Tag: big data analytics in food

IR30

5 Ways Big Data and Analytics is Revolutionizing the Food Industry

The food industry is one of the most profitable sectors across the globe. However, with the increase in global competition, companies in the food industry have started facing numerous challenges such as reducing supply chain waste, optimizing supply chain efficiency, boosting sustainable growth, implementing green policies, and finding a competitive advantage. To deal with such challenges, businesses in the food industry need to focus on leveraging big data and analytics to keep close tabs on new supply chain trends and competitors’ market progress.

Inefficient supply chain leads to low-profit margins and more wastage. Furthermore, if the supply chain is not optimized, it can result in the inability to adapt quickly to a problem and can increase operational costs. Therefore, players in the food industry must leverage the benefits of technology like big data and analytics to gain better insights into the ability to track and monitor supply chain activities in real time.

At Quantzig, we understand that to ensure the efficiency and performance of your supply chains, from sourcing to manufacturing to delivery, optimization is required. And to help companies in food industry thrive in the competitive landscape, our team of experts has highlighted five ways in which big data and analytics can help companies in the food industry to become market leaders. 

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3 Common Mistakes to Avoid in Big Data Analytics

Today, big data analytics is one of the most crucial processes for any business, big or small. For data scientists, it acts as a pair of glasses that helps them see the actual reality of a business’ performance, beyond scattered numbers in graphs. A proper, solid, and reliable analysis allows you to make fact-based and rational decisions, but if mistaken, advanced data analytics can lead you astray and you might end suffering a huge loss. So, it can be safely presumed that it is not enough to have good quality data unless you use the datasets efficiently. However, there are many hurdles that businesses might encounter along the way. While implementing a new strategy to strengthen your business with advanced data analytics, mistakes can prevent you from realizing its complete potential. So, in this article, we have summed up some of the common blunders businesses should avoid while developing a big data analytics strategy.

Mistakes to Avoid in Big Data Analytics

Being rigid in processes and products

If you are rigid with your process and product, you are committing a big mistake. You should begin your project in a way that is both strategic in vision and agile in execution. Therefore, you need to pick technologies that are open and expandable. For example, you must avoid vendor lock-in by using open source tools. For obtaining optimum results from advanced data analytics, it is important to foster a culture that fosters failing fast and learning from mistakes. You must avoid letting egos drive your project and understand that if your team experiments on ten things, eight of them might not work. You should get people on board in your data project team who can thrive in this sort of DevOps style of work.

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Neglecting security and governance at the beginning

Today, security and governance are extremely important, as privacy is one of the major concerns in almost every industry. Businesses still tend to start big data analytics projects as pilots, with just a handful of people working on them, and without security and governance baked in. This is a huge mistake when it comes to big data analytics. You must get compliance, governance, and security conversations started on the very first day of the project. You must carefullRequest Proposaly choose the right governance strategies, as well as the right technology of governance.

Relying on the same KPI’s always

As things are constantly changing and your business is subjected to constant dynamics, so you must learn to adapt to the ever-changing environment. This is how you can prosper. So, try not to hold on to the old performance indicators that are used to measure your success in the past. You need to use newer and more suitable tools to make advanced data analytics tools reflect the current performance of your business and identify what really drives your business forward.

Quantzig’s Advantage

Being a leader in offering big data analytics services, Quantzig helps businesses to manage, store, and integrate huge datasets. Also, we help businesses to gain predictive insights that facilitate proactive business decisions and pre-emptive planning. Additionally, Quantzig promises to deliver best-in-class frameworks for multi-dimensional data aggregation and utilizes visualization-based data discovery tools for insight generation.


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IR23

Is Analytics the New Protagonist for Food Safety?

Analytics is helping food producers, retailers, and restaurants to better understand customer needs and uncover important food safety and industry trends.

The food industry encompasses everything from producers and shipping companies to retailers and restaurants, making them one of the world’s largest and most important business sectors. However, with increased globalization in this sector, the tastes and preferences of customers have become more complex than ever before. Furthermore, the rising rate of food safety concerns is putting players in the food industry under constant pressure to meet the expected standards.

Analytics solutions help food industry companies with critical decision-making capabilities in the areas of pricing, product promotion, product development, and demand forecasting. This consequently results in improved product innovation, greater sales effectiveness, enhanced margins and profitability levels, extended customer reach, increased marketing ROI, and greater customer satisfaction and loyalty.

Analytics in food safety 

Reduce food recalls

The case of food recalls can cost a company millions of dollars in direct costs alone, not including lost future sales from the damaged reputation and goodwill. When factoring in medical expenses, productivity loss and mortality, the cost of such unfortunate events become higher. Despite increased regulation, the number of food recalls has continued to rise in the past couple of years. Food safety has garnered greater importance with the growing global complex food supply chain. Though regulation plays a major role, preventing future recalls require improved traceability throughout the food supply chain. The use of analytics solutions helps food companies to track production and operations, improve food traceability, and prevent contamination. Using technologies such as the Internet of Things (IoT), food companies can collect data through sensors embedded within the advanced food packaging. Furthermore, smart packaging provides real-time monitoring and management of food products frContact USom the farm to the table. Having the right data in hand is the key for food companies to stop contaminated products and prevent recalls altogether, eventually resulting in enhanced food safety.

GIS to predict foodborne pathogen

GIS refers to a computer-based tool that is highly useful in mapping and analyzing things on earth. It encompasses a combination of geographical data with attribute data (such as climate conditions, or other characteristics of a location). This technology facilitates food safety as it combines information on geographical features and attribute data that includes the characteristics/information related to a specific location to identify associations between the environment and a pathogen.

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Whole-genome sequencing in food safety

The advent of new affordable whole-genome sequencing is giving rise to a higher resolution of genomic data. Whole genome sequencing has the power to differentiate virtually any strain of pathogens. This capability was lacking in previous techniques such as pulsed-field gel electrophoresis (PFGE). Genomic data is being generated in order to track and trace foodborne illnesses across different food sources, food-manufacturing facilities and clinical cases. With today’s increasingly global food supply and the fact that food products are often multi-ingredient, such robust tools are integral to ensure food safety. They can track food contamination quickly and remove any contaminated food products from the food supply and ensure food safety.


Know more about Quantzig’ s analytics solutions for the food industry in ensuring food safety

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IR28

Big Data Analytics is the New Battleground for Food Delivery Apps

Food delivery apps have become a rage, especially among millennials, owing to the convenience and ease involved in their usage. The increasing tug of war between food delivery apps to retain their market share has forced stakeholders in the food industry to explore new areas of improvement, and this is where big data analytics comes into the picture. Food delivery apps have been employing big data analytics to identify new techniques of customer satisfaction. Let’s look at a few benefits that big data offers to the food delivery apps:

Big Data Benefits for Food Delivery AppsFree demo

 

Accurate Delivery Time Estimates

food delivery apps

Big data analytics helps to collect real-time data such as road traffic, temperature, route, etc.  and provide an accurate estimation of the delivery time to customers. Moreover, big data analytics can predict the impact of these factors on the food items, which helps take preventive measures for damage or wastage.

Operational efficiency

Big data helps firms to understand their customers better and provide the best quality of service. Big data analytics can be put to various uses such as analyzing the impact of temperature on food, the impact of market trends on stock consumption, formulating best practices to improve customer service, etc. This would help food delivery apps to improve their level of operational efficiency and survive in the market.

Understand customer sentiments

Social media platforms have become a part and parcel of everyday life. People take to social media to express their slightest emotions, thoughts, opinions, and complaints. The time has come for companies to maintain a good reputation to retain their customers and prevent the loss of customers through a negative word of mouth. Big data analytics categorizes the customers’ comments into positive, negative, or neutral.

Food delivery apps can use this data to evaluate their customer’s emotions on a scale and take appropriate corrective actions if required.

Personalization

Analyzing the big data collected about the customers can help food delivery apps to understand their customers better. Important customer data such as what they like, the posts shared on social media, their willingness to pay, reviews, etc. help to provide a more personalized and customer-centric service.

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Analyze the market basket

Market basket analysis is related to predicting the most likely behavior of the customer. This analysis is carried out based on the purchase history and the items currently in the cart of the customer. Based on the results of this analysis, combo deals can be promoted to customers, which would entice the customers to purchase more and ensure customer satisfaction by making their ordering decision easier.

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