What is real-time analytics?

Think about the last time you needed to move quickly at work. Maybe you were deciding whether or not to launch a new marketing campaign, or what price to set for your product ahead of a major holiday. Did you have time for data-driven decision making or did you end up having to trust your instincts? 

In order to make an informed decision, you likely needed up-to-date information. This is where real-time analytics comes in. Real-time analytics gives you access to current data so that you can make quick, informed decisions. Let's take a look at what real-time analytics is, some examples of how it's used, benefits of using real-time analytics, and how to successfully implement real-time analytics.

What is real-time analytics?

Real-time analytics is the process of collecting, analyzing, and acting on data in real-time. This means that instead of relying on data that is hours, days, or weeks old, you can make decisions based on information that is only seconds or minutes old. 

Real-time analytics requires three key components:


This is the raw information that you will be analyzing. It can come from a variety of sources, including financial trading, wearable devices, marketing campaign data, and more.


This is the process of turning data into insights. Analytics can be conducted using a variety of methods, including machine learning, statistical modeling, data visualization, and natural language processing.


Once you have insights from your data, you need to take action. This could involve sending a notification to a customer, restocking a product in real time, or adjusting your marketing strategy.

Benefits of real-time analytics

Some of the specific benefits that can be realized through real-time analytics include:

In-the-moment decision making

By having access to up-to-the-minute data, organizations can make more informed decisions that are based on the most current information available. This can lead to improved decision-making overall and better outcomes for the organization.

Improved operational efficiencies

Real-time analytics can help organizations to identify and correct problems in their operations more quickly. This can lead to increased efficiency and productivity, as well as reduced costs.

Gain a competitive edge

Organizations that are able to make use of real-time analytics can gain a competitive advantage over those that do not. This can be due to the fact that they are able to make better decisions, capitalize on unexpected opportunities, improve operational efficiencies, and so on.

Improved customer service 

By having access to real-time data, organizations can provide their customers with better service. This is because they can more quickly identify and resolve problems that their customers may be experiencing.

Faster reaction times to changes

Because they have up-to-the-minute data, organizations that use real-time analytics can react more quickly to changes in their environment. This can be beneficial in a number of different situations, such as when there is a sudden change in customer demand or market conditions.

Real-life examples of how real-time analytics is being used

Real-time analytics is a powerful tool that can be used in many different industries. Here are some examples of how different industries are using real-time analytics today.

  • Banking and financial institutions use it to detect fraudulent activity, prevent money laundering, and comply with regulations.

  • Telecommunications companies use it to monitor network usage, identify problems, and optimize their service.

  • Healthcare organizations use it to track patient health and monitor for potential outbreaks.

  • Retailers use it to track customer behavior, identify which products are selling well, and adjust their stock levels accordingly.

  • Manufacturers use it to monitor production processes, identify issues early on, and avoid costly delays.

How to successfully implement real-time analytics

Whether you are a seasoned data professional or just getting started, there's no better time than now to switch from historical analytics to real-time analytics. If you want your company's transition process to go smoothly and quickly, follow these five guidelines.

1. Define your goals and objectives.

Before you start collecting data and building analytics infrastructure, it is important to first define your goals and objectives. What do you hope to achieve with real-time analytics? What specific insights are you looking for? By having a clear idea of what you want to accomplish, you will be better able to design your system to meet your needs.

2. Identify the data you need to collect.

Once you know what goals you want to achieve, you can identify the data you need to collect to reach those goals. This data may come from a variety of sources, including financial trading data, marketing campaign data, devices, sensors, and more. It is important to consider both structured and unstructured data when planning your real-time analytics system.

3. Set up your real-time analytics modern data stack.

In order to collect and analyze data in real-time, you need to have a modern data stack in place. This includes the right data sources, an ETL/ELT,  a cloud data warehouse or data lake, and an experience layer. By investing in the right data stack, you can ensure that your real-time analytics are reliable and scalable.

4. Collect and analyze your data.

Once your modern data stack in place, you can start collecting and analyzing data. This data can be used to generate real-time insights that can help you make better decisions and take action more quickly.

5. Monitor and optimize your real-time analytics.

It is important to continuously monitor and optimize your real-time analytics. By doing so, you can ensure that it is meeting your needs and providing the insights you need to make better decisions.

The value of self service analytics and real time analytics

Real-time analytics lets people adjust quickly as the business, environment, market, or customer expectations change. To truly capitalize on this value, however, real-time analytics needs to be paired with the right self-service analytics front end that lets those who make the decisions, i.e., the business users, engage the data quickly without requiring a data professional to do so.

That's why a platform connected to real-time, living data and provides an interactive, intuitive front end is required. Otherwise, you end up with either dead data or real-time analytics with a human (data professional) bottleneck.

Make informed decisions in minutes

Real-time analytics can help businesses make quick, informed decisions by giving them access to current data. ThoughtSpot’s powerful search and AI capabilities give you the ability to answer complex business questions in seconds without writing any code. With our easy-to-use Live Analytics, you can get up and running quickly and start making better decisions faster. Sign up for a free trial today to see how ThoughtSpot can help your business make better decisions in real-time.