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Real-Time Analytics

What is Real Time Analytics

The Internet of Things is here and is definitely here to stay. With over millions of devices currently connected and billions more or even trillions devices going to get connected in the next decade. Data will be growing at a tremendous rate and this is what organisations have to consider and be prepared for.


Sensors are used to tag assets and these embedded sensors become part of long-term assets and, collect and transmit data. The telemetry data gathered through the sensors is processed in the cloud-hosted IOT Sensor Management system. This cloud-hosted platform acts as the backbone of the system integrating it with the end-user applications as well as other enterprise systems and enables real-time, automated responses to achieve remote monitoring of assets.


The system will monitor for pre-defined rules and trigger alerts or notifications based on those conditions. Analyzing the data collected can be used for future improvements such as process improvements and security enhancement for potential lapses.


  • Enterprise data predicted to grow by 650 percent in the next 5 years
  • Estimation of 40 zettabytes (400 billion gigabytes) will be created by year 2020
  • There are currently 2.5 quintillion bytes of data being created on a daily basis

Waiting for critical information to be generated could often be frustrating. Every second counts when it comes to critical intelligence. Organisations needs these critical intelligence to be continuously generated in order to make strategic and decisive decisions. This Is when real time analytics comes in handy.

Why use Real-Time Analytics for your business?

Analyse and Predict

Utilise real-time big data analytics to predict accurately and make dynamic change.


Enhance and Move Forward

Businesses on traditional data platform need to upgrade and transition as the traditional data platform is no longer able to keep up with the ever increasing data volume these days.

TeBS can help you through:

Hassle Free Transition of Data

Highly experienced professionals transition traditional data platform to modern data platform like Microsoft, Open Source and IBM Big Data platforms.