Real-time data analytics is the process of analyzing data and related resources as soon as the data enters the system.
Also known as dynamic analysis or real-time data integration, it lets a user get insights and make strategic decisions very rapidly after that data becomes available.
It is widely used for sales and customer relationship management by personalizing the experience of the customer through information received about the customer while they are still in the store, both offline and online.
Benefit from Real-time Data Analytics
- Instant identification of errors– with the real-time analysis of the all the inflow of data, any error in your organization or business process are immediately brought to your notice, minimizing its damage by allowing an immediate response.
- Improve operational processes– Real-time data analytics can provide patterns of how all your resources are allocated and utilized, to identify an area for improvements. Bottom line, the efficiency of running the business increases.
- Keeping a close(r) eye on your competitors– real-time data analytics tools can notify you of all the strategic decisions taken by your competitors as soon as it is implemented.
- Return to investment– Implementing the tools is expensive, but with the ability to analyze and draw conclusions as they come in, the cost it saves, in the long run, is more significant especially by reducing decision-making time and the time taken to analyze data to respond to an unpredictable crisis.
- Advances in workplace engagement and productivity- various tools of real-time analysis allow management teams to examine data regarding the performance and feedback of the employees from multiple lenses, make comparisons, and meaningful improvements in policy, system, and the experience of employees.
Workplace performance is after all critical to the overall performance of the business.
Three essential features to look for in Real-time data analytics tools
- Ability to Support for various analytics – businesses are required to carry out analysis from multiple approaches, the data analytic tool should be flexible enough to handle various forms of analysis.
- Scalability- data only gets bigger over time, real-time analytics tools must have the capacity to scale the rapidly increasing size of the data to be processed.
- Data governance– it is a critical feature that is necessary to maintain regulatory compliances, data governance help in tracking data sources, its level of sensitivity, and the regulations one must comply with to use it.
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