From fraud detection, stock trades, and multi-player games, to personalized shopping recommendations, the most important asset businesses can have is accurate, up-to-the-minute data. Learn what real-time data is, how it works, and benefits, with real-life use cases, and real-time data analytics solutions for businesses big and small.
Real-time data (RTD) refers to information that is processed, consumed, and/or acted upon immediately after it's generated. While data processing is not new, real-time data is a newer paradigm that changes how businesses run.
In previous years, batch data processing was the norm. Systems had to collect, process, and store large volumes of data as separate functions before data could be utilized for further action. In situations where real-time data or analytics are not needed, batch processing is still a viable process.
In contrast, real-time data processing (or streaming data) can collect, store, and analyze continuously, making data readily available to the end-user as soon as it's generated with no delay.
While databases and offline data analysis remain valid tools, the need for real-time data has increased exponentially with the advent of modern applications. After all, the world isn’t a batch process - it runs in real-time.
So why is real-time data important? The most important asset businesses can have is accurate, timely data. From consumer behavior, inventory tracking, and social media feeds, to risk mitigation, the ability to leverage real-time insights, performance, and trends within seconds or minutes vs days or months are what make businesses successful and competitive. Here are five benefits of real-time data.
1. Customer Satisfaction: Real-time data improves customer experience by enabling services to become more flexible, dynamic and interactive. Today, customers expect personalized experiences on their mobile devices. Advertising and recommendations must be tailored to customer preferences in the moment. Rules engines can combine customer data with channels and content to enable interactive experiences. Chatbots can talk to customers and offer products appropriate to their needs. In the future, Augmented Reality and the Internet of Things will create opportunities for businesses to interact with customers in new ways.
2. Business Intelligence: Real-time data can help managers to visualize key performance indicators on dashboards and intervene in areas where they can be most effective. Business intelligence can enable banks to customize their risk models and make quicker decisions about loans. Customer Relationship Management systems can join machine learning capabilities with customer data to build decision-making engines and content management systems that improve the profitability of each customer.
3. Business Development: Real-time data enables businesses to understand their markets and respond quickly with new business models, products and services. Google was able to transform itself into an advertising giant because of its data. Uber is able to match customers to drivers, thanks to GPS streams. Social networks and dating apps have capitalized on the human need for social bonding. Sales organizations use data to predict customer behavior and identify cross-selling opportunities.
4. Operational Intelligence: Real-time data can empower companies to optimize their supply chain and operational processes. For example, supermarkets can manage their inventories in real-time, saving millions of dollars for consumers. Investment banks can buy and sell financial instruments using artificial intelligence. Manufacturers can improve their production schedules and reduce costs.
5. Real-Time Analytics: Real-time data can be combined with data analytics and machine learning to unlock new business use cases. We have already mentioned decision-making engines and automated trading systems. Another example would be predictive maintenance in a power station. By monitoring the vibrational modes of turbines in real time, the operator can predict mechanical failures before they happen. This can prevent costly outages and improve safety.
A customer requests a ride from Uber. A thief uses a stolen credit card. A patient’s blood pressure drops. A server fails in a data center. All of these are considered real-time data (also knows as events.
We ingest real-time data into an event log, which captures a sequence of events as they happen. It is natural to imagine the data as a stream of events flowing in time. A data stream is an abstraction built on this analogy. The purpose of streaming is the ability to take on events in-flight, without waiting for that information to be stored first. This is the largest differentiation between real-time streaming and batch processing, and allows for real-time data analytics at scale.
The basic principles of real-time data processing are simple. We distinguish between producers and consumers of data. Producers are the sources of data. Consumers are the services that use the data. In modern streaming systems, producers send messages to a message broker. The broker assigns each message to a topic and publishes it. A topic is simply a queue of related messages. Consumers can then subscribe to different topics that interest them. This is often called the publish and subscribe model (also referred to as pub-sub). It works almost like a Twitter feed.
As usual, the devil is in the detail. We want our system to be scalable and fault-tolerant. We want to have high throughput and low latency. We want an immutable record of our data, but we also want flexibility in how we use the data in our applications. We want the right architecture and the right performance guarantees. This is where Apache Kafka's stream processing technology excels.
Another example is heart attacks. Approximately 10% of patients suffer heart attacks while they are already in a hospital. By using real-time data analytics, heart attacks could be predicted before they happen. Electronic monitoring and predictive analytics are vital in many clinical areas where patient safety is at stake.
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