What is Real-Time Data?

June 3, 2021
What is Real-Time Data?

Real-time data refers to the  technologies and systems that measure, process, and analyze the data your organization collects as it enters your databases. Analyzing data in real-time allows companies to adapt to circumstances as they develop, rather than after the fact. 

The applications of real-time data are incredibly broad, and it can be used in any industry from healthcare analytics, to adtech, to travel risk, and more.

Which is why this technology has been making waves recently. According to a survey of 450 CFOs conducted by Accenture, 99% believe real-time data is critical for navigating the challenges they face. What’s more, while the CFOs were near unanimous on the utility of real-time data, only 16% said that they are currently getting this data at the scale that is needed.

This accounts for the big investments organizations are making in this technology. According to the same report,  respondents say that they are (on average) investing 33% of their finance departments’ budgets to developing real-time data processing operations and processes. U.S. companies  with over US$10 billion in annual revenue are investing even more, on average at  least 50% of their (2020-2021) finance-department budgets. The CFOs surveyed said they hope to have completed these projects within the next 3 years.

Notably, none of the respondents had to ask “What is real time data”? 

Real-Time Data Pros and Cons

There are many benefits of real time analytics, but it can also have drawbacks, especially when it is not implemented in a well-planned manner. First, let's take a look at some of the pros: 

  • Real-time decision support: Data is , in essence, a tool for informing decisions. If you have data in real-time, you can act the moment your business is ready, rather than waiting to act until after a lengthy data gathering process
  • Empower your teams: The decision making speed real-time data provides is a huge force multiplier, but you don’t always want to be making fast decisions on an organization-wide scale. But what can you do is feed real-time data to your teams, empowering them to put out small fires as they arise. 
  • Detect and аddress issues: If your organization works with industrial production or product distribution for example, live video and sensor data fed to an AI can detect backlogs, mechanical issues, and distribution problems as soon as they start. Live video data can also be used for loss prevention in retail. 
  • Respond to current events: Unpredictable events can have a big impact on markets, stocks, and even the price and demand for common household goods. Real-time data lets organizations adjust for events instantly. For instance, live analytics enable automatically updating prices or cancelling the sale of flight tickets to a troubled destination
  • Personalize the online or retail marketing experience: That online stores use cookies to track users and present custom offers is already widely known. But this tech is also entering the retail and restaurant space. For example, fast food chains with self-service kiosks can use cameras and facial recognition technology to identify a user and offer them a special deal based on their usual favorites. 
  • Improve customer service: Real-time data has made  it much more pleasant to deal with customer service representatives than it has been in the past. Real-time data tools can provide a service rep with up-to-the minute information about issues in a customer’s area or problems with their device the moment they call. 
“In many industries, a real-time data capability is becoming a must-have to stay competitive. However, due to the complexity of the systems involved, enterprises can’t pivot to real-time over night. It’s critical that companies making this transition find an experienced technical partner to guide them through the process, and help them best identify the areas where this will most help their business.”

George Serebrennikov, COO at Proxet

This is far from an exhaustive list of the benefits real-time data can provide, but it should give you a sense of the wide range of possible applications of the technology.  

But there are drawbacks as well, which we will cover below.

Hadoop and Real-Time Data

The industry standard tool for working with large data-sets, Hadoop, is not able to work with real-time data natively, which can be an issue for organizations used to doing most of their data work with this tool. 

There are some integrations and tools out there that can help Hadoop to work with this data, but it is an additional thing you’ll have to set up. 

In addition to the Hadoop issue, there’s the question of raw computing power. If your organization is not approaching the likes of Google and Amazon in sheer scale, it’s a near certainty that you don’t have the computing power on hand to deal with large amounts of real-time data. You’ll need to rely on cloud computing options.

It can also be a challenge to adjust to the speed of data intake, especially for organizations used to getting data updates once a week, the sheer breadth and depth of information real-time data provides can be overwhelming. 

Image By Proxet. Real Time Analytics Challenges
Real-Time Analytics Challenges

It’s not just analyzing data in real time that can be challenging. Properly utilizing real-data also requires changing your whole approach to data collection.

Data must be collected constantly, rather than just during specific actions or events, which means your organization may have to implement a completely new set of tools and collection software. Especially if you need to do this at scale, this overhaul will require significant investment. 

Any time you make changes to the way your company handles data, it’s important to do it methodically and carefully. Data, especially user data, is very regulation-sensitive. Moving too fast can cause you to neglect the details of your jurisdiction’s relevant laws -- potentially resulting in multi-million dollar fines. 

Markets to use Real-time Data In

Let’s take a look at some real-time processing examples from a range of industries:


If you use a fitbit, Apple watch or similar, you are already using real-time data to take care of your health. But of course, at an enterprise scale the technology becomes much more sophisticated. Remote health monitoring, for example, allows physicians to monitor their patients’ care from a distance.


Real-time customer analytics are an absolute must for improving the experience customers have with your brand. It also helps you get the right information to the right customer.  As more and more organizations are implementing this technology, customers expect personalized interactions with brands, which is why 44% of enterprises gain new customers and increase revenue as a result of adopting and integrating real-time data.  


Real-time data can make your supply chain much more efficient. This is part of what enables Amazon to do next-day deliveries across the US. Shippers can use real-time information to understand shipping trends, and reduce costs by eliminating inefficient routes, while also delivering better customer experiences.


Real-time analytics is critical in the financial services industry. Financial institutions use real-time data to improve customer offerings, detect fraud, and implement sophisticated trading strategies that respond to events in real-time.

Travel Risk

Real-time data is the backbone of the travel risk industry. Service providers use real-time data to source breaking news stories and other information to analysts as they happen. This enables the analyst to determine whether or not an area is safe for travel and if personnel may need to be evacuated. 

Real-Time Analytics vs. Near-Real-Time Analytics

It’s important not to confuse real-time and near-real-time analytics. Near-real-time (or near-time) data processing and analytics is quick, but not instantaneous.

Near-real-time processing has many effective use cases, but it often is no substitute for real-time, especially in high-stakes industries like finance, healthcare, and cybersecurity. Advertising is also an example of an industry where real-time data is needed, to present the best offer to the customer at exactly the right time.

Real-Time Analytics Platforms and Tools

Now, let’s take a look at some of the most popular tools that organizations use to implement their real time big data analytics.  

Amazon Kinesis

Amazon Kinesis is one of the best real time software examples, used for building streaming applications using SQL editor, and open-source Java libraries. Kinesis leverages Amazon’s cloud computing ability to do all the heavy-lifting, running the applications and scaling to match requirements as necessary. This means you don’t have to manage your own servers and other complexities of building, integrating, and managing applications for real-time analytics.

Google Cloud DataFlow

Based on Python 3 and Python SDK, Cloud DataFlow is the latest real-time data analytics offering from Google. This sophisticated platform allows firms to filter data that is ineffectual and hampering the analytical process. Using Apache Beam with Python, you can create data pipelines to extract, transform, and analyse data from IoT devices and other data sources.

Azure Stream Analytics

Azure Stream Analytics is designed for doing real time big data analytics end-to-end analytics within a short time-frame using SQK, JavaScript, and C#. It has built-in machine learning capabilities to assist you in processing data intuitively. This feature allows you to detect outliers, spikes and dips, and slow down  negative and positive streamed data trends to help users interpret the data visualizations. 

IBM Streaming Analytics

This tool offers Eclipse-based IDE as well as supports Java, Scala, and Python programming languages. It also allows you to write code  in notebooks so Python users can effortlessly monitor, manage and make informed decisions about their data. This real time data service can be used on IBM BlueMix® to process information in the data streams. 

Apache Storm

Built by Twitter, Apache Storm is a must-have tool for real-time data evaluation. Unlike Hadoop, which does batch processing, Apache Storm is specifically built for analyzing streams of real-time data. It can be also used for online machine learning.

Its ability to process data faster than its competitors is what differentiates Apache Storm from the other offerings on the market. It can also be integrated with Hadoop to further extend its ability for higher throughputs.


Striim is an enterprise-level platform that can process data via the cloud or on-premise. It gives users the ability to mask, aggregate, filter,  and transform data, while also offering built-in pipeline monitoring to maintain operational resilience while moulding data for insights. Through Striiim, firms can effectively integrate with various messaging and other similar platforms to harness data for real-time visualisation.


StreamSQL is so easy to use that even a  non-developer can create applications for manipulating streams of data and monitoring networks, doing surveillance, and implementing  real-time compliance. Since it is built on top of SQL it is fast, easy-to-use and analytics-ready.

Finding a Technical Partner

At Proxet, we have deep experience in helping organizations leverage the power of real-time data. If you are considering upgrading your company’s ability to analyze and act on large volumes of information, please get in touch.

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