Question

What are some patterns that could be found using diagnostic analysis? Between which types of variables?

Answer 1

Q.1 What are some patterns that could be found using diagnostic analysis? Between which types of variables?

Answer : Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” It is characterized by techniques such as drill-down, data discovery, data mining and correlations.

The goal of the diagnostic analytics is to help you locate the root cause of the problem. To do so, the algorithms use owned proprietary data, and leverage outside information to understand what exactly happened and help you find a quick fix.

Analytics solutions ultimately aim to provide better decision support — so that humans can make better decisions augmented by relevant information. Decision support capabilities can be segmented into five related categories, each of which is deployed to answer different types of questions:

• Identify anomalies: Based on the results of descriptive analysis, analysts must identify areas that require further study because they raise questions that cannot be answered simply by looking at the data. These could include questions like why sales have increased in a region where there was no change in marketing, or why there was a sudden change in traffic to a website without an obvious cause.
• Drill into the analytics (discovery): Analysts must identify the data sources that will help them explain these anomalies. Often, this step requires analysts to look for patterns outside the existing data sets, and it might require pulling in data from external sources to identify correlations and determine if any of them are causal in nature.
• Determine causal relationships: Hidden relationships are uncovered by looking at events that might have resulted in the identified anomalies. Probability theory, regression analysis, filtering, and time-series data analytics can all be useful for uncovering hidden stories in the data.