What is real-time data analysis? Key benefits and methods to achieve it
March 27, 2023
Today, as many as 86% of companies rely on first-party customer data to make business decisions.
This allows brands to tailor the customer experience and improve their product or service based on feedback.
But what if you could analyze data in real-time? Instead of waiting several days, you gather insights immediately. You’d be able to make faster and better business decisions while adapting to changing market trends.
So, if you’re interested in real-time data analysis, continue reading. In this guide, we’ll answer all your questions about real-time analytics.
What is real-time data analysis?
As the name suggests, real-time data analysis is simply the process of analyzing data in real-time.
Like anything, data has a shelf life. And the sooner that you can convert your data into insights, the better it is for your organization’s business intelligence.
You might think this is cutting-edge technology, but businesses have used it for decades. For instance, banks will use real-time data analysis to detect bank fraud. They’ll monitor how and where you’re using your cards. If some purchases fall outside this pattern, there’s a possibility the purchase is fraudulent.
Key technologies involved in real-time data analysis
Are you excited to get started with collecting real-time analytics? Not so fast! Here are a few technologies you should consider before collecting data in real-time:
- An analytics tool
- Data infrastructure
- A customer data platform
An analytics tool
First, you’ll need an analytics tool that collects and analyzes data in real-time. You can choose between Google Cloud DataFlow, Amazon Kinesis, or Azure Stream Analytics. These will all allow you to collect data insights quickly.
Data infrastructure
After choosing an analytics tool, it’s important to build data infrastructure and import real-time data from your analytics tool into a data lake or cloud data warehouse. A few time-tested methods include ETL (Extract, Transform, & Load) and ELT (Extract, Load, & Transform.)
A customer data platform (CDP)
The last part is using a CDP to structure and logically store terabytes of customer data. You also want something that’ll make data visualization easy. For example, with Lytics, you can convert raw data into visual insights like graphs, charts, and tables.
What are the benefits of real-time data analysis?
Here are four benefits you can expect to see within your organization soon after analyzing data in real-time:
- Faster decision-making
- No data silos
- Personalized customer experiences
- Business agility
Faster decision-making
A recent McKinsey survey found that an executive spends 37% of their workday making decisions. But when you’re streaming data and consolidating it in a CDP, you can use CEP (Complex Event Processing) to convert this data into insights, reducing the time needed to make a decision.
No data silos
Real-time data analysis can also help remove data silos because you’re quickly turning data into insights and making it available to the entire team. This gives everybody the tools they need to perform at their best. Data engineers can model customer data for data scientists, where they can extract insights. From here, marketers and sales professionals gain a detailed understanding of customers and their preferences.
Personalized customer experiences
Speaking about tailoring marketing messages, with real-time data analysis you can personalize customer experiences. And since 91% of consumers say that they are more likely to buy from a company that cares about personalization, this makes real-time data analysis necessary for marketing success.
Business agility
You can easily pivot and respond to market changes when processing data in real-time. This reduces or even completely removes the lag you get when analyzing data lakes weeks after collecting it. So, for example, if you’re running an e-commerce sale, you can collect and understand data immediately, allowing you to adjust prices and marketing messages, thus boosting profitability.
Companies that have successfully implemented real-time data analysis
Need some inspiration before you get started? Here are three examples of companies that are implementing real-time analysis to personalize customer experiences:
- Amazon
- Netflix
- Trulia
Amazon
The first example of a company successfully implementing real-time data analysis is Amazon. Amazon utilizes real-time data analysis to understand the needs and preferences of customers. As they learn more about you, your search results will become personalized. For example, if you just bought an espresso machine, you’ll notice Amazon recommending coffee beans, filters, and other accessories.
Netflix
Another company that has been using real-time data analysis to its full potential is Netflix. They use real-time data analysis to understand what genre of movies and series you like. This process is instant, and you can test it out for yourself. For example, if you add a bunch of horror movies to your wishlist, you’ll notice that Netflix will recommend more horror movies. This is because it tailors recommendations around your preferences.
Trulia
Moving on to a lesser-known company, Trulia is a real estate marketplace for anyone looking to buy or sell a home. It’s a subsidiary of Zillow, and it’s streaming data in real-time to understand where users are based. Let’s say someone wants to change a ZIP code or city boundary. They can update it and Trulia will immediately inform interested parties about the change. With other analysis methods, this change can take days.
Analyze real-life data with Lytics
Analyzing real-life data isn’t as difficult as it seems. All you have to do is put the right systems in place, and use the data insights you gathered to make more informed business decisions.
Fortunately, the Lytics CDP can streamline that process. It lets you structure raw data and organize it in individual customer profiles, so you can understand everything about your customer across platforms, in real-time.
