Data Driven
Data driven implies the management of the company with the central importance of the information received and processed within the framework of the company’s functioning. Moreover, such management includes its visualization and interpretation in order to change the quality of the company’s work for the better. The calculus and statistical approach can now be used in all practical applications, from trade and economics to the regulation of natural phenomena. A specific topic for consideration in this paper is the application of date-based solutions to underestimate the environmental damage done by food manufacturers. The exclusion of the human factor from the decision-making process implies the concreteness of factual evidence of the need for specific decisions. Based on the principles of efficiency and achievement of goals. Through the involvement of sources considering exploratory date analysis as well as its effective visualization, the practical usefulness of such a methodological innovation as the data driven in the food industry is proved.
Annotated Bibliography
Susnik, J.(2018). Data-driven quantification of the global water-energy-food system. Resources, Conservation and Recycling, 133, pp. 179-190. Web.
This article perceives the processing of information about the ecological, economic and social situation of the planet as a continuous stream of a coherent system date that combines the supply of water, food and energy. In the context of this complex system, which is in a constant state of fluctuations, it becomes possible to observe dangerous tendencies regarding the depletion of certain material sources. This article clearly demonstrates how date processing can visualize a picture of social and material situation on a colossal scale and describes modern food production at the global level in statistical dimensions.
The study also shows to what extent easy access to statistical information on a global scale is able to build new methodologies for describing the world. The creators of the development have sharp causal and correlative relationships between all sectors that make up the basis of the food and energy chain. Demonstration of these flows clearly proves that they are all also linked by economic relations. Also the date used is verified against iterative sources in order to present more realistic pictures of the projected future. Following as a conclusion from the interpretation of these data, the risk of depletion of natural resources sets a fairly specific framework for this essay.
Srinivasan, R., et al.(2019). Modelling food sourcing decisions under climate change: A data-driven approach. Computers & Industrial Engineering, 128, pp. 911-919. Web.
The study is highly relevant to this work because it looks at the immediate future as a space for building a date-based strategy. The article focuses on the problem of global warming, which should have a colossal negative impact on the production of agricultural products and cereals. The use of publicly available statistical data allows the authors of the article to develop a strategy for changing the type of food supply to adapt to the changed economic and ecological climate. These data-driven strategies are presented in the form of functions that are designed to assess the potential success of the project and its risks. Through the primary importance of working with information with its collection, distribution and interpretation, there follows the possibility of developing an action strategy, which is also possible to evaluate in statistical data.
This article also demonstrates that in recent years there has been a real trend of using big data for work in the agricultural sector. Using data and visual indicators, researchers prove the real impact of the greenhouse effect and global warming on crops and the food industry around the world. The article offers a risk assessment in the fight against negative effects and possible threats to food products and the environment in the context of global warming and therefore can be extremely useful for the essay.
Wang, Q., et al.(2019). Data-driven estimates of global nitrous oxide emissions from croplands. National Science Review, 7(2), pp. 441-452. Web.
This study uses more than 16,000 pieces of global data collected from various local surveys and studies in order to draw up a real statistical picture of the impact of agricultural areas on the earth’s atmosphere. However, at the time of the creation of the study, data on gases ejected from grain fields and their contributing to atmospheric pollution were insufficiently studied and proven. Researchers have developed a map on which all problem areas of nitrous oxide emission are spatially distributed according to pollution levels. The information provided regarding airborne emissions of nitrous oxide from cereals is presented in a convenient visualized form. This article aims to demonstrate the statistical efficiency of using big data for forecasting and proving processes on a global scale.
The study also compares two ways of working with statistics, process-based models and those based on emission factors. Both models are presented as imperfect and subject to either lack of scale or likely inaccuracy. The introduction of an arithmetic model that takes into account the relationship between gas emissions and fertilizers in combination with other changing environmental factors allows a more accurate representation of the real situation. Indeed, the impact of nitrous oxide on the atmosphere on a planetary scale is one of the factors that bring global warming closer by affecting the climate. The article is of fundamental importance for the creation of this essay as it demonstrates how big data processing and analysis can create a coherent understanding of the immense environmental picture.
References
Susnik, J. (2018). Data-driven quantification of the global water-energy-food system. Resources, Conservation and Recycling, 133, pp. 179-190. Web.
Srinivasan, R., et al. (2019). Modelling food sourcing decisions under climate change: A data-driven approach. Computers & Industrial Engineering, 128, pp. 911-919. Web.
Wang, Q., et al. (2019). Data-driven estimates of global nitrous oxide emissions from croplands. National Science Review, 7(2), pp. 441-452. Web.