Back in the 17th century, John Dryden wrote, “He who would search for pearls must dive below.” The quote perfectly describes its essence and surely that time author did not have advanced data analytics in mind.
Before coming on Diagnostic Analysis let’s have a brief understanding about type of analysis and why these are required:
- Descriptive Analysis: To know “What is happening in my Business?”
- Diagnostic Analysis: To know “Why is it happening in my Business?”
- Predictive Analysis: To know “What is likely to happen in the future based on previous trends and patterns”?
- Prescriptive Analysis: To know “What are the best course of actions to choose to bypass or eliminate future issues?”
- Cognitive Analysis: It’s a combination of technologies like AI, ML, Deep Learning etc to apply human brain-like intelligence to perform certain tasks and support in above mentioned 4 cases.
Diagnostic Analysis is about identifying the reason behind the challenge/issue, allowing analysts to go down deeper into the data and find out the dependencies and patterns. It is the most important tool for organizations to know such factors which are affecting the KPIs, and also uncover the relationships and stories in the data which are creating those issues/challenges. This can be done using data mining techniques such as regression analysis, anomaly detection, clustering analysis, and others.
The biggest upside of diagnostic analytics is being able to provide context to a business problem through a number of data models.
Although diagnostic analyses rely on the speed and accuracy of machines, it is important for human analysts to not misinterpret patterns as “causation” of a business problem. Instead, this information should be used to support decision-making.
For many businesses, understanding “what” the problem was and “why” it occurred may be enough, but for some, looking ahead to the future holds more valuable answers. This is where predictive analytics come in.