Nowhere is the consumer demand for quality higher than in the food industry. At the same time, nowhere is the need to control cost greater.
We expect the food we eat to be safe, wholesome, and affordable. For those who provide our daily bread, balancing these priorities is a never-ending challenge.
According to the American Center for Disease Control (CDC), nearly 1 in every 6 Americans becomes sick, 128,000 are hospitalized, and 3,000 more die of foodborne diseases each year. And that is in the world’s most developed country.
But help is on the way.
Data analytics and data prediction technologies offer powerful solutions for food producers, transporters, grocers, and restaurants.
How data analytics impacts the food industry, and the use of predictive analytics, is our topic for this post.
Let’s get started and see how this powerful technology is transforming food production.
Table of Contents
- Data Analytics in Food Industry
- The Role of Data Analytics in the Food Industry
- Predictive Analytics in the Food Industry
- 4 Examples of Using Big Data Analytics and Predictive Data in the Food Industry
- The Future of Big Data in the Food Industry
- How Ignite Can Help
Data Analytics in Food Industry
Data analytics simply refers to the process of analyzing raw data to gain useful information. It uses insights provided by artificial intelligence (AI) to benefit the entire lifecycle of your food, from the farm to the plate.
Great. But how does that ensure that your dinner salad is fresh?
By pulling data from a variety of sources, software applications can identify problems that can affect food quality, safety, and freshness. Here is just one example of how it works.
The Role of Data Analytics in the Food Industry
The following simplified process represents one example of how data analytics can benefit growers, transporters, processors, and food retailers:
- The farmer enters soil test results and planting and harvesting data into a database used by the software program.
- Weather information for the entire growth cycle of the crop is also entered into the database. The inputting of precipitation and temperatures can be automated.
- The logistics company that transports the farmer’s crop from the farm to the processing company supplies the database with starting and ending times for the trip. The temperature of the refrigerated truck can also be monitored and added in as well.
- The food processor enters start and stop times for various stages of the process. Sorting, washing, packaging, and placing in cold storage can all be tracked with automated sensors.
- Again, the product is monitored on its way from the processor to the grocer or restaurant. Any delays that could cause the food to spoil prematurely can be easily identified.
- At the destination, the food vendor can enter information about the quality of the food when it arrives.
- Customer feedback on social media can be pulled into the aggregated data to provide even more insights to the food supply chain.
The entire supply chain can access this information. If there are problems, they can modify their operations to prevent a recurrence. More importantly, the retailer can use the data to decide whether to accept or reject a shipment.
No other technology provides this level of insight into what happens to our food before we eat it.
The software performs an analysis of the data and provides intelligent insights to all parties to the supply chain. Analytics software relies on a large number of sources for making its analysis, including social media. The collection of structured and unstructured (or raw) data used for data analytics is called big data. As we will see shortly, big data can benefit the food industry and its customers in other ways, also.
Predictive Analytics in the Food Industry
We just looked at some of the information that is available through big data. The actual amount of information that is available to data analytics software is gargantuan. By accessing huge amounts of data, predictive analytics software can help ensure that your breakfast omelette is fresh and safe to eat.
Data prediction, or predictive analytics, is cutting-edge technology. It taps the power of AI to identify patterns and predict outcomes. Here are a few examples of what it can do:
- The soil samples farmer Bob provided earlier can be used by AI to predict the quality of the harvest. Rain and temperature data further augment the analysis, even helping predict the size of the harvest before it has matured. Knowing in advance the probable crop yield can help vendors plan their pricing strategies far in advance.
- Traffic conditions, road construction, detours, and even adverse weather can all affect how quickly food products can get to market. Big data can inform AI of each of these issues, enabling the software program to predict the freshness of the food before it arrives at its destination.
- Environmental data is being used to provide long-term forecasts to farmers.
Predictive analysis is an enormously powerful tool that can foresee problems with the supply chain, and even predict customer behavior. Only AI has the power to sort through the huge amounts of data and to make sense of it in real-time.
4 Examples of Using Big Data Analytics and Predictive Data in the Food Industry
We have all seen the MedTech and FinTech revolutions. Now FoodTech is breaking new ground, with startups already carving out market shares for themselves.
Data prediction in the food industry is a lucrative nascent market that wise startups are tapping. Here are a few that are wasting no time staking a claim on this emergent market.
Connecterra has developed “Ida,” an AI-powered program that can help farmers predict certain health issues with their cattle. Ida not only helps farmers protect their assets, it helps them to provide higher-quality milk and beef by keeping cattle healthy.
2. The Yield
Australian-based The Yield startup has set its sights on the micro-environment AgTech industry. The company’s products use a variety of sensor technologies to monitor agriculture and aquaculture environments. Data provided by the sensors is used to create forecasts and to provide other insights.
American Brightseed uses AI, big data, and predictive analysis to identify beneficial plant compounds. They then use this data to create bioactives that can be added to make foods more nutritious and healthful.
With less than 0.1% of plant compounds having been discovered, opportunities are great for data analytics to produce the elusive superfood.
Quantzig targets the business end of the food industry. Its predictive analysis products help companies to make better decisions regarding marketing, sales, and pricing. Whereas the results may not directly affect food quality, they can have a tremendous impact on operational efficiency and, therefore, cost reduction.
The Future of Big Data in the Food Industry
We have seen just a few of the real-world applications for data analytics and predictive analysis. There are many more than we can cover here. But what about the future of this emerging technology?
Here are just a few of the many ways we expect to see data analytics in the food and beverage industry in the years ahead:
- Advanced restaurant-management software — powered by AI — will enable managers to be proactive in managing their restaurants. Food product availability and pricing, changing trends in customer preferences, and marketing trends can all be predicted well in advance.
- Mobile apps will suggest restaurants based on analysis of their supply chains. This will help consumers locate those with the freshest ingredients on a given day.
- Customer purchasing trends will help food companies develop food and beverage products to meet the changing tastes of their markets. The result will be new products on store shelves, and more choices for shoppers.
- Based on your dining history, the time of day, and your direction of travel, a restaurant using predictive analysis software could conceivably predict that you will visit their eating establishment soon and could suggest what you might order. Yes, really.
- AI and deep learning are being used extensively in the medical industry to identify and treat disease. It is only a matter of time until the same technology is focused on preventing foodborne illnesses at the molecular level.
Perhaps, predictive analysis will one day be used to alert us to restaurants where we are likely to contract an illness, based on their sanitation scores and customer reviews.
How Ignite Can Help
As food and beverage companies strive to deliver quality food at a profit, they need solutions to help them do both. Outdated approaches will no-longer suffice.
Successful startups are capitalizing on the emerging FoodTech space. But success in this highly-competitive marketplace requires more than an idea, it requires bleeding-edge technology.
If you don’t have data analytics and predictive analytics professionals on your team, your piece of the FoodTech pie will go to someone else.
That’s where Ignite can lend a hand.
We offer teams of world-class developers who specialize in big data, AI, and data analytics for the food industry. If you want to discuss how our team can be your team, why not contact us today for a no-cost consultation?