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Predictive Analytics Require Good Data

  • Most obstructions in predictive analytics are due to data-related limitations. Data that is incomplete or incorrect will provide insights that are not accurately or thoroughly analyzed.

  • Farmers should look for technology that collects accurate, complete, relevant and timely data.

  • Building a strong data foundation will pay off greatly in the long run. 


Predictive Analytics Require Good Data

Predictive analytics are the future of ag technology. They can guarantee outcomes, making it possible for producers to make specific decisions on how to operate their farms most efficiently and productively.

Accurate predictions depend on data collected, and collecting that dataset is no easy task. It requires a high level of confidence in the technology from animal to animal, day to day, and feedlot to feedlot.

Given this, most obstructions in predictive analytics are due to data-related limitations. Data that is incomplete or incorrect will provide inaccurate or incomplete insights. It can be an easy trap for producers to rely on incomplete data to predict outcomes, which can lead to lost production, lost time and lost revenue.

Cargill has identified four pillars of good data, helping producers know what to look for when determining which predictive technology to use.

1. Accuracy

Accuracy is a crucial data quality characteristic because inaccurate information can cause significant problems with consequences. For example, if you are looking to figure out a feed formula, you need precise lab quality data on the exact nutrient data. It seems straightforward, but make sure the technology you choose is collecting true and precise information.

2. Completeness

Predictive analytics rely on patterns, so a more extensive dataset leads to more accurate predictions. Also, with more data collected, there is less chance that the data is incomplete. There are systems that can monitor animal behavior and operations on the farm 24 hours a day, seven days a week, 365 days a year, giving farmers a comprehensive set of data to see patterns within.

3. Relevancy

Relevance comes into play because there must be a good reason why farmers are collecting this information in the first place. If the data they collect doesn’t match their farm’s objectives, then it is essentially useless data – wasting time and money.

The producer should think about the end goals when deciding the technology they want to implement on their farm and the predictions they want to make. For example, if they want to improve behavior, they can track behavioral patterns; for health, they can track food and water intake. Then predictive analytics can determine how to optimize best, giving them information that is useful to them, without having to spend hours digging through spreadsheets.

4. Speed & timeliness

The quicker the data is available for analysis, the faster the system can make a prediction, and the faster the farmer can decide what to do next. In addition, it is crucial to have the most up-to-date information.

Real-time data and insights can have a big impact. For example, in shrimp farming, capturing data pond-side and developing a live operations dashboard view of pond status and shrimp growth can help farmers accurately predict outcomes by combining production data with environmental data to maximize profits.

Or take dairy farming, in which consumer demands are constantly changing, and there’s consistent volatility and market changes. Data must be delivered immediately in order to be relevant.

Ensuring success with predictive analytics
Implementing predictive analytics into the farm can be a daunting process, but farmers will benefit in the long-run with data that is timely, accurate and aids in decision-making.
To learn more about the latest in farm technology, check out more articles on Feeding Intelligence.


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