Diagnostic analytics is a branch of business analytics that focuses on identifying the causes of past events or outcomes. It involves analyzing historical data to understand why something happened and to identify the factors that contributed to the outcome.
Diagnostic analytics can be used to identify the root cause of problems or to explain why certain events occurred. For example, a business might use diagnostic analytics to analyze sales data and identify the factors that contributed to a decline in sales. They might also use it to identify the factors that led to a product defect or a quality issue in their production process.
Some common techniques used in diagnostic analytics include regression analysis, correlation analysis, and root cause analysis. These techniques can be used to identify patterns and relationships in data, and to determine which factors had the greatest impact on a particular outcome.
Overall, diagnostic analytics is a valuable tool for gaining insights into the causes of past events or outcomes. By analyzing historical data and identifying the factors that contributed to a particular outcome, businesses can take steps to address underlying issues and improve their performance going forward.