Real-time data collection and analysis: Difference between revisions
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'''Real-time data collection and analysis''' refers to the [[process]] of gathering data and analyzing it in real-time, or near real-time. This means that the data is collected and analyzed as it is generated, rather than being stored and analyzed later. | '''Real-time data collection and analysis''' refers to the [[process]] of gathering data and analyzing it in real-time, or near real-time. This means that the data is collected and analyzed as it is generated, rather than being stored and analyzed later. | ||
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* '''Predictive analytics''': Real-time data can be used to predict future outcomes and make proactive decisions. | * '''Predictive analytics''': Real-time data can be used to predict future outcomes and make proactive decisions. | ||
Real-time data collection and analysis typically involves the use of technologies such as sensors, IoT devices, and cloud computing platforms. These technologies can be used to collect and transmit data in real-time, and then analyze it using advanced analytical tools such as machine learning algorithms and statistical models. | Real-time data collection and analysis typically involves the use of technologies such as sensors, IoT devices, and [[cloud computing]] platforms. These technologies can be used to collect and transmit data in real-time, and then analyze it using advanced analytical tools such as machine learning algorithms and statistical models. | ||
Overall, Real-time data collection and analysis allows organizations to gain a more complete and accurate understanding of their operations, customers, and markets, which in turn enables them to make better decisions, improve performance and increase efficiency. | Overall, Real-time data collection and analysis allows organizations to gain a more complete and accurate understanding of their operations, customers, and markets, which in turn enables them to make better decisions, improve performance and increase efficiency. | ||
== | {{infobox5|list1={{i5link|a=[[Harvesting strategy]]}} — {{i5link|a=[[Information processing]]}} — {{i5link|a=[[Ai in accounting]]}} — {{i5link|a=[[Computer information systems]]}} — {{i5link|a=[[Processing of information]]}} — {{i5link|a=[[Ai in manufacturing]]}} — {{i5link|a=[[Analysis and interpretation]]}} — {{i5link|a=[[Data and information]]}} — {{i5link|a=[[Telematics and informatics]]}} }} | ||
==References== | |||
* Kim, H. C. (2019). ''[https://www.koreascience.or.kr/article/JAKO201914260902411.pdf A Study on The Real-Time Data Collection/Analysis/Processing Intelligent IoT]''. The Journal of the Korea institute of electronic [[communication]] sciences, 14(2), 317-322. | * Kim, H. C. (2019). ''[https://www.koreascience.or.kr/article/JAKO201914260902411.pdf A Study on The Real-Time Data Collection/Analysis/Processing Intelligent IoT]''. The Journal of the Korea institute of electronic [[communication]] sciences, 14(2), 317-322. | ||
[[Category:Quality 4.0]] | [[Category:Quality 4.0]] | ||
Latest revision as of 04:30, 18 November 2023
Real-time data collection and analysis refers to the process of gathering data and analyzing it in real-time, or near real-time. This means that the data is collected and analyzed as it is generated, rather than being stored and analyzed later.
There are several benefits to real-time data collection and analysis, including:
- Faster decision-making: With real-time data, organizations can make decisions based on the most up-to-date information, rather than relying on outdated data.
- Improved efficiency: Real-time data can be used to optimize operations and improve efficiency by identifying and addressing problems as they occur.
- Better customer service: Real-time data can be used to improve customer service by identifying and addressing customer needs and concerns in a timely manner.
- Better risk management: Real-time data can be used to identify and mitigate potential risks, such as detecting and preventing fraud.
- Cost savings: Real-time data can be used to identify and address inefficiencies and reduce costs.
- Predictive analytics: Real-time data can be used to predict future outcomes and make proactive decisions.
Real-time data collection and analysis typically involves the use of technologies such as sensors, IoT devices, and cloud computing platforms. These technologies can be used to collect and transmit data in real-time, and then analyze it using advanced analytical tools such as machine learning algorithms and statistical models.
Overall, Real-time data collection and analysis allows organizations to gain a more complete and accurate understanding of their operations, customers, and markets, which in turn enables them to make better decisions, improve performance and increase efficiency.
| Real-time data collection and analysis — recommended articles |
| Harvesting strategy — Information processing — Ai in accounting — Computer information systems — Processing of information — Ai in manufacturing — Analysis and interpretation — Data and information — Telematics and informatics |
References
- Kim, H. C. (2019). A Study on The Real-Time Data Collection/Analysis/Processing Intelligent IoT. The Journal of the Korea institute of electronic communication sciences, 14(2), 317-322.
