Real time data analytics enables users to analyze and draw data insights in real time. My name is Santoshkumar Pandi an experienced Data & Analytics professional with over 11+ years of experience. As a Data & Analytics leader my expertise in data engineering, business intelligence, data science and working with cross functional business functions and stakeholders would help me share great real insights on how to use Real Time Data Analytics and drive the key decisions through data insights.
Real time analytics is getting visibility to data as the transaction occurs and enters into the data storage. It lets users to see, analyze and understand data and make key decisions in real time. One of the closest use case of real time data analytics is with stock exchange data, users would like to see the market trends in real time to make key decisions of buying or selling stocks. Real-time analytics allows individuals and businesses to react without delay. They can seize opportunities or prevent problems before they happen.
By comparison, batch-style analytics might take hours or days to yield results. Usually, batch analytical applications or tools would yield only “after the fact” insights. Real-time analytics would help to get ahead of the curve.
How do Real Time Analytics Work?
Real time data analytics tools or solutions can push or pull data. The response time for real time analytics tools can vary from nearly instantaneous to a few seconds or minutes (Sisense, 2022). Trading platforms would need to respond to new information within seconds. Retail products can get by with a minute between updates.
The components of real-time data analytics include:
- Aggregator — Compiles real time streaming data analytics from many different data sources.
- Broker — Makes data in real time available for use.
- Analytics Engine — Correlates values and blends data streams together while analyzing the data.
- Stream Processor — Executes real time app analytics and logic by receiving and sending data streams.
Who Uses Real Time Analytics?
- Real time stock platforms, e-commerce retailers, financial banks to decide immediately on the moving trends of data and take key decisions.
- Targeting individual customers in retail outlets with promotions and incentives, while the customers are in the store and next to the merchandise.
- Customer relationship management (CRM), maximizing satisfaction and business results during each interaction with the customer.
Keep tuned to my next post release, I would cover the most important topics related to Real Time Data Analytics : Benefits of Real Time Data Analytics. Come back and review this information on 1.24.2022.
References
Sisense. (2022, Jan 22). Real-time-analytics. Sisense. https://www.sisense.com/glossary/real-time-analytics/