Real-time Data Analytics: The Ultimate Guide

Luke Smith
Enterprise Solutions Architect
March 20, 2023
Matt Tanner
Developer Relations Lead
March 20, 2023
Luke Smith
Matt Tanner
Enterprise Solutions Architect
Developer Relations Lead
March 20, 2023
 min read
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Creating, managing, and utilizing data has become a key driver of business performance and success in enterprises. Data has become a tool to improve productivity and attain organizational goals. Data can be generated across several layers of an enterprise, from internal operations to customer-facing interactions. Data of itself may not have a lot of value but value accrues when it is consolidated and analyzed.

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Actionable insights can be extracted from consolidated data that can help to improve business processes, drive consumer engagement, or help management make data-driven decisions that improve the key metrics of success for the organization. The more quickly the data is analyzed from when it is produced is when insights are most accurate and effective. This is when historical and real-time data have their individual use cases. Historical data can be used for descriptive analysis that summarizes trends observable in the data. To be able to act on data to drive decisions it must be collected and used in real-time. Real-time data has to be analyzed as soon as it is generated or produced for it to serve as a feedback mechanism sent into the system to adjust or improve parameters that are being measured and recorded. 

The real-time nature of modern analytics means that organizations can respond to changes as they occur and increase the likelihood of getting a desired outcome or an actionable insight. Real-time data analytics is one such technique that can be used by organizations to fine-tune the extraction of information and trends from the heap of data collected. It can help organizations to base their decisions on the insights gleaned from the ongoing analysis. That is, organizations will be able to react to changes in the ecosystem as those changes are happening. This is powerful since outcomes can be influenced favorably to drive organizational goals. 

To enable real-time analytics, there are several steps involved and every organization must determine the metrics of success that they want to optimize. The first step of the process is clearly defining what they mean by “real-time”. Some organizations might be comfortable with a lax definition of real-time while others may define it to mean instantaneous action at any instance in time. The definition of real-time by an organization will feed into how a real-time data analytics project is set up and the achievements it seeks to make. 

The next step is in defining how the data is collected and from what sources that data will be collected. You will need to determine whether the data is coming from databases, sensors, user interactions, Internet of Things (IoT) devices, and any other data-producing platform. Suppose several sources of data are to serve as the input to the real-time data analytics pipeline, there will be multiple factors in how the data is handled and analyzed. Some of these questions and factors may include:

  • What processes will be required to combine and manipulate the data into a single coherent unit for analysis of the data? 
  • How will multiple formats be converted or transformed?
  • What parameters will be used to handle outliers, missing data, or statistical errors? 

Of course, this is by no means an exhaustive list but does outline some of the concerns that you’ll need to address when creating the real-time analytics environment you are aiming to implement.

After defining how the data is collected and from what sources, the next process is deriving insights from the analytics. Part of this stage is determining which algorithms should be used to analyze the data. The final result of analyzing the data may be output as visualizations or processed data but at this stage, the results should be actionable and provide clear insights. The main difference between this end-to-end process of real-time data analytics and traditional data analysis is that the actionable insights are updated in real-time as the data in the source systems change. The results of real-time analytics are always up to date and represent the state of the system at that specific point in time. 

In this article, you will be given an in-depth overview of real-time data analytics, what it is, and the different types of real-time analytics available. We will also review who can use data analytics to improve their processes and see actual examples of the use of real-time analytics across several industries. Lastly, we will explore the benefits of real-time analytics and some of the challenges that may be experienced when employing real-time data analytics. Let’s dive in!

What is Real-Time Analytics?

Real-time analytics can be defined as a set of techniques that are used to immediately process data the moment it enters a system or at the point it is generated or produced. The goal of real-time analytics is to process data as it becomes available so that answers, insights, relationships, or predictions can be derived from the data. Real-time data analytics helps with making better decisions in a faster time frame. This is accomplished through reliance on the latest data and fast query execution. 

Any real-time data analytics solution must have highly available data with low latency. Data has to be immediately available the moment it is produced and should be consistent. Similarly, the transfer of that data should be in a low latency environment so there are no delays or lags within the system that may cause issues with the insights derived from the data. Data latency and query latency are therefore two important measures for real-time data analytics. Both must be optimized if the end-user is to get a level of responsiveness that feels immediate and leads to a better user experience. 

Data latency is the amount of time from when data is generated to when it can be queried by users of the system. Low data latency, therefore, means that data can be queried almost instantaneously after it is generated. This allows for quick reflections of writes or changes in the data in subsequent queries. Query latency on the other hand is the measure of the time required to issue a query and receive the corresponding result. A real-time analytics application with low data latency and high query latency will still feel slow. This is because the results returned by complex queries will take a long time and the real-time analytics system or solution will feel unresponsive. Query latency, therefore, goes hand in hand with data latency. Optimized queries operating in a low data latency environment will be speedy and lead to wider adoption of the solution.

What Are The Types of Real-time Analytics?

There are two main types of real-time data analytics namely, on-demand real-time data analytics and continuous real-time data analytics. Let’s take a look at the two types and their similarities and differences.

On-Demand Real-Time Analytics

On-demand real-time analytics is a type of real-time analytics where the system is designed to provide insights instantly through a user-issued query. By using an on-demand approach, whenever a user desires to get actionable insights from an application or process, they can do so by issuing a query to the system. The system then interacts with the data store and provides the results of the query as actionable insights. The query results reflect the state of the data at that instance of time.

Continuous Real-Time Analytics

Continuous real-time analytics, sometimes called streaming analytics, involves the constant process of ingesting and analyzing data. This type of continuous analytics and stream processing returns results as responses to changes that are occurring in the application state. These results may be triggered by events that have occurred in another part of the pipeline. In this scenario, the end user does not need to manually query the system for results, rather insights are streamed or supplied continuously based on events or changes as they occur. An example of continuous real-time analytics is in the use of edge devices like sensors that are constantly streaming data and monitoring machinery. These machines and monitoring systems are capable of detecting anomalies in real-time and raising alerts, rather than waiting for a query asking it to describe the state of the system.

Who Uses Real-Time Analytics?

Real-time data analytics are used by organizations in several industries such as manufacturing, finance, logistics, social media, construction, and marketing. In this section, we will look at examples of how finance, logistics, and manufacturing companies may utilize real-time analytics in their processes and day-to-day activities.

Supply Chain Management

Real-time analytics can be used in supply chain management to reduce inefficiencies and optimize processes around the delivery of items. Supply chain management typically involves the cooperation of an organization’s internal teams working alongside external vendors and suppliers. Real-time analytics can help those teams across different organizations see a holistic representation of the state of the supply chain at any point in time. This level of insight can help to eliminate bottlenecks and preempt challenges. Equipment critical to the performance of the supply chain can be monitored in real-time using sensors to detect any anomaly or to predictively determine when the equipment is likely to get damaged. This data can provide actionable insights to implement preventative measures to reduce the chance of breakdown in the capabilities of the supply chain. 

Real-time data analytics can also be used to optimize route planning for delivery drivers, and analytics of weather, traffic congestion, or even fuel consumption trends to reduce lost man hours. It can also be used to reduce operational issues by making data-driven suggestions to schedule maintenance for vehicles or machinery, and other fleet management interventions. For suppliers, real-time data analytics can be a powerful tool in predicting future demand. By doing so, organizations can have insights into whether to scale up or scale down production and gain competitive advantages.


The finance industry depends on data for every aspect of the business. Simply having access to the necessary data in the finance industry is generally not enough. Many decisions in finance must occur in real-time since a few milliseconds can be the difference between a profitable decision and a disastrous outcome. In the stock market, volatility in prices has to be analyzed and acted upon in real-time to seize opportunities. 

As we have seen in the last few years, a company's actions on social media can drive prices in either direction. Because of scenarios like this, analysis has to occur in real-time for the traders and investors to react accordingly. Real-time analytics can also be used to detect insider trading where a large unusual move can be detected as an anomaly and can be used to prevent fraud or raise alerts for further investigations. 

Real-time data analytics is also used widely by credit card companies to detect fraudulent transactions in real-time. When a potentially fraudulent transaction occurs, payment processors can proactively deny or cancel those transactions based on both historical and transactional data. For example, an unusual purchase request online can be analyzed and compared to other factors to ensure the transaction is legitimate. These factors include weighing similar purchases from the card owner alongside additional information like the time of the transaction, place of transaction, nature of the transaction, and other factors. Such an application can only be possible using real-time analytics as traditional analytics would have already incurred a loss at the time of the analysis. 

Real-time analytics is also used by banks in the finance industry to prevent money laundering activities by flagging suspicious or dubious transactions. It is also used to issue credit requests to deserving customers by analyzing their previous transactions and spending habits in real time. The finance industry relies heavily on real-time data analytics to prevent fraud and optimize its operations.

Curious how financial services leverage Change Data Capture to prevent fraud in real-time? Read our blog on Leveraging CDC for Fraud Detection.


The manufacturing industry depends on real-time analytics to manage its processes such as inventory management, control processes, and maintenance. Real-time analytics can be plugged into inventory management to track, measure and finetune the management of materials and other inputs necessary to maintain and scale a healthy manufacturing process. The data obtained from inventory systems can be used to analyze when an item is about to go out of stock, what supplies may be running low and bring the manufacturing process to a halt, and what the possible workarounds may be. 


Real-time analytics can also be used to monitor the actual process of manufacturing by reading data from sensors attached to manufacturing equipment. This will give the organization a bird’s eye view of the entire process and help to eliminate bottlenecks or manufacturing issues. Data can also be collected from cameras to analyze the workflow dynamics and better understand how to plan the factory floor for greater efficiency. Real-time data analytics in the manufacturing industry can lead to an improvement of processes when applied strategically.

Advantages and Benefits of Real-time Data Analytics

As with any technology, there are massive benefits and advantages to implementing real-time data analytics. Some of the key benefits of real-time data analytics are explained briefly below.

Faster Decision Making

One of the most important benefits of adopting real-time data analytics is that it enables you to make decisions faster and more accurately. This is invaluable because decisions are based on the most up-to-date data. Companies no longer make decisions based on old or stale data and can always update their decision as soon as new data suggests otherwise.


Businesses that rely on real-time data analytics have a competitive advantage over those that don't. It is easier to analyze customer behavior in real time and react to address pain points and issues. This helps to improve the customer experience and the customer satisfaction for your brand and products. Another advantage is that you are more likely to recognize new market trends or sentiments based on the data you are analyzing. This can allow you to position your organization accordingly to maximize the opportunity since trends generally appear first in data before they are more broadly identified by the general public.


Organizations that employ real-time data analytics can offer a personalized experience to their customers. Data can be analyzed in real-time for the individual based on their unique interactions with the business and their experience can therefore be individualized. Personalized service, including how you market and sell to a customer based on their identified habits or needs, typically leads to higher consumer satisfaction and retention rates.

While many different retail use cases utilize real-time data to be successful, the benefits of switching to a real-time data strategy have already been proven. A recent survey showed that 80% of companies surveyed have reported a revenue uplift due to leveraging real-time data. Ultimately, retailers need to make faster decisions than their competition. Adapting to consumer behavior as it happens is essential, making real-time use cases the number one priority for many organizations at all levels. 

Data Visualization

Real-time data that flows through a real-time analytics engine can be visualized to give an easily digestible view of the data and trends. Unlike historical data that communicates an idea about the past, real-time analytics can serve as a data-driven dashboard that shows how decisions affect processes in real-time. Visualization dashboards are a great way to make real-time analytics and the outputs from them easily understandable.

Lower Costs

Real-time analytics can be used to identify inefficiencies and eliminate them. This can lead to improved productivity and lower costs. Insights uncovered through real-time analytics may help to reallocate resources to where they will have the greatest impact. It also helps to eliminate waste and reduces delays in an organization as data is readily available and can be acted upon.

Use Cases For Real-time Analytics

One of the best ways to understand the impact that real-time analytics can have on a business is to look at specific ways they could be used. Below are some use cases to highlight the benefits and applications of real-time analytics.

Information Security

Real-time data analytics is used in information security to analyze usage patterns for software and IT infrastructure. This analysis can help to identify security risks and alert the appropriate teams. It can help to mitigate risks identified in systems before the impact is widespread or before permanent data loss occurs. Real-time analytics is also used by organizations to make sure that they are obeying data compliance regulations, such as those set out by certain governments. By analyzing the flow of information and data coming and going from the system, real-time analytics could assist in blocking or alerting an organization about unauthorized usage.

Personalized Marketing

Real-time analytics can be used to offer personalized marketing to individuals based on their interests or location. For example, a user close to a geographical location of a physical store can be prompted with discount offers that encourage them to visit the store. Similarly, an online e-commerce store could recommend products to its users based on the items in the shopping cart or based on previous purchases. Personalized marketing efforts usually have a higher engagement rate and can be effective when combined with real-time analytics.


In the healthcare industry, wearable devices now exist that can monitor the key vitals of an individual. These devices can send data that can be analyzed in real-time to bring about timely intervention that could save lives. Live data can also be combined with the health records and historical data of individuals to spot trends that could help in the diagnosis and prediction of potential health issues.

Examples of Real-time Data Analytics

In this section, you will look at actual examples of real-time analytics that are being used in the world today. Many readers have likely come across many of the examples listed below in their day-to-day life.

Emergency Services

Real-time analytics is used by emergency services and humanitarian workers to analyze live input data and determine the appropriate responses. For example, drone footage can be analyzed to better understand hard-to-reach areas soon after a natural disaster like an earthquake or a wildfire. This information can then be combined with other data sources such as weather forecasts and geospatial data to plan an appropriate response or a proper course of action. The information will continue to be analyzed in real-time, even as first responders are sent out. As time progresses, new instructions can be given to first responders if the situation shifts or changes.

Fake News Detection

The proliferation of user-generated content online, particularly on social media, means that content can go viral and reach a broad section of the population very quickly. Media organizations are no longer the only source of information as citizen journalism is on the rise. Many of these new sources of information may not be as vetted as traditional media and can lead to hoaxes, propaganda, or outright lies. Social media companies cannot scout the entire content generated on their platforms manually to approve the publication of an article or post. This is why real-time data analytics is now being employed to track and detect erroneous sources of information and label them as such or take them down completely. Failure to analyze the data in real-time can lead to widespread misinformation and potentially deadly consequences. Real-time data analytics is therefore used by large technology companies to automate the management of user content on their platforms.

Precision Policing

Real-time data analytics can be used by law enforcement agencies to analyze sound data that can detect gunshots and use relevant location data to identify the vicinity of the sounds and respond proactively. This is possible through the real-time analysis of sound sensors and cameras to detect possible activities of interest to law enforcement. Once a potential incident has been identified, an actual team can be sent out to investigate further.

Challenges and Limitations in Real-Time Data Analytics

To successfully implement real-time data analytics in an organization, it is important to understand the possible challenges and address them at the implementation stage. Below are some challenges and limitations of real-time data analytics.


The definition of what “real-time” really means as it relates to your organization or project must be agreed upon by all interested stakeholders. Real-time can be interpreted as milliseconds in one company or a few minutes lag in the other. This is to align expectations so that the requirements of the system are acceptable to all. Once this has been defined, it becomes easy to measure the effectiveness of the solution designed or implemented. The requirements will also determine the type of architecture used and the number of resources allocated to the process.


The architecture of a real-time analytics solution should be based on the definition and requirements of the real-time system agreed upon by stakeholders. Any real-time solution should be highly available and have low latency, therefore the architecture chosen should support these concerns in addition to being scalable.

Implementation and Training

The implementation of a real-time data analytics solution should not be done in isolation but should work seamlessly with the existing data sources and processes of the organization. Technical training may need to be provided to staff to enable them to operate the new systems and transition into the new way of doing things.

Business Processes

During the implementation of a real-time data analytics system, some business processes may need to be modified to maximize the advantages of using real-time analytics. It will be a lot more effective to modify faulty internal processes to take advantage of the new system than viewing real-time analytics as a silver bullet that can solve poor organizational processes.


In this article, you were first introduced to the concept of real-time data analytics, what it represents, and how it can be of use to organizations and companies. Then you were given a more in-depth definition of real-time data analytics and the types of real-time data analytics which include on-demand and continuous real-time analytics. We then showed three industries that make use of real-time data analytics in their operations. A detailed breakdown of the advantages and benefits of real-time data analytics was provided and you were shown further use cases for real-time data analytics. Examples of real-time data analytics in the real world were highlighted in the areas of emergency services, fake news detection, and precision policing. Finally, we reviewed the challenges and limitations to watch out for as you implement real-time data analytics. 

When it comes to real-time data architecture, using a data pipeline that can move data from a source to an analytics platform in a matter of milliseconds is important. Arcion, with its no-code approach and abundance of connectors, is the perfect solution for getting data to where it needs to be. Power your real-time analytics with Arcion and your favorite big data analytics platforms, such as Databricks or Snowflake. Want to learn more about how we can help? Chat with us today.

Matt is a developer at heart with a passion for data, software architecture, and writing technical content. In the past, Matt worked at some of the largest finance and insurance companies in Canada before pivoting to working for fast-growing startups.
Luke has two decades of experience working with database technologies and has worked for companies like Oracle, AWS, and MariaDB. He is experienced in C++, Python, and JavaScript. He now works at Arcion as an Enterprise Solutions Architect to help companies simplify their data replication process.
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