Real-time data processing and analysis

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Using AI to predict events also helps companies respond to market changes faster and adapt to new conditions. AI can not only predict, but also make decisions, minimizing human involvement in the process.
Previously, analytics-based predictions could only be made based on historical data. Today's technology allows data to be analyzed in real time and predictions to be made instantly. This is an important innovation for industries such as finance and retail, where the timeliness of decision-making can have a significant impact on profits.
For example, in retail, real-time predictive analytics can quickly adjust product ukraine consumer mobile number list prices based on demand, seasonality, or user behavior. In financial markets, it can help predict exchange rate fluctuations or stock price changes, giving investors a competitive advantage.
Forecasting based on new types of data
Traditional data, such as financial statements or purchasing data, will continue to be used in predictive analytics, but new sources of information are also emerging. This could include user behavior data on social networks, data from sensors and IoT devices, as well as text information from blogs, news, and forums.
Natural language processing (NLP) technologies make it possible to analyze texts and draw conclusions about people’s moods, trends, and even identify future consumer needs. This opens up new possibilities for predictions that are based not only on numerical data, but also on textual information.