Converting agricultural products into food requires a mixture of precise engineering and exacting science. Along the way, food processing companies generate vast amounts of data. Food safety programs require data. The efficient function of processing equipment and logistical systems also require data. Statistical analysis is a common way to analyze the day-to-day data, the results of which can remain internal to the operation or be summarized for other purposes. Food safety-related data is used to monitor and document processes. What if there was a different way to look at the data, though? What if the totality of data could be examined quickly and efficiently?

The goal of this new approach would be to use data analytics to discern some pattern, trend, or association that remains hidden when using normal statistics, but to do so in real time or near-real time.