How Real-Time Data Analytics Transform Your Business

In the past, business leaders made decisions based on information that was weeks or even months old. As things can change very quickly in the business world, this would lead to lost opportunities. But today, real-time data analytics is transforming the way businesses operate. Now, they can make decisions based on events and information with unprecedented speed, giving them a tremendous competitive advantage as they can quickly react to changes in the market.

What Are Real-Time Data Analytics?

Real-time data analytics is the process of collecting, analyzing, and providing information in real-time with low latency measures. So instead of waiting for scheduled reports, data is available as soon as it’s generated. It's an application usage made possible by using parallel programming to collect and process data much faster than traditional methods. For businesses that want to stay ahead of the competition, it’s essential.

5 Key Benefits of Real-Time Reports

Here are five key benefits for businesses using real-time reports:

1. Allows for Quick Decision-Making Based on Data

Real-time data collection gives businesses access to rapidly developing information. Instead of looking at historical data to make decisions, business leaders can now gather real-time insights to make better decisions faster.

2. Identifies Opportunities for Businesses in Real-Time

Another key benefit of real-time big data analytics is being able to identify business opportunities as they happen. With the right tools in place, business leaders can be alerted to potential new markets, changes in customer behavior, or operational inefficiencies as they're currently happening. They can take advantage of opportunities as soon as they arise.

Amazon famously demonstrated this by using real-time big data analytics to quickly capitalize on the success of the Harry Potter books. When the first book in the series became a surprise bestseller, Amazon used data from sales and search patterns to predict that the rest of the books would also be in high demand. They stocked up on copies of the subsequent books in response, ensuring that they could meet customer demand and stay ahead of their competitors. Harry Potter books continued to be ranked on the Top 10 most read books on Amazon as recently as May 2022.

3. Detects Issues Quickly

Another significant benefit of real-time data analytics is the ability to detect issues fast. Businesses can take steps to rectify problems before they become too big. This can be anything from a drop in sales to increased customer complaints.

Netflix demonstrated this using real-time data analytics to detect a strain on internet service providers that affected video streaming quality in Europe. They were able to quickly identify the issue, cut their streaming bitrates, and prevent customer dissatisfaction.

4. Improves Overall Customer Experience

With thousands of data points readily available, businesses can use real-time data analytics to fix issues that may be causing problems for customers and improve the overall customer experience. They can see exactly what their customers want and need at any moment in the buying process, eliminating the need for guesswork.

A practical use case is how banks are able to detect shifts in the behavior of their clients and use that data to approach them with relevant offers. If a customer is expecting a baby, they’re probably thinking about the financial implications of their new child. A perfectly-timed email from their bank offering to set up a college account can break through the clutter of generic marketing.

5. Opens the Door for Real-Time Personalization

One of the most exciting things about real-time data analytics is the potential for using individual customers' data to provide them with a personalized experience — in short, real-time personalization. This could be anything from tailoring product recommendations to providing real-time customer support.

How Real-Time Technologies Compare to Near Real-Time Processing

Real-time data analytics has many benefits, but how does it differ from near real-time analysis? Can't they be used interchangeably? Here's a quick overview of the differences between these two types of data processing and how they support different technologies.

Real-time data collection and analysis is defined as the ability to process data and get results within seconds. This type of analysis is often used for mission-critical applications, such as air traffic control or real-time stock trading.
Near real-time analysis is defined as the ability to process data and get results in minutes. This type of processing is often used for applications that don't require real-time information but still need timely results.

So, how do these two compare? Real-time data analytics operates on information generated by real-time software systems, meaning that it does real-time data integration. For example, autonomous driving systems and air traffic control systems use machine vision memory technology. They make rapid decisions based on visual input from their surroundings.

Real-time data processing benefits businesses that need to take action on data right away. This could be anything from responding to a customer complaint to safely executing workloads with robotics technologies. A Forrester report on RPA services market shows that the financial sector accounts for 36% of all use cases, which isn’t surprising — banks have been using automation not long after it was invented.

For instance, a single bot can be programmed with thousands or even millions of rules-based operations to automate high-volume data entry processes. This includes account opening transactions, matching customer information against databases, and completing other necessary tasks like validating identity sources (eCard).

On the other hand, near real-time processing is ideal when speed is critical but not urgently needed. Operational intelligence is a notable example as it includes data processing and Complete Event Processing. CEP in particular has demonstrable utility for discovering possibilities such as sales leads and threats like phishing attacks in data sets. Other applied uses of near real-time processing are processing sensor data and IT systems monitoring.

Near real-time data integration is a good fit for businesses that can tolerate a short delay before taking action. This could be monitoring customer behavior or detecting trends. Recommendation engines are one way businesses use near real-time data to improve user experience. These engines look at users’ browsing habits and current trends, such as when they’re reading the news, listening to music, or making a purchase. From there, they provide valuable recommendations that prompt users to return, heightening their Repeat Visitor Ratio and Average Session Length metrics.

How Are Companies Using Real-Time Data Analytics Tools?

Today, businesses are using real-time data analytics tools to optimize their operations and how they handle their data. Here's a closer look.

Improve Business Processes

Businesses are using data analytics tools to enhance their supply chains, reduce their operational costs, and improve their customer service.

Supply chain optimization: data analytics helps businesses identify barriers and inefficiencies in supply chains by tracking inventory levels and sales data. This puts them in a better position to improve their supply chain.
Operational cost reduction: data analytics can help businesses reduce the costs of their operations by identifying areas where they can save money. They might find that they can scale back on the number of employees needed to run a process or reduce how many resources they're using.
Customer service improvement: businesses can use data analytics to identify customer pain points and areas where they can improve their service. It could be anything from reducing wait times to avoiding product quality issues.

Gain Business Intelligence

Here's how business leaders are gaining business intelligence by using tools for real-time data analytics to better understand their markets, competitors, and individual customers.

Understanding customers: businesses can better meet their customers' wants, needs, and service standards. From buying patterns to feedback, not only do businesses see what customers are doing, but they're also in a stronger position to understand why they're doing it. This helps them with their marketing, product development, and sales strategies.
Understanding the market: analyzing market data helps business leaders serve their market better by giving them a deeper understanding of — and competitive edge within — their respective markets. This information could be about the economy, people, and other business factors in their field.
Understanding their competitors: businesses can track their progress relative to their competitors, allowing them to stay ahead of the game.
Talent acquisition and management: not only can data analytics be used to identify top talent, but it can also help businesses make informed decisions about where to allocate resources in order to attract and retain the best talent.
Understanding financial data: by analyzing financial data, businesses can understand where their money comes from and where it goes, which is necessary for them to get the most out of their spending and investments.

Aid Predictive Analytics

Data analytics tools are becoming more popular seeing as they allow businesses to identify patterns and relationships, helping them predict future trends before they happen.

One example is using data to predict customer behavior. By analyzing customer data like buying patterns, businesses can understand what customers are looking for and are likely to do in the future. After looking at the buying trends, they can advertise products that consumers are likely to be interested in. This saves time and money on marketing and advertising campaigns.

Another example is using data to make informed financial predictions. By looking at financial data, businesses can get a good idea of where the economy is going and what's likely to do well in it. This helps them make better decisions about where to spend money and how to invest it.

Shift Your Focus To Real-Time for Deeper Insights and Better Decision Making

Operations teams have always been focused on getting things done efficiently and effectively. But today's business environment demands that they also need to be able to make decisions quickly. The information is available, but it's constantly changing.

If you're not using real-time data analytics, you're missing out on the many benefits it can offer:

Operational efficiency: Use real-time data analytics to streamline and improve the efficiency of your operations. You can find your bottlenecks, cut back on manual processes, and make the most of your time.
Strategic planning: develop better plans and strategies by understanding past trends and using them to predict future ones.
Improved decision-making: by understanding the links between different data points, you can get the information you need to make better decisions. You can find patterns and make plans that will help you reach your goals.

Start today and transform your business. With Canvas, data teams can focus on strategic work, and business teams can answer their own ad-hoc questions. Our platform empowers your business team with self-service data visualization, making it easy for anyone to build beautiful dashboards without relying on IT or data teams. So what are you waiting for? Sign up for a free trial today.