, cultural values, environmental outcomes, spiritual traditions etc.
4. Development of models that recognise the potential impacts of risky outcomes on the value contributed to future generations, and include strategies to manage these risks;
5. Looking for provenance opportunities and ways of unlocking value from the indigenous story;
6. Decision making and modelling that formally recognises concepts of Mauri;
This project will draw upon a toolbox of analytics tools, such as:
1. machine learning;
2. data visualisation;
3. statistical analysis;
4. computer science;
5. operations research;
6. big data processing, and
The emphasis of this research will not be to replace the decision maker, but instead to use analytics approaches to:
1. help frame the questions being asked and the decisions to be made;
2. provide a language for discussion of these decisions;
3. provide interactive tools that help decision makers better understand the trade-offs that have to be made;
4. use visualisation to help explore data relevant to the problem, and
5. create tools that allow decisions to be evaluated against a wide variety of metrics, such as risk, short term profitability, long term economic value, environmental outcomes, and spiritual values.
The research in this project will be driven by a need for analytics that we identify from within the Maori primary sector. The first step is to identify a Maori collaborator who faces an important optimisation or data analysis problem, and then to work with this collaborator and other stakeholders to build novel analytics approaches and tools that best address the distinctive requirements of their problem. The ideal project and collaborator for this case study would include the following characteristics:
1. The problem domain is widely regarded as being important to Maori;
2. The problem involves complex decisions that require balancing competing objectives;
3. Outcomes of the decisions have long term consequences that are hard to predict;
4. Decision makers would benefit from a deeper understanding of these risks;
5. The problem is likely to result in the development of tools and/or processes that are integrated into the long term decision making processes of the collaborators;
6. Solving the project requires novel approaches that draw upon a wide range of analytics tools;
7. The collaborator has extensive data to support their decision making, and some experience in processing and exploiting this data;
8. While focussed on a particular problem, the analytics approaches we develop and showcase are likely to have wider applicability within the Maori community;
9. Success will lead to wider adoption of analytics techniques and data-driven decision making by the collaborator, and within the target sector more generally.
Our research will result in new approaches that sit at the interface between indigenous knowledge and modern analytics practice, and will assist Maori businesses to improve productivity and performance while contributing to a deeper exploration of indigenous values and more sustainable environmental outcomes.
Project 2 is led by Associate Professor Andrew Mason from the University of Auckland Department of Engineering Science.