5 ways in which big data is helping the food industry tackle challenges

big data is helping the food industry

Five ways big data is helping the food industry tackle emerging challenges

Much like any other industry, big data solutions are invading the food industry as well. Stakeholders ranging from farm owners to restaurant owners are trying to use the power of data analytics to answer questions which were considered unanswerable in the past. Not only is big data helping food players and public health monitoring agencies mine tonnes of relevant data for relevant analysis, it is also giving life to novel business models startups enter the food industry value chain.  In this article, I have highlighted five ways in which big data is helping the food industry tackle its historical and emerging challenges.

#1 Improving Farm Output and Efficiency
Precision agriculture is the new buzzword in the farming industry and data analytics forms the backbone of this initiative. A farming management concept, precision agriculture measures and responds to field variability for crops, often using satellites and GPS tracking systems. Big data is employed to analyse gathered farm data using developed and stored predictive analytics algorithms. The intent is to find solutions that can improve yield, reduce crop failure and optimize input resources such as water, soil feeds, etc.

Companies such as John Deere and Monsanto have been at the forefront of big data deployment in agriculture practices. In 2012, Monsanto acquired Precision Planting, a Tremont-based big data driven planting equipment and technology firm. In 2013, it acquired Climate Corporation, a weather big data analytics company based out of San Francisco. A year later, it bought another Chicago-based big data player, 640 Labs. 640 Labs uses wireless and mobile technologies data from farms to build yield improvement solutions for the farmers. Such strategic moves from Monsanto suggests the company is determined to make big data an integral part of its core agricultural operations.

#2 New Product Development
Massive relevant data sets on food products can also help companies find the ideal new ingredient or substitute for existing products. A well-known example of this possibility is that of Hampton Creek, the company behind Just Mayo, a mayonnaise created from plant-based proteins. The company analysed 18 billion plant proteins to discover the best-tasting substitutes for the traditional ingredients of mayonnaise. None of this, at least of this scale, would have been possible without the intervention of big data analytics. If the concept catches up, we might see a lot more ingredient options available on food shelves in the future.

#3 Optimizing restaurant menu and efficiency improvement
Big data can be extremely useful for franchise-model restaurants chain in identifying trends and process improvement measures. For e.g., players such as McDonalds and Starbucks employ big data to track consumer purchasing trends  and identifying replaceable restaurant-level best practices.

Similarly, a lot of restaurants these days are leveraging the services from big data analytics vendors to optimize their menu. A noteworthy and emerging name in the vendor category is that of Food Genius, a US-based analytics player that aggregates data from restaurant menus to decipher pricing, consumer food preferences, winning marketing strategies and emerging trends. To widen its information source base, apart from collaborating with a huge network of restaurants, the company has also tied up with food delivery startups of the likes of Grub Hub. Other vendor startups operating in this arena, with different business models, include Avero and Punchh. Avero provides point-of-sale big data software solution for restaurants. Punchh is a mobile app that allows customers to leave reviews, earn loyalty points and even order food from the device.

#4 Waste Management
WISErg, a Washington state based startup, has developed a unique product called Harvester that converts restaurant food waste into fertilizers. What is noteworthy is that the company employs a cloud-based analysis solution for tracking the waste disposal activity. It uses the analyzed data to help restaurants improve their waste generation and management practices. Another firm that employs a similar business model is the New York based BioHitech. Its digester device is called Eco Safe Harvest. This concept can easily be extended to grocery stores and if necessary technological interventions can bring the harvester costs down, this product and the tech will be definite success.

#5 Preventive Care against Food Poisoning
The USFDA, in its GenomeTrakr database, has combined the power of big data and genomics to isolate the cradle and causes of food-borne illness outbreaks. The database first creates complete DNA sequencing of dangerous pathogens, maps it to identify lethal ones from isolated food samples and then matches them with pathogens from sick patients. The intent is to minimize the time currently taken by FDA in responding to lethal contamination in food products. According to a statistic database, food contamination causes more than 325,000 hospitalizations and 5,000 deaths in the US every year. The database is still growing and potentially has global relevance.

In another instance, Chicago Department of Public Health has started employing big data predictive analytics for its food inspection audit program. The solution is much needed as the city has only 32 food inspectors for auditing more than 15,000 restaurants. The department uses data sets such as the current weather, nearby construction, past health code violation and online restaurant reviews to identify and prioritize high risk centers for audits. Needless to say, this idea needs replication.

Within the private enterprises as well, food safety is gaining mainstream attention. Under the umbrella of Consortium for Sequencing the Food Supply Chain, Mars and IBM are jointly working on an initiative that collects data from microorganism samples at Mars facilities, which is then analysed by IBM for food safety vulnerabilities. The intent is to ultimately create healthy and protective microbial management systems within the food supply chain. Both the current stakeholders plan to engage more participants such as universities, farm owners and other food players in this research initiative. In the foreseeable future, we are likely to witness this expand into a global movement.

 

Anubhav is a data scientist who works and writes on new big data decision sciences models and their application in key business areas across industries. Anubhav also tracks the industry developments. He can be reached at anubhav@thinkbigdata.in

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