TYPES OF DATA ANALYTICS
In this modern world, is it sufficient for a company to rely upon a particular type of analytics? The answer is No. Because it is not an efficient way to derive insights for the company. So, in this blog, I came up with a picture called “Types of Data Analytics”.
What are the types of Data Analytics?
There are 4 types of Data Analytics:
● Descriptive Analytics (What happened already?)
● Diagnostic Analytics (Why did it happen?)
● Predictive Analytics (What is going to happen?)
● Prescriptive Analytics (What do I need to do?)
Descriptive Analytics:
Descriptive Analytics is a process of analyzing the raw data and providing solutions to the questions of what happened already? In Business, the analysis can find key metrics and measures within the Business.
Example:
- What is the profit percentage in Chennai city this year?
- Which branch has a high no. of sales?
- Whether the loss is high or low this year compared to last year?
- Which marketing campaign channel performed well?
- What type of products the customers preferred the most?
Diagnostic Analytics:
On the assessment of Descriptive Analysis, the next step of analytics is Diagnostic Analytics. In this type of analytics, you analyze the data and give solutions to the question “why did it happen?” This process gives the reason for the problem or something good happened.
Example:
- Why are the sales very less in the south region?
- Why is our profit percentage increased even though we did not change the marketing strategy?
- Why is there a continuous loss for the last two years?
- Why did so many customers leave this month?
- Why cannot this product find a place in the market?
Predictive Analytics:
Predictive Analytics is more about forecasting and analyzing future trends. It answers the question “What is likely to happen?” based upon the past datasets. In this process, more of no. variables or parameters are involved. So these variables are directly correlated with the target variables. In this type, we use predictive models like regression models, Timeseries, RNN, and LSTMs.
Example:
- What is my sales percentage in the south region in 2025?
- What is my profit percentage in India in 2028?
- Whether the sales will increase or decrease in 2024?
- Is there any change in trend in the future?
- How many customers will retain in 2026?
Prescriptive Analytics:
On the successful assessment of Descriptive, Diagnostic, Predictive analytics, the last one is Prescriptive Analytics. The purpose of prescriptive analytics is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. Prescriptive analytics uses advanced tools and technologies, like machine learning, business rules, and algorithms, which makes it sophisticated to implement and manage.
Example:
- To save the company from the loss that is likely to happen in 2025, the company should do some innovation in the product and increase the marketing channels and change the marketing strategy from today itself.
I hope that you find something useful in this article and thanks for spending your valuable time reading this article.
