Descriptive Analytics
What is Descriptive Analytics?
Descriptive Analytics in the artificial intelligence industry is about examining past data to provide insights into what has occurred within a system or organization. It helps businesses understand trends, patterns, and anomalies by summarizing large volumes of data into digestible information. This type of analytics doesn't predict future outcomes or prescribe actions; instead, it focuses on interpreting historical data to inform decision-making and strategy. For instance, AI tools can be employed to sift through customer transaction records, website interactions, or social media activity to produce comprehensive reports. These reports can highlight key performance indicators, customer behavior patterns, or operational inefficiencies, enabling businesses to make data-driven decisions moving forward.
Descriptive Analytics involves analyzing historical data to understand what has happened in the past in a business context.
Examples
- A retail company uses AI-driven descriptive analytics to analyze sales data from the past year. They identify peak shopping times, popular products, and seasonal trends, helping them to better manage inventory and plan marketing campaigns.
- A healthcare provider employs descriptive analytics to review patient records and treatment histories. This helps them understand the effectiveness of different treatments and identify common health issues, leading to improved patient care and resource allocation.
Additional Information
- Descriptive analytics is often the first step in the analytics process, laying the groundwork for more advanced analytic techniques like predictive and prescriptive analytics.
- It relies heavily on data visualization tools, such as dashboards and reports, to present information in an accessible and actionable format.