Real-time data has become a critical component of modern enterprise operations, and increasingly businesses are seeking scalable OLAP database solutions that can refresh their real-time dashboards, monitoring systems, and other applications in a matter of seconds. In this article, we'll answer the question: what is real-time analytics?, and offer up a practical definition, use cases, and more.


Defining Real-Time Analytics

Real-time analytics refers to the use of, or the capacity to use, available enterprise data and resources right as they are produced, rather than at a later time.

Real-time analytics is important because the value of data can diminish over time, varying by business context. An example of this is fraud detection, where significant financial losses can be incurred every minute the fraud goes unaddressed. Real-time analytics tailors its immediacy to the specific needs of each business use case. In essence, real-time analytics allows businesses to analyze information when it still holds the highest level of value.


Real-Time Analytics Use Cases

Real-time analytics has found a home in most of the world's largest enterprises and across virtually every industry. It has many practical applications including:


Fraud Detection

  • Airbnb: With users spanning across the globe and millions of listings available, it's imperative for Airbnb to detect and prevent fraudulent activities instantly. Through real-time data analysis, Airbnb can quickly spot suspicious patterns, flag questionable bookings, or detect fake reviews, averting potential losses in time and maintaining user trust. Read more about Airbnb's story here.

Customer-Facing Dashboards

  • Xiaohongshu: A prominent social media platform in Asia with a massive user base, an integral part of their monetization strategy includes advertising. To provide maximum value to their advertisers, Xiaohongshu has implemented a real-time analytics system. Advertisers can log into this system, configure their dashboards, and get an instantaneous view of their ad performance. This empowers them to make quick decisions, optimize their ad strategies, and improve ROI. Read more about Xiaohongshu's story here.

Logistics Management

  • JD Logistics (JDL): As one of the largest logistics companies in the world, JD Logistics needs a robust system to monitor its vast array of shipments and operations. By leveraging real-time analytics, JDL offers internal dashboards and reports that allow them to track slow shipments, monitor vehicle locations, and ensure timely deliveries. This real-time insight streamlines operations and enhances customer satisfaction by providing accurate delivery estimates and timely updates. Read more about JDL's story here.

Internal Dashboards

  • A leading travel service provider, manages a massive number of bookings, customer queries, and operations every day. Real-time analytics plays a pivotal role in their operational efficiency. With internal dashboards powered by real-time data, employees at can get live updates on bookings, flight statuses, hotel availability, and customer feedback. This enables them to provide swift customer support, optimize their offerings, and make informed business decisions. Read more about's story here.


How To Get Started With Real-Time Analytics

Adopting real-time analytics might seem daunting, especially given the technical intricacies associated with it. However, this process is not as difficult as it appears, as long as you break it into simple pieces. In fact, with the right approach and guidance, businesses of all sizes can harness the power of real-time insights. In this section, we'll dissect this process into straightforward steps to help you kickstart your real-time analytics journey.


Real-Time Analytics, Is It for You?

You are probably drawn to the idea of making decisions in real time using analytics. While the cost of real-time analytics has come down quite a lot in recent years, it still introduces new technology and requires specialized skills to develop and maintain. It's vital to consider if it's truly suitable for your organization based on factors like business needs, costs, and implementation challenges.

Here are four questions to ask yourself first to understand whether real-time analytics is suitable for you:

  1. How fast is data being generated and at what frequency?

  2. How quickly can you make decisions based on this data?

  3. Does your business model or strategy benefit from real-time insights?

  4. Can the benefits justify the cost of real-time analytics?

Depending on your answers to the questions above, it should become clear to you if real-time analytics makes sense. In some cases, the decision to embrace real-time may be slightly more complex. We recommend reading this guide to better understand how to navigate those situations.


How Does Real-Time Analytics Work?

You've decided that real-time analytics is for you, but how do you get started? The road ahead may appear complicated, but it doesn't have to be. To bridge the gap between raw data and instant decision-making, you'll want to first break down the complex structure of your real-time system into simple steps and illustrate how it can be harnessed for impactful, efficient decision-making.

  • Step 1: Real-Time Data Generation

  • Step 2: Data Capture and Ingestion for Real-Time Analytics

  • Step 3: Data Preprocessing for Real-Time Analytics

  • Step 4: Real-Time Data Analysis, Visualization, and Reporting

  • Step 5: Decision-Making with Real-Time Analytics

Breaking everything down into these five key steps makes it easy to focus on what matters when developing each core component of your real-time system. If you'd like more detail on how to approach each step, read our guide to real-time analytics.


Cost-Effective Real-Time Analytics

When you are implementing your real-time analytics pipeline, there are going to be parts that are very different from traditional OLAP that you are familiar with: the time restraint of real-time analytics often requires different tools in your data pipeline and technology stack.

The architectural nuances between real-time and conventional batch analytics not only differ substantially but also have potential pitfalls that, if overlooked, can be costly. As you transition into real-time analytics, it's important to understand and navigate these intricacies for efficient and cost-effective implementation.


RTA PipelineFigure 1: A real-time analytics pipeline to avoid


Take a look at our guide to better real-time pipelines and learn how to avoid building expensive pipelines like the image above.


Get Hands On Experience With Real-Time Analytics

From fraud detection at Airbnb to real-time dashboards at Xiaohongshu, the transformative power of immediate insights is clear. While transitioning from traditional OLAP systems to real-time analytics can appear daunting, it's a leap worth taking. Technological intricacies and strategies may differ, but with the right approach, businesses can harness real-time data to make informed decisions, optimize operations, and drive unparalleled growth.

It has also never been easier to get started. Get a feel for how real-time analytics can benefit your business with a 30-day free trial of CelerData Cloud. Sign up now.



Join StarRocks Community on Slack

Connect on Slack
copy success