Real-time big data analytics are crucial for increased business efficiency and profitability today. There is no use in having large quantities of data without being able to make sense of it and apply it to business problems that need solving. Quick and complete access to data and analytics can inform a variety of business functions, from marketing and sales to services and finance. Without real-time analytics, businesses are dealing with the problems of the past instead of those of today or tomorrow.

Enterprises can use large data sets to uncover information about their market, customers, processes, and more. Combining real-time analytics with big data makes it possible to extract valuable information as soon as it enters the existing infrastructure.

In the context of computing, real-time data processing essentially means performing operations on data milliseconds after it becomes available.

Challenges and how to overcome them

A number of challenges can stand in the way of having access to real-time analytics.

Collect good data: The first comes in collecting relevant data. Errors in data collection can influence its value. Companies need to have systems in place to make sure the data collected comes from dependable sources. Data quality issues are often cited as an inhibitor to business analytics and systems modernization initiatives.

Decide which data is relevant: Once data is collected, there is the challenge of deciding which data is most relevant. If companies keep their goals in mind, they can focus on the right type of data to collect and analyze.

Replace traditional architecture: Traditional architecture may be irrelevant to the way an organization will use data. Some traditional solutions are inadequate when it comes to the need for speed and scaling of data in real-time. Software architecture is, therefore, one of the challenges to face when implementing real-time analytics.

Deal with a resistance to change: Another challenge when wanting to use real-time analytics is more to do with the organization. There may be internal resistance to change and the use of internal processes that don’t support it. Business managers could feel it’s too costly and employees may need the education to realize that technology can enrich their jobs. Even without these barriers, it may be difficult to figure out how to make the transition to new ways of getting relevant information from big data.

Use in-memory computing: In-memory computing simplifies architecture by moving data previously stored on hard disks into memory. Computing becomes lightning fast when data is stored in RAM across multiple servers.

Partitioning of data into the memory of multiple computers makes parallel distributed processing a technical necessity. The horizontal architecture means that scaling is easy as it simply requires adding a new server. Instead of a centralized server managing the processing, the processing capacity is shared between many computers.

Use in-memory computing platforms: Vast amounts of data can make it difficult to get information and insights just because of the impracticality of traditional disks at such a scale. In-memory platforms can make the difference as they combine all in-memory technologies for more efficient control. They make it possible to interrogate huge data sets in real-time to offer insights for business purposes and enable predictive analytics.

What is real-time processing?

Real-time processing is when data is needed immediately, such as at a bank ATM. Near real-time is when speed is important but it is not needed immediately. Batch processing is when it is possible to wait for days.

Real-time processing requires constant input, processing, and steady output. For example, at a bank ATM, immediate processing is crucial for the system to work.

Some advantages of using real-time analytics

Using real-time analytics means businesses can identify issues quickly and respond to them immediately. This may help to prevent negative occurrences, such as cyberattacks and equipment breakdowns.

Businesses can give their customers what they want exactly when they want it, improving their customer service and increasing their conversion rates.

Businesses such as utility companies, banks, and retailers can analyze huge data volumes quickly and detect patterns so they can make adjustments. Banks can also stop possible fraud before a transaction is complete with access to real-time data feeds.

Businesses can create predictive models for pricing, trends, demand, etc., and get an idea about everything from potential problems to customer sentiment.

In the financial services industry, companies can use real-time analytics to meet regulatory requirements. Health insurance companies can make use of them to identify overbilling and manufacturing companies can gain insights to handle complex scheduling issues.

In transportation, businesses can reduce downtime, increase safety and decrease maintenance costs if they have access to real-time analytics.

The tech revolution in industries like healthcare is having an impact on the way we live our daily lives. Wearable technologies can offer great benefits in terms of diagnosis and the Internet of Medical Things can allow data from medical devices to be shared across healthcare facilities. With all the connected devices comes the importance of handling the data in the correct way and getting the correct insights from analyzing it.

Real-time insights from big data can gather application usage data and analyze performance to drive product development decisions that could increase customer engagement. They could prioritize improvements, like adding the right features at the right time.

The costs of in-memory technology may initially be high but access to real-time analytics can save costs over time through increased efficiency, better customer experiences and more sales. Even handling employee engagement, hiring and retention is more effective when using real-time big data analytics.


Organizations today deploy more applications than ever before. Each application creates computer-generated records of its activities and organizations rely on real-time big data analytics to comb the data for relevant insights to drive business decision-making.

The best analytics tools combine advanced technologies like machine learning with other software features. As information technology systems become more distributed, real-time big data analytics is likely to become more commonplace. Real-time computing is challenging the status quo and enabling the application of analytical processes to transactional databases without impacting operation system performance.