What is real-time data?

Real-time data is information available for processing and analysis immediately after it is generated or collected, often within milliseconds.

Real-time data is the driving force behind fast decision making—which is critical to staying competitive in today's business climate. Organizations use real-time data to power real-time analytics, allowing them to access actionable insights with speed and confidence. According to 2025 IDC data, surveyed enterprises indicate that 63% of use cases must process data within minutes to be useful. 

Across enterprises, real-time data helps accelerate fraud detection, optimize supply chains, personalize customer experiences and manage risk. And in the era of artificial intelligence, real-time data has proven essential for effective AI systems. AI models perform best with fresh, relevant data. Without it, they may make decisions based on outdated information—essentially, yesterday’s reality.

Real-time data can come from a variety of sources, including:

• Internet of Things (IoT) devices and sensors
• Mobile apps
• Transportation systems
• Weather forecasting services
• Financial markets
• Social media platforms
• Sports databases
• Cybersecurity intelligence platforms
• Point-of-sale and e-commerce systems

Application programming interfaces (APIs) can help automate the transmission of real-time data from various sources to data pipelines for processing and storage.

Today, using historical data and dated information—even data collected as recently as the day before—to make informed decisions just isn’t good enough.¹

But that’s exactly what enterprises are often forced to do when contending with traditional data processing approaches—namely, batch processing—for data-driven intelligence. Through batch processing, tasks are gathered during certain intervals and are eventually run in batches at specific times, such as overnight.

While batch processing is a valuable tool for tasks that are not time-sensitive, such as routine reports, it hinders the ability of businesses to derive immediate insights. For instance, a bank relying solely on batch data processing as part of its fraud detection program may not be notified of a suspicious financial transaction until well after a significant loss has occurred.  

The development of low-latency technologies that can process data instantly—what’s now known as real-time data—has revolutionized the speed at which businesses can respond to changing conditions and execute business intelligence initiatives.

Revisiting the fraud example: Real-time data processing supports real-time data analysis (also known as real-time data analytics) of financial transactions, alerting banks to suspicious activities as soon as they occur. This, in turn, gives banks the opportunity to intervene quickly and prevent major losses, safeguarding client assets.

The increasing adoption of artificial intelligence further amplifies the significance of real-time data. Up-to-date, high-quality data is often integral to AI and machine learning-powered workflows.

For example, AI-driven diagnostic models require current patient data to detect possible medical conditions, while e-commerce chatbots are equipped with real-time inventory information to effectively answer shoppers’ questions about available products.

Agentic AI, in particular, leverages real-time data to support autonomous decision-making. For instance, a shipping business might use agentic AI to automatically adjust delivery routes in response to real-time traffic conditions.

Enterprises that leverage real-time data reap multiple benefits, such as:

Real-time data, near real-time data and streaming data are often used interchangeably, but the terms do bear subtle distinctions.

While real-time data is available instantly after its generation or collection, near real-time data can take minutes or even hours to be accessible for analytics or other purposes.

For instance, NASA considers near real-time data to be data that’s available one to three hours after being captured by an instrument about a space-based platform.²

In contrast, Forrester describes data for near real-time analytics as being available in under 15 or under 5 minutes, depending on the data source.³ (It’s important to note that when the latency associated with data delivery is just a few minutes, it might be characterized as “real-time” even if it’s actually a near real-time process.)

Streaming data, also known as real-time data streaming, refers specifically to data that is continuously generated and flows into data pipelines from various sources. Typically, this data is real-time data, such as IoT device recordings or social media activity.

However, not all real-time data is necessarily streaming data: Real-time data that is not part of a continuous flow—instead, it’s produced and transmitted as an individual event—is not considered streaming data. A mobile phone user using an app to share their current location with a friend once (instead of continuously) could be considered an example of non-streaming, real-time data.

A collection of data management processes and tools can help organizations manage real-time data pipelines.

Data ingestion is the process of collecting and importing data files from various sources into a database for storage, processing and analysis. Real-time data ingestion refers to collecting data from different sources with minimal latency. Leading tools for real-time data ingestion include Apache Kafka and AWS Kinesis.

Data processing is the conversion of raw data into usable information through structured steps such as data collection, preparation, analysis and storage. Real-time data processing entails the execution of these steps as soon as the data is generated or collected. Popular frameworks for real-time processing include Apache Hadoop and Spark.

Stream processing can be considered a form of real-time data processing. In stream processing, data is processed while it is “in motion.” Transformations such as filtering, enriching and formatting take place as data flows through the data pipeline. Frameworks such as Apache Flink enable organizations to process complex events in real time and perform data aggregation at scale.

Real-time data integration involves capturing and processing data from multiple sources as soon as it's available, then immediately integrating it into a target system. Real-time data integration tools and methods include stream data integration (SDI), change data capture (CDC), application integration and data virtualization. Tools and platforms for streamlining real-time integration include Apache Kafka and IBM Streamsets.

Data analytics is the querying, interpretation and visualization of datasets. Real-time data analytics entails performing these tasks on datasets as data is generated, resulting in real-time insights that can inform better decisions. Real-time analytics tools rely on real-time data ingestion, data processing and data integration as well as storage methods optimized for analytics solutions, such as cloud-based data warehouses.

Real-time data supports important processes and functions across different industries.