What is Diagnostic Analytics? Advantages, Differences Between

What is Diagnostic Analytics?

Diagnostic analytics is used to find the root cause of a given situation. It can also be used to find the casual relationship between two or more data sets if the root cause is not detectable. The analytics team or person must be careful about selecting relevant data for analysis or for finding relation among more than one data set.

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Let us take an example where you have done descriptive analytics and it shows low sales on your online grocery store website. Followed by some event checks and analysis, it occurs to you that users are adding items in the card but are not checking out. You now come to a conclusion that there is some issue with user experience on your website, but what is it precisely?

There exists many factors which could be affecting sales, such as the payment page is not using more secure https web service, the payment options form does not work or an unexpected charged amount appears on the page. Hence, diagnostic analysis enables you to present a picture and the cause behind it, which is not apparent in the presented data.

Advantages of Using Diagnostic Analytics

Diagnostic analytics help users to recognise questions of some kind such as what is going to happen in the future concerning updates, operational setting and so on. Some of the benefits of using diagnostic analytics are as follows:

• Obtain modified and precise answers: As you think related to the opportunity or events. This means that your finding is not precise to your business.
  
  
• Obtain data-driven descriptions, not estimations: Diagnostic Analytics depend on firm data and technical tools to reach at its conclusions. Through techniques like data discovery, data mining, drill-down, Diagnostic Analytics can process terabytes of data within minutes to look for relationships and actions across a multitude of variables.
  
  
• Avoid reiterating historical faults: Diagnostic analytics help users to understandthe relationship between multiple variables associated to your consequence. If you understand the reason behind the conclusion, you can yield preventive measures to snub same results in the future.

Differences Between Diagnostic and Prescriptive Analytics