10 common big data use cases for data leaders to enable

1. 360-degree view of customers

Organizations commonly generate vast amounts of customer data across multiple channels. Websites, mobile apps, social networks, connected devices, contact centers and in-store systems all contribute to an expanding data trail. By integrating and analyzing this data, companies gain a more complete understanding of customer behavior, preferences, intent and issues, often updated in near real time.

They use those insights to boost customer acquisition and engagement through targeted marketing and personalized recommendations, websites and services, all based on individual interests and behavior patterns. Organizations can also spot trends and problems earlier and identify why customers leave, enabling stronger retention strategies. AI amplifies these capabilities by surfacing complex patterns in datasets and creating optimized promotional content and customer communications.

The business payoff of a 360-degree customer view built on big data: increased sales, stronger brand loyalty and higher customer satisfaction.

Industry examples

Amazon, Walmart and other retailers use advanced analytics and AI powered by customer data to personalize marketing and UX, enhance customer service, optimize product offerings and improve website and brick-and-mortar store layouts. Insights into how customers navigate websites and stores, the products they buy, items removed from online shopping carts and product or customer service issues inform the entire retail operation.

Streaming services such as Netflix, Disney+ and Amazon Prime Video rely on AI models that analyze behavioral data and dynamically adapt viewing recommendations to drive increased customer engagement. Financial services firms also analyze customer data to personalize product offerings and marketing campaigns, while manufacturers use it to prioritize product development plans.

2. Demand forecasting and price optimization

Companies can detect business patterns and emerging trends early by analyzing data on sales, inventories, supply chains, customer behavior, pricing, promotions and marketing. This helps them improve demand forecasts, optimize pricing and respond more effectively to business changes and market volatility. Additional inputs on economic conditions, seasonal patterns, external events, weather forecasts and competitors further enhance forecasting accuracy and pricing precision.

Industry examples

Retailers and manufacturers use big data to drive more informed decisions as they manage fluctuating demand, pricing pressures and supply chain disruptions. For example:

• Walmart generates about 10 petabytes of data every day from customer visits, purchases and interactions, both in stores and online. The retailer processes the data in real time to enable dynamic inventory management and demand planning and to optimize product selection, pricing and promotions.
• Amazon runs advanced mathematical models to forecast demand in real time for the hundreds of millions of products sold on its websites worldwide. It uses the forecasts to optimize product purchases from suppliers and determine where to stock products across its hundreds of warehouses to meet delivery promises.
• Manufacturers use demand forecasts created with big data to develop more accurate production plans. Identifying potential production shortfalls enables them to proactively adjust manufacturing schedules and increase purchases of components and raw materials in time to avoid stockouts and lost business opportunities.

3. Operational efficiency

Big data plays a central role in efforts to increase operational efficiency. Organizations use it to identify inefficiencies, bottlenecks and redundant processes, enabling them to improve processes, eliminate wasted work and allocate resources more effectively. This can be done faster than with periodic reports: real-time analytics, interactive data visualizations and AI-driven automation empower business managers to respond to operational issues as they arise.

Industry examples

Increasing operational efficiency is a common use case for big data across industries. The following are some notable examples:

• Manufacturing. The companies that previously made up General Electric -- now separated into standalone aerospace, energy and healthcare businesses -- analyze sensor and operational data from their manufacturing facilities to optimize production plans, reduce equipment downtime and improve asset utilization.
• Logistics and transportation. UPS continuously analyzes traffic data, vehicle telemetry, weather conditions and package volumes to optimize routing, which improves delivery times, reduces fuel consumption and lowers operating costs.
• Retail. Target analyzes point-of-sale data and customer traffic patterns across its 2,000-plus stores to predict business volume at different times of the day, enabling it to optimize staffing levels, reduce checkout wait times and avoid unnecessary labor costs. Amazon uses machine learning and other advanced analytics on order and warehouse operations data to optimize workflows and pick paths in its fulfillment centers worldwide.

4. Fraud detection and prevention

Businesses that process high volumes of transactions use big data systems to detect and block fraudulent ones. In addition to the transaction data itself, they analyze device, geolocation, account, identity and customer behavior data to flag potential fraudulent activity in real time. Companies also incorporate data from blocklists, credit bureaus, identity verification services and other external sources. Fraud detection and prevention applications now rely on AI and machine learning models that adapt as fraud patterns evolve, rather than on static rules that become less accurate over time and are hard to manage. Using the models helps reduce fraud rates while minimizing false positives that frustrate legitimate customers.

Industry examples

Financial services firms, payment processors and insurers all do real-time processing and analytics on large, complex datasets to identify and mitigate potential fraud issues in transactions. For example, PayPal has developed machine learning models that use more than 500 data points to evaluate billions of transactions each month. The models dynamically generate fraud risk scores along with recommendations on whether payments should be approved, blocked or reviewed.

5. Threat detection and response

As organizations expand their IT infrastructure across cloud, hybrid and edge environments, the volume and complexity of security-related data have increased dramatically. Big data platforms enable security teams to gather and analyze streams of telemetry data, including network traffic, endpoint activity, application logs, and identity and access management events. Many organizations also augment internal datasets with external threat intelligence. Advanced analytics and AI models help teams correlate cybersecurity signals across systems to detect anomalies, identify threats and prioritize response efforts based on business risk.

Industry examples

Not surprisingly, threat detection and response is a common big data use case across industries. For example, large financial institutions analyze trillions of security events each year to identify cyberthreats, prevent account takeovers and protect customer data from attackers. Healthcare organizations use big data analytics to protect sensitive patient data and meet regulatory requirements for data security and privacy.

6. Risk identification and management

Anticipating and proactively responding to rapidly evolving risks is critical to business success. Big data platforms enable organizations to ingest and analyze risk-related information from a wide range of internal and external sources, including operational and supplier data, market signals, weather data, geopolitical intelligence and regulatory updates. By synthesizing these inputs, business and risk management leaders can gain earlier visibility into emerging risks, better quantify potential exposure and model different response scenarios, from risk avoidance or mitigation to acceptance.

Industry examples

Supply chain management and logistics companies are particularly adept at using big data to identify and manage the potential risk of supply shortages or excess inventory due to natural disasters, changes in buying behavior and other factors. Financial services firms use risk models driven by big data to assess credit risk, market exposure and operational vulnerabilities, often across global portfolios. Such models have also proven their value across a range of other industries.

7. AI-enabled unstructured data analysis

A significant portion of enterprise data is unstructured -- text, images, video and audio. Despite its potential value, unstructured data is often underutilized -- or not used at all, turning it into dark data. But advances in natural language processing (NLP), computer vision and generative AI (GenAI) make it easier for organizations to extract insights from unstructured data at scale for applications such as analyzing customer sentiment, moderating user-generated content and supporting intelligent search in document repositories.

Industry examples

Since big data technologies first emerged, online platforms such as Amazon, Google, Meta, Netflix and Shopify have analyzed large volumes of unstructured data to underpin content moderation, targeted advertising, personalized recommendations and UX optimization. Now, enterprises in various industries are applying similar analytics techniques.

Analyzing call transcripts, chat sessions and support tickets in contact centers helps financial services firms, telecom companies, healthcare providers and other organizations identify common issues, detect customer frustration and improve both service quality and overall CX. In addition, healthcare organizations increasingly use AI-driven analysis of clinical notes, medical images and physician reports to improve medical diagnoses, support clinical decision-making and track population health trends.

Financial services firms also apply NLP models to recorded customer calls and written communications to support compliance monitoring and detect potential misconduct. In law firms and other enterprises with vast document repositories, AI-powered search and summarization tools help employees find relevant information faster and reduce manual review efforts.

8. AI and intelligent automation applications

Machine learning, automated decision-making, GenAI and agentic AI systems all depend on large, diverse and continuously updated datasets to function effectively. Organizations are increasingly using big data platforms to support AI chatbots and agents that accelerate information retrieval and insight generation, augment human decision-making and streamline complex workflows. Building partly on the ability to access and analyze previously dark data, the AI applications often draw on both structured and unstructured data from across the enterprise and, in many cases, external sources.

Industry examples

Clearly, this has become a dynamic -- and transformative -- use case across industries. Machine learning applications such as predictive analytics, anomaly detection and pattern recognition have been widely implemented by businesses. Using these foundational capabilities, banks and investment firms are notable examples of organizations that have invested heavily in data platforms to support AI-driven analytics and decision-making. These systems automate credit approval decisions, customer service interactions and detection of anomalous transactions or trading behavior.

Insurers similarly use intelligent automation in the underwriting process, including dynamic policy pricing. Manufacturers are also applying AI at scale, using data from sensors and production systems to optimize production processes, improve quality control and automate decision-making in factories.

9. AI governance and model lifecycle management

As AI adoption accelerates, deploying AI models and agents is only the beginning. Ensuring that AI systems are trustworthy, explainable and aligned with corporate policies and regulatory requirements is now a big challenge for organizations. Big data platforms play a central role in enabling scalable AI governance and model lifecycle management. They provide the foundation for collecting, storing and analyzing AI outputs, audit logs and feedback loops to monitor and control the behavior of AI systems.

Industry examples

Organizations across industries use big data environments to detect bias, fairness issues and unintended outcomes in AI systems and to track model performance, drift and degradation. For example, financial services institutions apply big data analytics to monitor AI models used in credit scoring, fraud detection and risk assessment for accuracy, explainability and regulatory compliance. Healthcare and life sciences organizations do the same to govern AI models used in research, clinical trials and manufacturing.

10. Predictive maintenance

Predictive maintenance is one of the most mature and high-ROI big data applications, particularly in asset-intensive industries where unplanned equipment downtime can result in significant financial loss, safety risks and production or service disruptions. Sensor readings, machine logs and other types of operational data can provide early warning signs of equipment degradation or failure, enabling organizations to take preventive action before problems occur.

Industry examples

Along with manufacturers, the energy and transportation industries are particularly susceptible to equipment-related outages and have invested in predictive maintenance systems powered by big data.

For example, Shell uses big data analytics and AI to monitor more than 13,000 pieces of equipment globally and detect early indicators of potential failures or performance degradation that could affect its drilling, pipeline and refinery operations. The oil and gas company is now also deploying AI agents to automate root cause analysis and remediation of maintenance issues.

Deutsche Bahn, Germany's national railway company, similarly uses AI-driven analytics to anticipate maintenance needs on trains, switches and signaling systems for reduced service disruptions and optimized maintenance scheduling.

Ron Schmelzer is the founder of Scalebrate, a platform that provides content, a community and tools to help "microteams" and solopreneurs scale their business. He also hosts the Small Team Big Scale Podcast.