The Global Food System is a complex network of consumers, producers, economics, trade agreements, financial transactions, demand data, supply data, forecasting models, climatology, large and small-scale farms, politics, distribution systems, agricultural systems, nutritional standards, and access systems, just to name a few. By any measure, the data sets are big and unlikely to be efficiently handled by traditional scientific methodologies and technologies. For example, a recent report by the World Bank Group outlines an ambitious plan to end poverty and hunger in the world by 2030. In this regard, the measurement alone of poverty and hunger levels will take the careful and accurate handling of vast amounts of data which has many of the characteristics of what might be considered big data: volume (size), diversity, high rate of change, fluctuation, and complexity. There will be a need for leading edge big data analytics.
Many of us in the food industry have been tirelessly doing our bit to improve many aspects of how we feed and eat, locally and regionally. But, most of the analyses and retrospection about our efforts has been anecdotal, at best. How do we really know whether what we are doing is making positive strides and how/who is measuring that? The active players are mired in the day-to-day functions of their businesses to have any time to analytically reflect, based on accurate and significant data, on the effects of their actions. Ironically, running a successful business requires one to be on top of the quantitative & qualitative indicators beyond just a seat-of-the-pants approach. Accounting & cost control software are critical to ensure that a company's performance is within acceptable standards of sustainability and profitability. So, while we are implementing ways to measure aspects of our efforts, there will be a need to implement systems and technologies which will feed (pun intended) our local and regional efforts into larger data warehouses which will provide the backbone for big data analysis of national and ultimately, the global food system.
In the end, the sustainability of a better food system hinges on the collective actions of all. But it presumes that we can agree quantitatively and qualitatively on what the word better means. If the actions of a few improve the lives of the many, then the way that information is disseminated across the audiences will impact the collective buy-in of the many. Science and mathematics need to be emphasized and re-emphasized in the halls of education. Without them as part of the backbone, the results of improvements in the global food system arrived at via big data analytics will be viewed with speculation and mistrust because frankly, very few will understand the results. Food policy actions in the developed and developing countries rooted in and leveraged by the results of science will depend on a sound scientific modeling of the global food system. The resulting analyses and dissemination will ultimately dictate whether science has the power to change human tendencies and actions for the better. Meanwhile, let's invest in better understanding the volume of the data we are generating, one robust big data analytics system at a time.