Real-time analytics is a set of techniques for processing data as soon as it becomes available. The main goal is to provide fast and actionable insights.
Companies must derive real-time insights from a variety of sources that produce large volumes of data at an extremely high rate. One of the biggest challenges of real-time analytics is combining data from different sources and analyzing it in a timely manner.
Real-Time Analytics Examples
Examples of real-time analytics are:
- Providing the customer with an offer or a piece of information that matches their needs and inclinations based on a real-time analysis of their behavior.
- Application monitoring to prevent downtime and improve performance.
- Real-time blocking of fraudulent transactions.
- Chatbots that automate conversations with customers.
How Does Real-Time Analytics Work?
To get a better understanding of the term real-time analytics, let's break it down into its components:
- Real-time: Data is collected and processed rapidly and continuously.
- Analytics: Actionable insights are extracted by applying a set of algorithms to the data.
We can now see that real-time analytics is a process, not just a technology. To "make real-time analytics work," all components should operate in real time.
Real-time analytics process: Collect data, combine sources, analyze to extract actionable insights
Collect Useful Data
Real-time analytics only works if you're collecting useful data as it becomes available. You need to understand what data is important for your business and how it can be collected. This is the first step in the process. For example, you may be running a manufacturing company. You need to know if a machine is showing signs of failure or if it's working perfectly. To do that, you need to collect data from the machine sensors and monitor it in real time.
Integrated data platforms that provide real-time data ingestion allow you to collect useful data as it becomes available. Apache Kafka is a well-established solution for implementing real-time data transfer with event streams. With the MongoDB Connector for Apache Kafka, you can consume data from Kafka topics and write it to a MongoDB Atlas cluster.
Combine Data from Multiple Sources
In practice, we usually collect data from multiple sources. In our machine-monitoring example, we may have a sensor that collects data about the temperature of the machine, a sensor that collects data about the pressure of the machine, and a sensor that collects data about the humidity of the machine. To perform a complete analysis, we need to combine the data from all of these sources. The second part of the real-time analytics process is the convergence of data from multiple sources.
In many cases, this results in using slow ETL (extract, transform, and load) processes or custom-built pipelines for converging data. These solutions are costly, difficult to maintain, and cause delays in the real-time analytics process. Moreover, adding new data sources can be frustrating and difficult to manage. MongoDB allows you to run aggregation queries in place. With the MongoDB aggregation framework you can perform intricate analytics and generate pre-aggregated reports in real-time.
Another important caveat with real-time analytics is that to create a more complete analysis, you need to combine your transactional (current) data with analytical (recent and historical) data. As we mentioned earlier, data is generated at rapid rates and in large volumes. A reasonable approach is to extract insights from the transactional data and then move it to a cheaper storage. However, querying data from these cheap storages is slower and somewhat limited. This can be an obstacle for real-time analytics.
How can we combine transactional and historical data to create a more complete analysis while also keeping the costs low? This is where solutions like the MongoDB Atlas Online Archive come in. With the Online Archive, you can automatically archive aged data, while also being able to query it in real time.
Combining current and historical data leads to more complete real time analysis
Analyze to Extract Insights
The third and final step in the process is to extract actionable insights from the data. This is where the real-time analytics process really starts to make sense. But to analyze data, you need to have the right tools. Being able to query data in a way that's easy to understand and interpret is the key to success. Different tools solve that problem differently. For example, the MongoDB Query API allows you to analyze data in-place, right in your operational database. To learn more about how MongoDB allows you to analyze data in real time without heavy ETL processes and duplicating data, check out the dedicated blog post.
Real-Time Analytics Use Cases
Businesses are increasingly looking for ways to improve their processes and increase productivity. Real-time analytics is a great way to achieve that. We can categorize the real-time analytics use cases into the following categories:
- Personalization & Experience
- Fraud & Error Prevention
- Process Optimization
- Preemptive Maintenance
Let's take a closer look into each of these categories.
Personalization & Experience
Analyzing user behavior to provide personalized experiences is a key use case for real-time analytics. For example, a customer may be interested in a product that they've recently purchased. If you can provide a personalized experience for this customer, you can increase the likelihood that they'll return to your store.
A common example of predictive analytics is the so-called “next best offer” analysis. This is a technique that uses real-time analytics to provide the user with a compatible offer based on their behavior and historical interaction. A really simple example is showing the customer of an online store relevant products based on the products they've viewed. This analysis should happen in real time because the customer is actively browsing the site. A more complete example is the “personalized shopping experience.” This is a technique that uses real-time analytics to provide the user with personalized offers based on their behavior and historical interaction. As you may have noticed, this is an analysis that is performed in real time by converging transactional data (current browsing session) with historical data (previously purchased products).
Fraud & Error Prevention
Detecting fraudulent behavior is a key use case for real-time analytics. For example, suspicious credit card transactions can be detected in real time and blocked. Traditional analytical systems can be used to detect fraud, but they are too slow—processing the data and analyzing it may take hours. Real-time analytics can be used to detect fraud in real time.
The same mechanism, also known as anomaly detection, can be used for preventing or detecting clerical errors. For example, a seller may be updating the prices of a line of products in an online store. This can easily lead to clerical errors and inaccurate prices. Automated anomaly detection to the rescue! If an anomaly is detected, the seller can be notified and the price can be fixed.
Process Optimization
Optimizing existing processes has been one of the goals of digital transformation. But digitalizating a business process won't necessarily improve it. Digitalization allows you to collect data and analyze it to provide actionable insights. But how do you optimize a running process? Well, you can analyze it in real time and then apply the necessary adjustments. Of course, the process should allow for real-time adjustments.
Production planning is a good example of a process that can be optimized in real time. By connecting the production planning process with real-time analytics, you can automatically adjust the production plan to meet the needs of the customer. Tracking demand in real time will provide you with the ability to create a more agile supply chain.
Another aspect of process optimization is automating repetitive human tasks. For example, a call-center agent is very often answering the same questions over and over again. Chatbots can be used to automate a lot of the conversations with customers. The conversations that customers lead—both with agents and chatbots—can be analyzed in real time to improve the efficiency and accuracy of the chatbot.
Preemptive Maintenance
Preemptive maintenance is a way to reduce downtime and maintenance costs in various industries. For example, a manufacturing company may be running a machine that is showing signs of failure. If you can detect this failure, you can immediately fix it. This is a key use case for real-time analytics.
Another use case is application monitoring. You can collect and analyze system logs to detect errors in real time. This often allows you to resolve the problem before it causes a downtime. For example, you may notice degraded performance in a web application. You can resolve the problem and then deploy the new version before the website goes down.
Conclusion
Real-time analytics is an essential process for successful businesses. To put it simply, real-time analytics is a process that requires you to be able to collect data as it becomes available and analyze it in real time. The importance of extracting insights quickly and accurately from data is ubiquitous to industry and can be achieved by using the right tools.
While there are a lot of tools promising to help you with analytics, choosing an integrated data platform is the best way to go. With MongoDB, you can run powerful analytical queries right in your operational database. No need for ETL processes or expensive custom-built pipelines. No additional data storage costs. MongoDB's real-time analytics capabilities are leveraged by multiple companies from different industries. If you want to learn how and why, check out the dedicated blog post.
