Major Use Cases of Big Data Analytics in the Food Industry
Here are a few big data analytics in the food industry that changes the food and beverage business practices to make better-informed decisions:
- Test New Products
- Improve Operations
- Cater More Effectively to Customers
- Precision Agriculture
- Accurate Information and Forecasts
- Food Safety and Traceability
- Food Safety Through Multiple Data Sources
Fast food chains can use data analytics to evaluate the financial impact and popularity of new products—including food items and in-restaurant technologies—before they implement them. Using food and beverage analytics to look at how customers interact with drive-through menus, for example, can give chains insights as to how they will react to certain technologies and changes. They can also utilize publicly available data for further knowledge of customer preferences and habits. Additionally, chains can conduct surveys that will allow customers to give them direct feedback about how they would respond to a new product or in-store attraction or service.
They can use analytics to increase the speed and quality of service. They can also derive insights from the data collected from delivery orders and get a better picture of where their customers live and what they are willing to spend money on having delivered. Analytics and big data can also improve in-store operations—for example, chains could analyze data on wait times to improve service and decrease the number of time customers spend standing in line to order and receive their food.
Analysis of data about what food products customers prefer can help fast-food chains to optimize their menus and increase sales. The use of food and beverage analysis for fast food menus lets chains know what the most popular or most frequently purchased menu items are, as well as which largely unpopular items they can cut to save costs without much outcry from customers. It can also tell them what changes they can make to their menu to expand their customer base—data about the prevalence of food allergies, for example, can help chains decide what ingredients to alter or omit so that a larger number of people can safely access their products.
The advent of GPS and GNSS technology has enabled precise location tracking of field maps to measure variables such as crop yield, terrain topography, organic matter content, and moisture levels. Such data helps farmers in effective water management, waste minimization, crop yield increment, and minimizing environmental impact. The data captured from thousands of tractors on farms across the world can then be collected and analyzed in real time.
The food industry depends on accurate forecasts and the right information to maximize crop yield. Integrating information relating to soil, weather, and market prices with granular data can provide inputs to optimize the agricultural input factors. Additionally, optimizing the agricultural input factors increases crop yield, optimizes resource usage, and lowers cost.
Big data is set to change the landscape of the food industry by enhancing food safety and traceability. Big data along with IoT proactively monitors the condition of food right from the farm to fork and sends out an alert when discrepancies are found. As a result, consumers are always assured of the food quality and food wastages are minimized.
Big data takes the food safety game to the next level by gathering data across other verticals apart from just temperature and humidity. For instance, regulatory inspection programs are taking advantage of publicly available information such as food inspection reports, 311 service data, community and crime information, and weather data to run predictive models to identify restaurants that are likely to breach food safety regulations.
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