Big data examples, applications and use cases

Big data use cases are distinct situations in which organizations collect, process and analyze big data to complete tasks and achieve goals. Such use cases include fraud detection, disease risk modeling, dynamic pricing, supply chain optimization and targeted marketing campaigns.

Big data consists of massive, complex datasets, and big data use cases are the concrete manifestation of their power and value. Organizations use big data analysis to identify patterns, optimize business processes, forecast trends and inform business decisions. Big data solutions deliver competitive advantages such as greater operational efficiency, better customer experiences and improved risk management in a range of fields and industries, including:

• Financial services
• Healthcare
• Retail
• Manufacturing
• Entertainment
• Government

Critical to the success of big data applications are big data technologies. Businesses and data science professionals use data processing, data integration and advanced analytics tools and platforms to manage and unlock value from vast amounts of data. Leading analytics tools and platforms include capabilities powered by artificial intelligence (AI) models, which are also trained on big data.

Before exploring big data use cases, it’s helpful to understand big data and the processes that operationalize it.

Big data refers to massive, complex datasets that traditional data management systems cannot handle. Generally speaking, five key dimensions known as the “V’s of big data” distinguish big data from traditional data:

• Volume: Datasets for big data are considerably larger, containing terabytes’ worth of information
  
  
• Velocity: Big data moves fast and can be used for near real-time analysis
  
  
• Variety: Datasets include different data formats, including semi-structured and unstructured data
  
  
• Veracity: Big data’s complexity requires robust data quality measures
  
  
• Value: Analyzing big data yields actionable insights

Big data analytics, as the name suggests, refers to the processing and analysis of big data to extract value. Its objectives are to uncover trends, patterns and correlations in large amounts of raw data to support data-informed decisions. Big data analytics encompasses sophisticated data analysis techniques, including:

Big data analytics tools and platforms often incorporate AI, including machine learning, to rapidly interpret large volumes of data, identify patterns and deliver predictions that result in better decisions. Leading analytics software features data visualization, interactive dashboards, predictive modeling and AI-powered forecasting tools.

In addition to big data analytics tools and platforms, other key technologies underpin big data management and modern data-driven decision-making.

Common open source data processing tools in this space include Apache Kafka and Apache Hadoop. Apache Kafka supports the creation of real-time data streaming pipelines that reliably move millions of records between enterprise systems, while the Hadoop framework is often a top choice for batch processing and linear data processing.

Data integration tools help unify datasets from different sources, creating a single, comprehensive view that supports analysis. Hybrid, open data lakehouses can automate and streamline the ingestion and enrichment of unstructured data at scale. This allows for the unification of unstructured data with structured data into datasets ready for analysis.

While big data use cases are a relatively recent phenomenon, data analysis use cases predate the advent of modern computing. For instance, in the early 1900s, engineer Frederick W. Taylor collected data on manual labor processes to inform a school of thought known as “scientific management,” which was later used to optimize manufacturing at American carmaker Ford’s famous Model T assembly lines.¹

Decades later, advancements in computer science and data engineering helped data analysis become a mainstay of modern commerce. Business users could track key datapoints such as customer data, financial transactions and inventory levels, and deploy classic statistical methods such as time-series analysis and linear regression to inform decision-making. Government leaders and employees also became avid users, with agencies like the US Department of Veterans Affairs pioneering analytics for healthcare records.²

As data generation increased exponentially in the digital age, organizations sought to leverage newer data sources—including the Internet of Things (IoT) and sensor data, social media, e-commerce transactions and more—for business intelligence and decision-making functions.

These large volumes of complex, often unstructured or semi-structured data became known as big data. While use cases for traditional data persisted, new use cases emerged to harness the applications of big data.

At banks, brokerage firms and fintechs around the world, big data analytics has become a staple of everyday operations. Common examples of big data use in financial services include:

Big data analytics, in combination with machine learning, helps financial institutions analyze millions of transactions. The analysis can quickly flag unusual patterns and customer behavior that could signify credit card fraud, identity theft or other fraudulent activity.

Instant detection can mean expedient intervention: The global financial services firm JP Morgan Chase, for instance, developed a real-time fraud detection system that delivered a 50% reduction in fraud-related losses within its first year of implementation.³

Financial firms rely on machine learning models that use big data—from historical equities data to sentiment data scraped from the Internet—to predict share price movements, helping to inform investment decisions and power high-frequency trading.

Some lenders are using big data analytics to take a more holistic approach to evaluating the creditworthiness of potential borrowers. Instead of relying solely on traditional metrics such as prior loan repayments, companies are also incorporating information such as income, rent and utilities payments, and bank account transaction history.

Big data analysis is proving transformative in the healthcare industry, supporting more accurate diagnoses, better treatments and cost savings. Use cases include:

Researchers are using AI-powered models to help predict the risk of certain diseases and conditions. For example, researchers in China studying cardiovascular disease created a machine learning-based risk model based on variables such as age, sex, blood pressure, medications prescribed and various laboratory test results.

Their model, which was trained on data from more than 150,000 people, predicted the risk of cardiac events such as heart attacks and strokes in individual patients—valuable information that could inform the work of their healthcare providers.⁴

Big data collected from electronic health records, insurance records, smartphone apps, wearable devices, genetic testing and other sources is helping to improve healthcare quality and lower costs. Analysis of such data can help clinicians tailor effective treatments specific to individual patients.

This approach can help circumvent time-consuming and expensive “trial and error” processes, which entail prescribing one medication after another to a patient until a treatment proves successful.⁵

The retail sector is known to be rich in data. Information on consumer behavior comes from sales records, surveys, loyalty programs, checkout scanner data, e-commerce activities and more—and retailers are putting it to work through the following use cases:

Customer segmentation is the process of dividing consumers into groups based on behavioral, demographic, geographic and psychographic characteristics. Such groupings allow retailers to engage in more precise, personalized marketing to consumers with new product offers and services designed to appeal to specific groups. Big data makes segmentation strategies more powerful, providing more information about segments and even enabling the subdivision of groups into micro-segments.

For example, MOL, a European fuel retailer with 2,400 service stations, leveraged data from its loyalty program—encompassing millions of monthly transactions—to create micro-segments of consumers. MOL designed the micro-segments around customers’ purchases of select products, such as coffee, and then targeted them with personalized communications.

Those communications ultimately generated returns that were three times higher than those of general communications, while customer satisfaction levels rose to be 20% higher than those of their competitors. Learn more about MOL’s journey.

Dynamic pricing is the adjustment of prices in real time based on demand, competitor pricing and customer preferences. It allows retailers to optimize revenue by offering deals to customers during low-traffic periods and increasing prices when demand is high.

While dynamic pricing can be based on smaller datasets, dynamic pricing models powered by big data and AI have led to lucrative results for e-commerce pioneers like Amazon. AI and big data-based dynamic pricing have also become more common in the travel industry: In July 2025, Delta Air Lines announced its implementation of a generative AI model for ticket pricing.⁶

Modern manufacturing operations generate data from thousands of sources, including Industrial Internet of Things (IIoT) sensors, production systems and supply chain management systems. Combined with big data analytics, product manufacturers are using this information to bolster their bottom lines through important uses such as:

Manufacturers apply predictive analytics to big data to identify inefficiencies, bottlenecks and quality issues. This information allows companies to take preemptive actions to address equipment failures, system downtime and product defects, as well as to optimize maintenance schedules.

For example, computer vision models trained on vast datasets of images can automate the detection of flaws in raw materials or manufactured components, resulting in issues being identified and resolved more efficiently in comparison to manual-based processes. At PepsiCo’s Frito-Lay plants, computer vision systems assess the characteristics of the potatoes used for the brand’s snack foods—a process that generates savings of over USD 300,000.⁷

As with production data, big data analytics can be applied to supply chain data to identify bottlenecks. It can also help optimize shipping routes and track real-time information on inventory.

For instance, truck parts distributor FleetPride used data mining and predictive analytics to transform their warehouse operations and shipping processes, leading to changes that doubled productivity and reduced shipping costs.

Before a product is manufactured and shipped, it must be designed—and designed well. Companies can harness big data to identify opportunities for product improvements. That was Honda’s objective when the automotive giant took advantage of newer sources of big data—including vehicle diagnostics and telematics, smartphones, biometric sensors—to support the work of its engineers.

By unlocking insights from these huge datasets, the company’s engineers gained a better understanding of driver behavior and vehicle performance. In partnership with IBM, Honda trained its engineers on data analytics tools and techniques such as data mining, enabling the regular use of big data analysis in their work. The results, Honda officials say, are “better, smarter, safer automobiles.”

As with other industries, the digital transformation of entertainment paved the way for a monumental increase in data generation, data analysis and efforts to monetize them. Use cases include:

Major streaming platforms such as Netflix famously analyze big data related to subscriber viewing habits—including shows watched, device use and language preferences—to offer recommendations for programs that individual customers might enjoy. Netflix credits its deep learning-based recommendation system for increasing member retention over the years.⁸

While big data supports customer segmentation and personalized marketing strategies, it can also be featured as the subject of marketing campaigns. Such is the case with Spotify’s popular “Wrapped” series: Near the end of each calendar year, the music streamer releases personalized recaps of each user’s listening habits as well as global trends in music and podcast streaming.

Big data use cases in government often overlap with use cases in other industries. For instance, like financial institutions, financial regulatory authorities use big data analytics to detect anomalous and potentially fraudulent activities. And, similar to independent healthcare researchers and medical providers, government health agencies incorporate big data analysis in studying diseases. Other use cases, however, are more unique to government bodies:

In the United States, proposed federal regulations are subject to comments submitted by the public. In 2021, the federal CDO (Chief Data Officer) Council collaborated with federal agencies to use natural language processing to categorize and analyze the millions of public comments submitted regarding proposed federal regulations. The pilot program, the council reported, resulted in more efficient analysis and better insights.⁹

Government smart city initiatives rely on big data-driven insights to improve urban planning and the deployment of city services—ranging from waste management to public transportation—leading to better quality of life for residents. Local governments around the world, from Chattanooga, Tennessee to Zhejiang Province, China, have embraced a variety of data-driven smart city solutions.

For all the benefits of big data and big data use cases, businesses should also consider the potential challenges of big data applications. These include: