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In today’s increasingly competitive business environment, there’s an increasing need for fast decision-making. In this connection, the number of companies relying on real-time data analytics is growing. Such an approach helps reduce financial and operational risks and increases customer loyalty. 

At NIX United’s business intelligence agency, we know very well what real-time analytics is and implement it with practical data engineering. In this article, we’ll share our vision of the possibilities and prospects of implementing this concept in your business.

What is Real-Time Analytics?

Real-time analytics is a concept in which logical and mathematical methods are applied to data to provide information with minimal delay to make more informed decisions.

There are two types of implementations of the concept:

  1. On-demand: providing data or computational results
  2. Continuous: constantly processing events and streaming results to end users, applications, or data warehouses

We’ve discussed the significant differences between business intelligence and data analytics. And what is the value of real-time data analytics? Speed times efficiency. High-bandwidth event streaming technology allows you to process, retrieve, and execute transactions with the latest information in the shortest possible time.

Why is it Necessary?

Organizations are increasingly faced with the need to shorten time from receiving information about changes in external conditions to responding accordingly. Businesses need to be able to receive data and respond quickly and avoid risk. 

Critical drivers for adoption include the following: 

  • Increased competition: data is king, and market competition is shaped by the players that find the most intricate ways to acquire it and implement data-driven solutions.
  • The massive shift of users online: events like the COVID pandemic and other shake-ups of recent years gave a felt push to the mass consumer’s transition online, and you need to be able to efficiently work with everybody’s personal and public information online.
  • Digital transformation: going digital requires a firm hang on data—the way you source it, analyze it, and put it to practical use.

In such a dynamic environment that we see in the market today, companies urgently need to speed up their workflows focused on the extraction of meaningful information from the general flow of data. Their best bet yet is to enable a real-time mode for this process. All in all, the ultimate implementation of real-time analytics solves two key challenges: 

  • Gaining a unique customer experience: user experiences can be tailored on the go, getting personalized and more on-point for potential and existing customers while they browse.
  • Security and business continuity: staying in tune with business-essential insights puts you one-step ahead, where you can seamlessly detect and eliminate security and business risks.

Other challenges being addressed include rapidly bringing advanced products and services to market. In addition, more and more people are interacting with connected devices, so the ability to respond appropriately to events becomes especially important. Real-time analytics can play a role in creating value if:

  1. Extremely low-latency response is critical for predicting or responding to disasters and natural disasters
  2. The current risks of working with big and high-performance data must be detected
  3. Ample data storage is not possible or optimal
  4. Quick, appropriate action based on situational awareness is needed

Moving from “reactive” to “proactive” data processing offers various other benefits for multiple industries and activities.

Specific Benefits for Business

Real-time analytics is in demand mainly because of the benefits it provides:

Applications with a Fast and Responsive Interface

End users’ work is simplified because they don’t have to wait long for downloads. They can solve their tasks quickly, interacting seamlessly with the data they’ve already processed.

Informed and Timely Decisions

Users are uniquely empowered to slice through data. With low latency, they can make multiple queries simultaneously and make informed decisions quickly. Overall, this increases business productivity.

Real-time data analytics is also optimal in many sensitive scenarios, such as security, route optimization, and advertising bids. This helps you retain the precious time needed to influence critical processes. 

Reducing Cognitive Load

With automated or semi-automated application intelligence, it’s easier for users to make the proper assessments and conclusions. They can focus on strategic tasks and do their jobs much more effectively.

How It Works

Traditionally, decisions are initiated within a predefined algorithm of actions when we expect some event to happen. In real-time analytics, there is constant evaluation, manipulation, and cleaning of data in motion, all the way to preservation. And that’s the main difference. 

Most importantly, the process is focused on detecting or changing a pattern. It allows you to look for differences in normal behavior and use those changes to notice something going on.

Typically, data is collected, stored, and transmitted to a repository or, sometimes, a lake. Reporting and analysis tools are applied to this data for insights into the business process.

Real-time data analytics requires thinking differently about deploying analytical models without excluding the ability to look at offline data to identify new trends. That said, there is a specific type of data for each organization that has real-time value.

Main Components of Real-Time Analytics Software

Since the main tasks are to send and retrieve data into a system with the ability to enter vast amounts of information quickly, the corresponding software generally includes:

  • An aggregator that handles data collection
  • An analytics engine which compares values and aggregates data
  • A broker designed to provide access
  • A streaming processor that receives and sends data to perform logic

On the other hand, streaming can consume many resources and may need to be more practical for some applications. Therefore, real-time analytics architecture should include components that can schedule data acquisition regularly.

For the raw data to be acceptable for delivery to the target system, data pipelines need to provide the following:


The integration pipeline must be able to process data of different formats and degrees of structuring from various sources with minimal delay. It must also be able to deliver for immediate processing and use. It could be cloud, transactional databases, files, messaging, etc.


As events arrive, they need to be cleaned up, processed, and transformed on the fly before they can be used or stored. Some platforms perform filtering, conversion, aggregation, masking, and enrichment of streaming data before delivering it to on-premise or cloud environments. 


To support a variety of big data scenarios, real-time analytics architecture must include scalable storage and data lakes with easy and reliable access. These components must support the aggregation and pooling of data from different sources. 

Representation / Consumption

The ultimate goal of the pipeline is the presentation layer of the processed data. This layer in real-time analytics architecture provides the following:

  • Visualization
  • Direct queries from storage
  • Extraction of useful information

All of this is done with the help of special tools supporting SQL, batch analytics, report dashboards, and machine learning.

Options for Use

Ubiquitous digitalization and increased competition are demanding completely new solutions from manufacturers. They must quickly collect information about user interactions and operational infrastructures to operate more productively. Implementing this concept enables this. Below are some real-time analytics use cases. 

The Real Economy

There’s a lot of room for improvement in this area. In particular, thoughtful analytics implementation can help fine-tune production processes and improve efficiency. The concept also helps enhance workplace safety. 

With the help of data from CRM, ERP, sensors, and video cameras installed in a single system, you can assess in real-time how a company and even employees are working and receive signals about problems.

Stock and Asset Management

Real-time analytics in manufacturing provides a detailed analysis of the state of inventory, including assessing the value, the status of obsolescent products, and sales potential. In this case, the concept may include the following methods:

  • Descriptive: focusing on crucial inventory metrics
  • Diagnostic: indicating catalysts for changes in reported data
  • Predictive: suggesting future developments
  • Prescriptive: recommending actions to correct the situation

Deploying real-time analytics techniques in manufacturing is valuable when unplanned needs arise. For example, to reduce operational risks, costs, and downtime, large companies are moving to predictive fleet maintenance. This ensures technicians don’t always have to be around the plant for scheduled maintenance and can save time.


Logistics service providers, with this concept, can more easily identify optimal routes, organize efficient supply chains, and plan supply and demand. Transportation companies can monitor drivers’ behavior and increase their awareness of possible problems on the roads at critical times.


Real-time analytics in the financial industry is essential because it’s easier to deal with traditionally massive data and complex patterns. The concept provides the ability to create trading strategies based on current market trends. This helps predict failures and reduce downtime of critical systems as well:

Confront Fraudsters

The framework helps identify insider trading and price manipulation. It can collect data from Twitter feeds, news feeds, company announcements, and other external data streams. One method is generative adversarial networks (GAN). 

Real-time analytics in the financial industry helps implement Markov models and machine learning better to protect the financial sector from the actions of fraudsters. By studying their behavior, banks can translate this knowledge into rule sets to analyze incoming data and recognize them in time.

Improve Transactions and Reporting

Financial institutions must make trading decisions almost instantaneously. With analytics, traders can quickly access information from many sources to minimize risk. By applying an event-driven architecture, banks can partition business functions, allowing changes to be made without compromising the underlying platform. 

The concept allows for rapid error detection, transparent financial reporting to customers, and offers that benefit them at a given point in time. It reduces staff workload and access to up-to-date financial data increases customer confidence. It allows you to assess organizational performance, improve workflows, and fix problems early on. 

Increase the Accuracy of Forecasts

Up-to-date numbers provide a better measure of actual performance. Using current data makes forecasts more accurate and timely, allowing for sound planning.


The implementation of the concept in this area has significant benefits:

Centralization of Information 

Medical data is often scattered, reducing the industry’s ability to improve the quality and efficiency of services. Real-time analytics in healthcare solves the problem because the information collected (including from wearable gadgets and virtual appointments) is concentrated in one place. This improves the quality of both healthcare operators and insurance companies. 

Predictive Patient management

Clinical symptoms are not always obvious, and physicians must act before it’s too late. The framework allows us to identify optimal algorithms that can then be used to provide critical care. 

Patient lifecycle statistics from wearable sensors can be linked to laboratory results, medical history, and other data, which can then be used to act on established patterns. It can detect and treat disease in newborns long before clinical symptoms appear. 

Physician-patient Synergy

Real-time analytics in healthcare can bring stakeholders together. Smart devices encourage patients to participate actively in the treatment process. They can remotely monitor their vitals and assess how their habits affect them. On the other hand, the nursing staff has remote access to this data and their medical history to draw conclusions in time and notify the client in case of a threat. 

This way, the medical facility’s client will be fully covered from all sides. Thanks to this, the industry can provide preventive care, improve treatment outcomes, reduce rehospitalizations, and lower mortality rates.


Among real-time analytics use cases, this one is the most interesting in terms of a personalized and customer-centric approach. Implementing the concept ensures that marketers provide accurate and valuable information to the right consumer at the right time. It can quickly increase clientele and revenue for ecommerce businesses, for example.

The effectiveness of marketing campaigns can range widely. Many customers perceive calls and messages offering services as spam. A personalized approach, where customers are provided precisely what they need at the time, could make a difference. 

For example, based on contextual experience, telecom operators develop the most relevant offers and recommendations for clients, which become popular. By analyzing real-time banking transactions, retailers often offer customers to make another discounted purchase under a loyalty program at the same shopping area where they are currently located.

How to Add Real-time Data

Companies that decide to accelerate access to the information they need should be prepared, because some business processes will have to be revised or replaced, as well as the solution architecture. Self-development will take several years, and you could stay caught up with your competitors. To implement the technology, it’s worth turning to third-party companies with the appropriate expertise. 

NIX United offers unique business intelligence and analytics solutions with a wide range of features to tailor the use of big data to the business. Our services use an efficient algorithm:

  1. Defining business goals and product vision
  2. Development of requirements and project documentation
  3. Solution design and prototyping
  4. Software engineering and implementation support

Our software ensures that you make informed decisions and set the right priorities. With our take on business intelligence reporting and other analytics, you can identify market trends and benchmarks, optimize opportunities, and unlock growth potential. These are popular because they:

  • Provide everything you need for seamless data replication
  • Lead to scalable architecture that requires no management or maintenance
  • Automatically detect incoming data schema and map it to the destination schema
  • Provide easy data conversion, fast information retrieval, and decision-making

Plus, we guarantee 24/7 support. Is it worth missing out on a chance like this? Contact us to make your business more adaptive, productive, and competitive tomorrow!

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