What is the difference between descriptive and predictive analytics?

Descriptive analytics focuses on summarizing and interpreting historical data to understand what has happened. This involves techniques such as reporting, data visualization, and basic statistical measures to provide insights about past performance or trends. It gives organizations a clear picture of their historical data, allowing them to identify patterns and make informed decisions based on that information.

On the other hand, predictive analytics utilizes data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. By analyzing past data, predictive analytics can help forecast future trends, behaviors, and events, enabling organizations to make data-driven decisions and take proactive actions rather than just reacting to past situations.

The other choices do not accurately capture this distinction. For example, the statement that descriptive analytics analyzes future data while predictive analytics analyzes past data is fundamentally flawed, as it misrepresents the focus of both forms of analytics. The claim that both analyze historical data in the same manner ignores that predictive analytics actively seeks to forecast and model future scenarios. Lastly, describing descriptive analytics as qualitative and predictive analytics as quantitative does not reflect the nature of their methodologies; both types can utilize quantitative data and methods, although their applications and purposes differ significantly.