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 requires using artificial intelligence (AI) to delve through data, to identify and classify concepts, and to build a semantic model with meanings and relationships. 

Data Harmony is a fully customizable suite of software products designed to maximize precise and efficient information management and retrieval. Our suite includes tools for taxonomy and thesaurus construction, machine aided indexing, database management, information retrieval, and explainable AI.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.