Augmented analytics uses artificial intelligence and machine learning to automate or assist parts of the data analysis process. Instead of requiring users to manually prepare data, identify patterns, and determine which findings matter, augmented analytics can help discover relationships, detect anomalies, generate forecasts, and surface insights from large datasets. It is designed to make analytical capabilities accessible beyond specialized data teams.
For example, a business analytics system may automatically identify a sudden decline in sales, determine which customer segment contributed most to the change, and highlight the trend for further investigation. Natural language queries, automated insights, predictive models, and intelligent data preparation are commonly combined in augmented analytics platforms.
