Data analytics is a trending domain, and people are attracted to it. The reason is not one but many. Data analytics offers high-paying jobs and gives you many options to choose from when choosing an industry. Since data analytics is needed for every industry, you can be anywhere or transition to any industry.
However, you must know that there are four types of data analytics before you make a career in Data Analytics. So, let's explore these four types of data analytics in this blog.
Descriptive Analytics
The first type of data analytics is descriptive Analytics, which deals with questions like What happened? In Descriptive Analytics historical data is analyzed to know the trend in the past. The process of Descriptive analytics included structuring the data and identifying patterns and trends.
In this process, the relationship between various events around the targeted event is also analyzed. In business, there is no need to explain why it is essential. Many events keep occurring in businesses, and identifying them can help create a strategy in advance if the signs of the side event are seen.
For example, a company did Descriptive analytics where they found that their sales reached all-time highs in June and July for the last three years. So, based on this insight, the company can make informed decisions on their production, delivery, and marketing in June and July. They will increase production and also make the supply smooth. Moreover, they may also cut budgets on marketing in this period as the sales are already up.
Diagnostic Analytics
Now, from what happens, let's move to why it happened. Answering why it happened is also important for many reasons, such as dodging a negative event that occurs again. Similar to the day, the oast data is also analyzed in Diagnostic Analytics. There are two further types of Diagnostic Analytics: "query and drill down" and "discovery and alert".
Query and drill down are basically when a query or an event is available to study, and the past is drilled down to know the cause. Similarly, the discovery and alert are done the same way, but it is most used in businesses to know the events, their cause and their potential of happening again. So, the signs are also evaluated in discovery and alert, which can let the concerned people know that the event can occur again.
A simple example of Diagnostic Analytics is: let's say a company, ABC Private Limited, gets to know that their sales went down for the last month, but the cause is unknown as there were no changes in demand, supply, production, etc.
However, when the company used Diagnostic Analytics, the insights showed that there were more public holidays in the previous month, which does not happen every month. This example of Diagnostic Analytics is overly simplified, but it gives you an idea of its use in a business.
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