Predictive analytics uses historical data to predict future events. Typically, the data is used to build a mathematical model that captures important trends. That predictive model is then used on current data to predict what will happen next, or to suggest actions to take for optimal outcomes. This interesting information came to us from Analytics Insight in their article, “Using Predictive Analytics in Agriculture: A Digital Twist.”
There are many applications for predictive analytics – healthcare, industry and even agriculture. Data-driven agriculture aids in continuous monitoring and enables efficient management of supply chains. Being able to predict the risks and alternatives ensures better and faster distribution of products.
The agriculture industry endures the brunt of climate change. Using predictive analytics enables farmers to foresee weather conditions for effective resource management and promotes sustainable agriculture.
Analyzing that data takes artificial intelligence (AI) to delve through data to identify and classify concepts and build a semantic model to create meaning and relationships.
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Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.