What Is Real-Time Analytics?
Real-time analytics refers to a type of data analysis focused on delivering insights to end users and consumers in real time. Where batch analytics and reporting of data once measured turnaround in hours or days, in recent years the pace of business has accelerated to require results in second or even subsecond scales. Companies now require far faster analytical speeds and face far greater challenges in managing large quantities of data.
Every event, from a simple online search to a meal delivery request, holds the potential to shape decisions and outcomes. Real-time data analytics provides the ability to ingest data as soon as events happen and make it available for querying as soon as it’s ingested.
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:
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.
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.
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
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.
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.
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.
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.
Benefits of real-time analytics
Real-time analytics is in increasing demand for the benefits it gives to application users.
Snappy, responsive applications
Snappy, responsive experiences increase user adoption. Embedded real-time analytics gives users a better experience, they don’t have to wait seconds to minutes for data or queries to load. They can interact quickly with the data, providing a seamless user experience.
Faster decision-making
Users can slice and dice data for quick decision-making. With sub-second query latencies, users can ask several questions of the data and reach a decision in a matter of minutes. This makes users more productive, increasing the number of decisions they can make in a day.
Automated Intelligence
Applications can reduce the cognitive load of decision-making with automated or semi-automated intelligence. Teams can become more efficient, relying on applications for a subset of decision-making and focusing attention towards larger, strategic initiatives.
Time-sensitive Interventions
There are use cases that are inherently real-sensitive: catching security vulnerabilities, optimizing delivery routes or bidding on advertisements. If you waited minutes for the data to be processed and queryable, you would lose out on the window of time to make an impact.