big data analytics

Today, in this post we will read about big data analytics, first of all, know what big data is?

Contents

• What is big data
• big data analytics
• benefits of big data analytics
• types of big data analytics

what is big data

The term 'Big data' is used to describe a large amount of data.
Simply put, "big data means huge data". And this data goes on growing with time.
This data is so large and complex that it is very difficult to store and process through a traditional software application.
example of big data:
Here's an example to understand big data: -
Every day more than 500 terabytes of data are generated in the database of Facebook. This data is mainly generated from photos and video uploads, messages, comments, etc.

big data analytics
"Big data analytics is a process in which a large group of data is collected, organized and analyzed so that hidden patterns and useful information can be discovered."
In other words, "big data analytics is a process in which large data sets are examined so that organizations can get hidden patterns, market trends, customer preferences, and other useful information."
This information is used by the organization to improve its business decisions.
Data scientists and predictive modelers, through big data analytics, analyze data from very summary sources.

benefits of big data analytics
The benefits of big data analytics are as follows: -
1: - By doing so, the company or organization can make a better decision, i.e. it can make a better decision by accessing data from search engines and social media sites like  - Facebook, Twitter, etc.
2: - By this, the error of the company is detected very quickly. With the help of real-time insights into the errors, the company quickly resolves the problem.
3: - This is better customer service. When a company monitors the product used by the customer, it remains ready for any future failure.
For example  - Cars that contain real-time sensors, before the sensors crash, they tell the driver that there is some disturbance in the car.
4: - cost savings: - The cost of implementing big data tools can be very high. But these tools save a lot of money and these companies are very beneficial for the company. Through them, we can store large amounts of data. And these tools also identify effective ways of business.
5: - It saves time. Big data tools such as Hadoop and in-memory analytics are much faster. These tools easily identify new sources of data, so that data is analyzed very quickly and early decisions are taken based on learning.
6: - new product development: -Customers are using which product they are using and what they have needs are addressed by big data analytics. So based on these analytics, we can develop a new product according to the needs of the customer.
7: By this, we can understand the condition of the market, after analyzing the big data, we know what is the condition of the market. For example: - If the company finds out what the customers in the market Buying and selling which product is the most, the company will be one step ahead of its competitors.
8: - By this, the company can control its online reputation. Through big data tools, the company knows what the customers are giving feedback about the company.
If the company wants to monitor and improve its online reputation, it can do with the help of big data analytics and tools.
9: - This leads to fraud. The criminals do fraud online now, but if someone hacks the system of a criminal or hacker company, then the company gets its address immediately and the company's IT department can immediately take necessary action.
types of big data analytics
The following types of big data analytics are: -
1: - perspective analytics
2: - predictive analytics
3: - diagnostic analytics
4: - descriptive analytics

1. Perspective analytics
This is the most valuable big data analytics technique, it suggests the best solution among many choices. So that the suggested option can be availed. And future risks can be reduced.

2.Predictive analytics
The predictive analytics is most commonly used. It predicts something that can happen in that situation?
It uses statistical, data modeling, data mining, and machine learning techniques to predict the situation.

3.Diagnostic analytics
Data scientists use this technique when they want to know why something is happening, i.e. what is the reason behind this thing?
diagnostic analytics which analyzes the past performance

4.Descriptive analytics
This technique takes a lot of time and gives the least benefit.
What is descriptive analytics is that it provides an insight into the data such as: - summary statistics,
clustering and association rules etc.