What is Real-Time Data & Analytics: Expert Tips

There’s an enormous 2.5 quintillion bytes of data being generated every single day. Naturally, businesses are utilising this data to make informed and split second decisions. The ability to do so is powered by real-time data.

In this article, we are going to look at the benefits of real-time data and see how real time data processing is changing the way finance operates (spoiler alert: it’s for the better).

Coming Up

1. What is Real-time Data?

2. What is Real-time Data Processing?

3. How is Real-time Data Used?

4. What Can be Learned from Real-time Data?

5. What are the Benefits of Real-time Data and Analytics?

6. What is the Biggest Challenge of Real-time Analytics?

7. What to Consider for a Real-time Solution?

8. Final Thoughts

What is Real-time Data?

The term may have given it away, but real-time data (RTD) is information that is processed, acted upon, or consumed right as it’s being generated.

Data processing has been around for some time, but the advent or real-time data has made an enormous impact on business operations. Real-time data can be generated from a variety of sources, including special media, applications, mobile devices, sensors, etc.

To get a better feel for how data can be generated and consumed within milliseconds of one another, consider these real-time data examples:

Sensors like alarms and river flood gauges react as they process (intake) the situation. Or, think about stock trading systems and page view events on your website. You can use Google Analytics to see how many people are currently on your website at any given time, which means that the system is acknowledging the data of a page view and displaying it immediately thereafter.

What is Real-time Data Processing?

Real-time data processing is the act of making data immediately available to the end-user once it’s been generated. Real-time data processing does not suffer from delays.

You can compare real time data processing to batch processing. In the past, batch processing was the norm, which meant that systems collected data. After collection, the system would process and store mass volumes of data before it could be utilised. Batch processing is still an option in the cases when data is not immediately needed.

As modern applications and their use cases proliferate, businesses have ushered in and welcomed the era of real-time data. This shift makes sense because the world and peoples’ actions happen live and in real-time, so the opportunity to transform data into insights in the moment opens up doors to new possibilities.

How is Real-time Data Used?

Speaking of new possibilities, it’s helpful to know how and why real-time data is used to understand its benefits.

The main use of real-time data is to enable real-time analytics. This refers to the process of transforming raw data into use insights upon its collection. Real-time data analytics is also called business intelligence. Business intelligence is industry-agnostic, meaning that it can benefit any industry or type of business in which a fast response or action is crucial.

Real-time analytics can be broken down into two categories, namely:

• On-demand real-time analytics: On-demand real-time analytics calls upon the need for a system or end user to create a query after which the analytic results are delivered.

• Continuous analytics: Also known as streaming data analytics, continuous analytics processes data as soon as it's being collected and uses triggers to execute an action as a response to a detected event.

What Can be Learned from Real-time Data?

As explained, real-time data creates the possibility of real-time analytics. Real-time analytics come in different forms. While analytics don’t execute an action in itself, they provide insights that aid in determining what action to take.

The four types of data analytics include:

Descriptive analytics

Descriptive analytics answer “What happened” in order to identify a problem. The limitation of descriptive analytics is that it cannot explain why something happened.

Diagnostic analytics

To answer why an event happened, diagnostic analytics can be used. For this reason, diagnostic analytics is also known as root-cause analysis.

Predictive analytics

Using historical data, predictive analytics looks at trends and patterns of descriptive and diagnostic analytics to predict what may happen in the future.

Prescriptive analytics

At the pinnacle of data analysis sits prescriptive analytics. Prescriptive analytics provide the optimal course of action to take to prevent a problem from occurring. This type of analytics leverages machine learning and algorithms.

Businesses rely on these various types of analytics to make data-driven decisions. When it comes to financial functions, every task and procedure is based on data and its accuracy.

With a financial automation solution like SolveXia, businesses can streamline their processes with automation and benefit from real-time data analytics to make speedy and trustworthy decisions. It connects with all data systems enabling analysis and reporting to be accomplished faster and with fewer errors.

What are the Benefits of Real-time Data and Analytics?

There are countless benefits of using real-time data. Let’s take a look at some of the outstanding advantages businesses can gain from utilising RTD:

Customer Satisfaction

When you can access real-time data, your customer service teams can be more flexible and interactive. Customers expect pesonalised experiences, so the use case of using real-time data to tailor advertising, marketing, and customer experiences can go a long way in building brand awareness and brand loyalty.

Faster Responses

In mission critical industries, real-time data can be the difference between life and death. For example, sensors that detect fires, earthquakes, and weather events rely on real-time data for proper monitoring, management, and response times.

Operational Intelligence

Businesses with access to real-time data can optimise their processes in an effort to achieve operational effectiveness and efficiency. For example, supermarkets can manage inventory in real-time, or investment banks can make use of artificial intelligence to buy and sell financial instruments.

Remain Proactive

With systems that are capable of real-time data and real-time analytics, businesses can react immediately when something goes wrong. In the event or a security breach, real-time data will provide an alert at the time of the breach, allowing for action and remediation before more loss occurs. This ends up saving time and money.

What is the Biggest Challenge of Real-time Analytics?

Real-time analytics isn’t challenging when you have the right tool by your side. Software like SolveXia provides the ability to handle massive volumes of data in quick response times without being bogged down.

Real-time data, by definition, requires that the solution can collect data from various and changing data sources. SolveXia’s financial automation solution can integrate with your existing toolstack and legacy systems with the use of APIs so that no data goes untouched or forgotten.

What to Consider for a Real-time Solution?

As a strategic business leader, you are likely aware that a real-time data processing solution is a must-have to remain competitive in an increasingly saturated market.

When shopping for your real-time data analytics solution, keep in mind the following features:

Scalability

The processing power you need will depend on data velocity and the amount of processing you’ll require for each data stream. If you need to expand your capacity, you’ll want a solution that can scale with your needs.

Fault Tolerance

Fault tolerance is a feature that protects data processing in the event that any node processing failure occurs.

Final Thoughts

Real-time data provides the ability to know what’s happening within your business as it happens. With real-time data analytics, you are able to ensure that your operations are running optimally.

Software solutions like SolveXia empower your team with data analytics and automation capabilities to streamline processes and their outcomes.