The Advantages of Real-Time Analytics for Enterprise

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The use of real-time analytics can be a boon businesses, but only if properly used.

Real-time analytics is the ability of a business enterprise to use all available enterprise data when needed. A crucial feature of real-time analytics is that the available systems and setup should be able to quickly generate analytics based on the data received, ideally within a minute of the data being generated. A big advantage of real-time analytics is the freshness and the context of the data. Organizations can reap a lot of benefits by accessing real-time analytics purely because of their close relevance to market realities.

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Big Data — The Dimension to Unravel

As per Gartner’s definition, big data has 3 dimensions:

  • Volume — To develop a commercial end-to-end application for a business enterprise, all the incoming sensor data needs to be captured and stored. The voluminous data needs to be formally organized and synthesized to get the real meaning in real time.
  • Variety — The demand is to provide an application that can capture a variety of data, including textual format.
  • Velocity — Analyzing data using a batch process is not a feasible solution for real-time data. This works well when the incoming data rate request is slower than the batch processing rate. With the new sources of data like social media, mobile devices and sensors, the batch process is not effective.

Advantages of Real-Time Analytics

Because of the immediacy of real-time analytics, it can provide advantages over other analytic methods, including:

  • Sharing information in the transparent form of dashboards
  • Monitoring custom behavior
  • Effective decision-making mechanism
  • Immediate changes or fixes whenever necessary

What’s so Real About Real-Time Analytics?

Real-time refers the ability to process data at the very moment it arrives, rather than processing it later as part of a batch. So, the main significance is that the processing of data is happening at the moment, rather than in the future.

Real-time big data analytics can be defined as the ability to make better decisions and perform meaningful actions at the right time. It is about combining and analyzing data, so that the right actions can be taken at the right time and at the right place. The essence is to generate value from disparate data.

Needless to say, the end result is to increase sales and lower marketing costs.


Types of Real-Time Data Analytics

There are two types of real-time analytics:

  • On-demand real-time analytics — This is a reactive approach. It awaits a query from the end user to process a request and then deliver the analytics. For example, a web analyst monitors site traffic to avoid a potential crash of the website.
  • Continuous real-time analytics — This is a proactive approach. It alerts users with continuous updates in real time. For example, tracking the stock market with various visualization representations on a website.

Advantages of Real-Time Analytics on Enterprise

Big data is at a juncture full of possibility and opportunity, but at the same time, it is ever challenging. The objective of real-time analytics is to convert voluminous data into the customer and business insights that will let your company move forward. The need of the hour is to make good use of big data and analytics, and to present these as solutions to address business problems.

Real-time continuous analytics can address these problems:

  • Capture the real-time and historical sensor data and analyze it further
  • Evaluate the patterns of normal and errant behavior

Use Case 1: Transportation Industry

In the transportation industry, trucks and trailers are equipped with sensors and global positioning systems (GPS) to track not only routes but also timely delivery. The objective is to optimize the routes that result in the fastest delivery. These sensors go even a step further by reporting about safe driving skills and fuel economy habits.

These real-time analytics are not only ensuring timely delivery but also saving millions of dollars in fuel costs. Logistics companies have eliminated waste from routes and conserved energy, and of course the customers are pleased with improvements in on-time performance.

Use Case 2: Technology Industry

With the rapid growth of social media sites as a communication channel between businesses and the entire community, the model serves as an interactive and simple channel to connect with customers. You can use web analytics to integrate and analyze the data to build a personalized association with the customer.

Real-time web analytics can address these problems:

  • Social media analysis to predict the end-user patterns
  • Enhance sales by reinforcing the products an end user is interested in
  • Holistic customer view to engage the interest level

Use Case 3: Manufacturing Industry

In the manufacturing industry, companies are constantly busy trying to reduce the cost in processing a product from beginning to end. With the advent of real-time analytics and an enormous amount of big data kicked out by sensors and other machine-based communications, companies are heading toward process automation activities and reducing dependency on manual labor operations.

Needless to say, the automation process would still require human judgment. From a global company’s perspective, the objective is to improve the process automation driven by big data and embrace it holistically. To explain it further, the process automation drive lets you integrate the manufacturing workflows, so that completion of a process in the U.S. can trigger a subsequent flow of work processes in a factory located in India. And the best part is this is all driven by data and analytics.

Use Case 4: Oil and Gas Industry

Let us imagine a scenario in which a component has failed earlier than expected in an oil drill. This can be an expensive and time-consuming task for the business because the required tools or people may not be available to address the problem in real time. Also, at the same time it is equally difficult to maintain an inventory of all parts or tools, as it may result in unnecessary cost.

Using real-time continuous analytics, you can predict equipment failure and take appropriate corrective action well ahead of time. Hence, this saves a lot of operational cost by allowing you to perform preventive maintenance rather than incurring the cost of an emergency repair.


We are part of a global village in which everything is dispersed, but well connected. The common element that connects us is ever-changing data.

To tap into the power of data, real-time analytics is gaining popularity for the simple fact that we have become a real-time society. Data has always been valuable, but now it has become a commodity. Nowadays, people expect immediate access to the information they are seeking, and want to experiment with applications to bring new insights that allow them to make decisions on what to do next with the data.

In my opinion, the biggest challenge is not only about voluminous data but how to make it a valuable piece as information in the form of a solution. The paradigm should be:

Data -> Information -> Wisdom

The challenges are enormous and the industry is on constant lookout for a solution to address these serious issues. The way forward is to devise a solution using the historical data and new analytic models to address the business challenge. Remember, the most important thing to ask yourself whenever you intend to apply real-time analytics to big data, is “What is the purpose of this data?” If you’re using real-time data, measure only what matters to the enterprise and not everything by and large.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…