Impact of Near Real Time Data on Data Science Model Predictions - Gautam, Ankush Ramprakash; Sharma, Ritu (International Journal of Computer Sciences and Engineering, 2024)
The article delves into an exploration of how the integration of almost real time data streams impacts the accuracy, strength and effectiveness of models, in the ever changing field of data science. groups go beyond boundaries to examine sectors carefully analyzing the effects of data velocity on model performance in industries like finance, healthcare and transportation. Through an investigation the article reveals a story that highlights not the many benefits but also examines the complex challenges involved in utilizing almost real time data for modeling purposes. Additionally the article takes a look at the details discussing the necessary setup requirements and explaining the various methodological approaches needed to seamlessly integrate rapidly updating data streams into existing modeling frameworks. The paper also covers considerations and privacy requirements, for handling data responsibly emphasizing the importance of preserving individual privacy and data integrity. In the end this research acts as a signal emphasizing the importance of utilizing nearly real time data to enhance predictive abilities and drive a significant change in how decisions are made in various fields. This pushes us towards a future of opportunities and transformative possibilities.