ASAS-NANP Symposium Introduction: The Evolution and Application of Information Technology to Assist with Animal Production Analytics

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https://zoom.us/j/95578742888?pwd=MzNmQVNyaFFqNFB5UTNFRGh4V0Z1dz09

Information technology (IT) has evolved remarkably since the 1940s, surpassing its original purpose of data storage, retrieval, transmission, and manipulation using digital computers. IT has been slowly but steadily intertwining with data analytic approaches to gain insight through statistical design and methods, knowledge-based modeling and simulation, and data-based and learning technologies. These technological systems are classified as descriptive, predictive, prescriptive (forecasting), and self-ruling (self-learning), and their level of sophistication usually goes from basic for descriptive systems to complex for self-learning systems. Data analytics encompasses one or more quantifiable approaches used to extract meaningful information from data to develop technological systems to assist in the decision-making processes. Understanding which one and how to apply the information technology of data analytics can provide a competitive advantage to diverse entrepreneurship, including those associated with the animal industry. As the world becomes more complex in competitive analytics, the critical lemma has become how to squeeze the data to get more valuable information under such a competitive environment. Questions such as what can we learn from the data? Do we need more data? Are there different points of view of the data (and results)? Data analytics commences during the cleaning up phase of the data. Therefore, identifying and deciding to remove outliers, leverage, incorrect, and missing data is an important step, though tiresome, to establish the truth. Researchers should be seeking genuine relationships among variables to discover and form new knowledge for future wise decisions. Such endeavor requires an extensive description of the data and rationale for the perceived variable relationships. The analyst has to decide on the robustness of the data and how far (analytics progression) they can go with the data in hand. We will compare different data analytic approaches to extract information and develop predictive tools relevant to different animal production scenarios.