Data science applications across industries in 2025

Healthcare

In healthcare, early disease detection improves patient quality of life and health outcomes. Some diseases present no visible symptoms until they've progressed to a critical level. Data professionals use big data in these settings to identify and diagnose diseases sooner.

Physicians collect patient data in the office, combine it with historical patient health data and analyze it using tools that detect recurring sequences across the data set. This process, known as pattern recognition, identifies meaningful configurations or arrangements in data.

In practice, this helps physicians to rapidly detect patterns indicative of diseases that they might otherwise miss. With the diagnostic support that pattern recognition provides, physicians can identify diseases more accurately and earlier and even forecast disease progression. Using big data in this way supports preventive care and reduces the need for more expensive or prolonged treatments for advanced disease care.

Transportation and travel

In transportation and travel, moving people and goods from point A to point B is the name of the game, but it's rarely as simple as finding the shortest route. Some travelers might prioritize a comfortable trip, which might mean taking a shorter flight with layovers over longer nonstop flights, even though it might not be as fast. Thanks to the myriad available flight options, figuring out the logistics of these routes can be difficult and time-consuming.

Recommendation and personalization engines powered by big data simplify this process by tapping into historical customer data, shared preference data, collective customer data such as ratings and reviews and data from travel providers to suggest optimal routes for users. They identify which airlines a customer takes most, which hotels they've rated highly, which seat they typically reserve on the plane and logistical patterns such as which route they might prefer based on location.

This saves the user time building the ideal travel experience with much less manual work, and the entire journey can be hyper-personalized to their unique needs.

Retail and e-commerce

Retail and e-commerce often experience demand shifts based on various factors, such as seasonal or economic trends. Retailers that can anticipate when those shifts happen can adjust stock, prepare inventory and stay one step ahead of demand. This is where predictive analytics and modeling come in.

Predictive modeling uses big data to analyze patterns, forecast outcomes and identify trends. In retail, this can take the form of demand forecasting.

For example, an e-commerce storefront can feed historical product data and current consumer behavior data into a predictive analytics tool. Predictive modeling then classifies the data, searches for correlations between variables, identifies patterns within the data set, measures future likelihoods and predicts scenarios that might play out based on that analysis. The insights can guide the e-commerce storefront to restock inventory that will be in demand or to prevent overstocking.

Retailers also use data modeling to inform where stock is placed in a store based on upcoming trends, what stock should be moved in the warehouse to free up space and how prices should be adjusted to meet demand. This is key to optimizing inventory management and maintaining a balanced inventory.

Manufacturing and logistics

Consumers now expect fast, accurate production and distribution of goods, which puts pressure on manufacturers, distributors and the supply chain network to coordinate flawlessly. Big data-powered autonomous systems provide the precision and speed necessary to meet these expectations at scale.

Autonomous technologies perform everything from basic, repetitive tasks to big data analyses to extract insights that optimize operations. For example, IoT devices can track and monitor shipments in real time. Autonomous systems can connect to and interpret this data as it comes in, analyzing it to discover ways to improve delivery times and find efficiencies across the supply chain.

In manufacturing, autonomous systems can gather data from sensors to evaluate machine utilization and monitor the quality of goods produced. This allows the production line to identify areas where effectiveness and quality control can be enhanced. These tools can also flag when equipment is starting to fail, deviate or require maintenance.

From a logistical standpoint, autonomous systems can use production data and supplier network information to sync scheduling and identify dependencies. This narrows the gap between manufacturing and distribution through minute optimizations.

Financial services

Financial services institutions -- especially those that process enterprise-scale transactions -- face constant threats from increasingly sophisticated fraud tactics. These schemes evolve as technology advances, making them more difficult to catch and prevent. Fraud can result in financial losses as well as reputational damage.

However, financial services institutions also have technology at their disposal that can fight back against fraud. For example, anomaly detection is the process of identifying data points that fall outside the normal range in a data set. These tools use big data analytics to correlate factors, such as the size of transactions, location and time, while identifying patterns and flagging suspicious activity. Suspicious activity could be a transaction in New York taking place a few minutes after a transaction in London. This is theoretically impossible and would be flagged as an anomaly and a sign of potential fraud to investigate.

Anomaly detection tools also identify fraudulent activities on a larger scale. For example, a financial institution analyzing big data sets might reveal money laundering schemes by looking for certain patterns. The number-crunching power of big data and anomaly detection tools is key to catching outliers and highlighting any action that deviates from the norm, forming the basis for fraud prevention.

Energy, oil and gas

The energy industry faces challenges in resource exploration and extraction efficiency, especially in the oil and gas sectors. Finding new oil and gas reserves is necessary to keep the supply flowing, and extracting those resources effectively maximizes the supply, which ensures that demand can be met. Big data is essential to improving this efficiency.

Seismic data, geological data and subsurface data can be fed to advanced analytics tools to identify and predict the location of oil and gas pockets. This improves the accuracy of resource exploration by narrowing potential locations. In addition, subsurface data can provide insight into recovery rates, which reduces the risk of running into dry wells.

During extraction, sensors collect real-time data on the angle of the drill and reveal drilling pressures and environmental data. Big data analytics tools interpret this data and optimize drilling procedures to minimize damage to the surrounding ecosystem, reduce the risk of drilling complications and improve the overall efficiency of recovery.

Aerospace

Aerospace engineering demands precision and accuracy. A single incorrect value can disrupt a flight system's performance, reduce operational efficiency and compromise flight safety with collision avoidance systems and atmospheric hazards detection.

Machine learning (ML) is a key technology for aerospace optimization and can be applied across a variety of use cases in the industry. For example, sensor data can help flag aircraft equipment maintenance requirements and failure potential, identify commonalities in weather patterns to improve flight paths, search for efficiency opportunities in component manufacturing, and monitor air traffic and hazard reports to enhance flight safety.

These models need high-quality big data to provide reliable results. Collecting the data is rarely a challenge with the variety of IoT and sensor technology available, but often much of the data is unstructured or raw, making it unusable. Data classification and categorization organize data variables into searchable groups and classes based on certain characteristics and make it easier to retrieve data.

Organizing, classifying and categorizing data turns unstructured big data into usable fodder for ML algorithms.

Insurance

Filing an insurance claim is time-consuming and data-intensive. Reporting a claim to an insurance agent often means repeating a lot of the same information in different places and waiting for them to record it, which leads to customer service bottlenecks. Replacing this with a self-service process can save time for both customers and agents.

Conversational systems accomplish this task by using a combination of big data, AI and natural language processing technology to create AI chatbots and virtual assistants. These tools interpret customer inputs and perform relevant actions in response. For example, a customer can initiate a claim using a chatbot on the insurance company's website, and the system will record details such as date, time, location, receipts and photos of the incident.

Insurance companies also automate much of the claims process. Conversational systems can store data for later review by human agents and cross-check information with the database to validate policies, report the status of a claim and notify customers about coverage changes. As these systems collect data over time, they improve and deliver even better customer service.

Management consulting and professional services

Consulting services primarily focus on assessing customer experience (CX) and satisfaction by gathering feedback and evidence of the success of business products or services. This data shapes their strategy moving forward and identifies areas for improvement. Management consulting and professional services organizations often implement behavior and sentiment analysis techniques to collect this data and extract insights from it.

Behavior and sentiment analysis focuses on studying how customers feel, react and engage with a business, whether that's related to a website, product or service experience. It can evaluate customer emotions and expectations as well as a brand's reputation. To gather the big data that powers this analysis, consulting agencies might send out surveys to customers, collect social media data like interactions per post, and interview current and prior customers.

Once they gather a large volume of data, sentiment analysis tools assign sentiment scores measuring a variety of factors related to customer behavior and interactions. This can help paint a picture of how well a business's actions are resonating with the intended audience, and consulting agencies can use this insight to create new, actionable strategies. The insights can also help shape metrics that future data can compare against to evaluate performance over time, encouraging continuous improvement.

Jacob Roundy is a freelance writer and editor with more than a decade of experience with specializing in a variety of technology topics, such as data centers, business intelligence, AI/ML, climate change and sustainability. His writing focuses on demystifying tech, tracking trends in the industry, and providing practical guidance to IT leaders and administrators.