Tag: Importance of Big Data Analytics

data and analytics

Evaluating the Importance of Data Analytics and Visualization for Businesses

When it comes to successfully positioning your organization in the competitive market, data analytics and visualization act a key ingredient. Data visualization using appropriate data analysis methods can provide valuable insights into business operations. Such insights can be incorporated by businesses in the decision-making pipeline to make data-driven decisions. Leveraging such data analytics and visualization solutions can help companies to analyze their data irrespective of the size, scale, type, and domain and improve their decision-making capabilities.

Data analytics and visualization have become a necessity for companies to manage risks and comply with the changing regulatory landscape.

Therefore, it is imperative for businesses to realize the importance of data analytics and visualization as visuals are immensely capable of predicting all possible outcomes and make complex decisions to yield favorable outcomes. In this article, our team of experts has highlighted how data analytics and visualization techniques can be utilized to make the most of the statistical data, old experiences/learnings, and fresh ideas to launch new products.

Extracting insights from disparate datasets can be a difficult task for companies. But data analytics and visualization techniques can help. Request a FREE demo today!

How can data analytics and visualization help businesses?

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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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Zero Waste Solution: Effective Food Waste Management with Data Analytics

Food waste management is a matter of global concern. According to recent estimates, roughly one-third of the food produced in the world for human consumption every year — approximately 1.3 billion tons — gets lost or wasted. This contributes to the emission of greenhouse gases from landfills. Food waste occurs across the entire food system ranging from producers, retailers and restaurants, to consumers. Forward-thinking businesses are using advanced technology such as data analytics to tackle the issue.

Using big data and data analytics to collect real-time operational data throughout the food waste disposal process allows visibility into the organic waste stream. This further facilitates businesses to identify inefficiencies in food management processing and helps initiate process improvements to create immediate impacts. Measuring and optimizing food waste management not only supports environmental directives but also forms the key to finding operational efficiencies, enabling a business to make informed decisions about purchasing, production or other logistical needs.

How can data analytics help in food waste management? 

Retailers and other businesses dealing with food products are increasingly turning to data analytics solutions in order to manage the food wastes. The information collated and analyzed using data analytics reveals the waste generated by the business and seasonal change in demands, helping business to better plan their food waste management strategy.

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Optimum inventory level

Analyzing sales information, weather forecasts, and seasonal trends, help manufacturers to identify an optimum inventory level which they can then use to reduce the effects of food wastage. Predictions of consumer demand during a particular time can then be made and promotional plans and sales approaches can be structured around sell-by and expiry dates. This is primarily intended to cut down the food wastage and the knock-on environmental and cost issues that arise.

Predict changes in demand

Data analytics can be used to identify seasonal changes in consumer demand for food products. This helps retailers or restaurant chains to plan what quantity of a particular food item must be produced or procured, consequently leading to reduced wastes and better food waste management. Data analytics also helps rRequest Proposaletailers determine the products that are closer to expiry and aggressively reduce the prices on such items so that they are consumed before their expiry date and not wasted.    

An example of analytics in food waste management

A notable success story for using analytics in food waste management is that of the British multinational groceries and general merchandise retailer – Tesco. The company uses a data-driven approach to reduce food waste and ensure effective food waste management. Tesco’s systems order approximately 110 million pounds of food products every day. So, the retailer turned to data analytics to improve the supply chain and minimize the instances of food wastage. Their systems utilize large amounts of data from its many store locations to develop, train, and test their algorithms. They utilize weather forecasts to increase their accuracy in predicting how the demand for food will change. Common sense tells you the seasonal change in people’s demand patterns. Data tells you exactly how much the change is and plan the inventory accordingly. This method helps minimize food waste by ensuring the right quantity of food products are available at each location. In addition to reducing waste and ensuring better food waste management, these initiatives have a positive economic impact for the retailer as well.

               


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Quantzig’s Big Data Analytics Helps a Leading Car Battery Manufacturer Blend Data with Inventory and Order Backlog Data-Sets to Increase Periodical Margins

Case Overview

QZ Banner SquareA leading car battery manufacturer had access to high-level, manually-generated summarizations of product sales information, with almost no ability to drill deep into the sales numbers to understand the performing products, product families, and geographic regions. As a result, the car battery industry client approached Quantzig to help them overcome their predicaments. During the course of the big data analytics engagement, the client evaluated future business decisions accurately through big data analytics and reporting.

Client Profile

The client – a leading manufacturer in the car battery industry segment is based out of the United States. The company has both manufacturing and recycling plants located across the U.S. and Europe.

Predicaments

The car battery industry client was facing predicaments in terms of their capability in limiting the company’s ability to push out excess inventory timely to high sales regions to maximize profits and understanding the importance of big data analytics. Additionally, most of the required data were either missing or needed manual cross-checking from CRM systems.

Solutions Delivered

With the help of Quantzig’s big data analytics engagement, the car battery industry client was able to outline a global cost and average selling prices across product lines and associated materials, blend data with inventory, and order backlog data-sets to increase periodical margins. Additionally, the client gained unparalleled visibility into their own internal sales and manufacturing data.

About Quantzig

For more than 15 years, we have assisted our clients across the globe with end-to-end data management and big data analytics services to leverage their data for practical decision making. We have also worked with 120+ clients, including 55+ Fortune 500 companies and helped them understand the importance of big data analytics.

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