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Explore the Challenges and Uses of Real-time Analytics

December 20,2018 | No Comments  |  Posted In AutomotivePlatforms

What is Real-time Analytics?

Real-time analytics is a term used to refer to those analytics that accessible as soon as data is entered into the system. Normally, the word “analysis” is used to explain the data patterns that is meaningful to a business or other entity. Analysts would then get valuable information by sorting that data and analyzing it.

Real-time analytics is also known as real-time data integration, dynamic analysis and real-time intelligence, all of which allows businesses to respond without delay. BI insights from real-time analytics can allow businesses to overtake the curve. It can also seize opportunities or prevent problems before they occur.

Big Data analytics focuses on large data produced, consumed and stored in live environments. It is mostly used in industries or organizations which regularly produce large quantities of data in very short periods and this data is usually given to the administrator via analytics software dashboards.

Real-time Analytics’ Challenges

  • Latency inherent in high-volume data movement
  • Data movement between disparate sources and targets in complex environments

To be promptly useful, real-time analytics applications ought to have high accessibility and low reaction times. However, they should in any case return answers to inquiries within the span of seconds.

"Real-Time" is important to the handling of changing data sources, which heavily affects market and business factors. Real-time big data analytics are also used in the financial sector. They use data in financial databases, social media, and satellite weather stations to instantly inform integral buying and selling decisions.

Real-time Analytics Uses

The uses of Real-Time Analytics include:

  • In finance: Real time credit scoring and assisting financial institutions in making immediate decisions on whether to expand credit or not
  • In retail: Targeting individual customers in retail outlets with promotion and encouragement while the customers are in the store and are next to the business.
  • In supply chain: Predictive analytics/maintenance, with minimal disruption and cost. Beyond the factory, the supply chain can be optimized for raw materials because capacity planning and improvements in the process and quality management can be further enhanced.
  • Fraud detection at points of sale
  • In smart homes: Smart meters can be used not only to manage the energy efficiency at home, but data can be feed to the secondary substation allowing available energy to meet the demand.
  • In transportation: Traffic data can be fed into a centralized dashboard or returned to vehicles for driver action.

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Real-time analytics

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