What is Big data? Big data analytics. Types of big data analytics.

What is big data?

 'Big data' is a term used to describe very large amounts of data.

Big data

 Simply put, "big data means huge data".  And this data keeps on increasing with time.

 This data is so large and complex that it is very difficult to store and process it by traditional software application.

 Example of big data: -

 To understand big data, let us see the example: -

 facebook: - More than 500 terabytes of data is generated in facebook database every day.   

The data is mainly generated from photos and video uploads, messages, commenting etc.

 Big data analytics

 "Big data analytics is a process in which large groups of data are 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 the organization can get hidden patterns, market trends, customer preference and other useful information."

 Organisation uses this information to improve its business decisions.
 Through big data analytics, data scientists and predictive modelers analyze data from many sources.

 benefits of big data analytics

 Following are the benefits of big data analytics: -

 1: - Through this the company or organization can make better decision making, that is, it can make a better decision by accessing data from search engines and social media sites like: - Facebook, twitter etc.

 2: - Through this, errors are detected in the company very quickly.  With the help of real time insights in errors, the company resolves the problem quickly.

 3: - This improves customer service.  When a company monitors the product used by the customer, it is ready for any subsequent failure.
 For example: - Cars which have real time sensors, those sensors tell the driver before the accident that there is some fault 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 amount of data.  And these tools also identify effective ways to do business.

 5: - It saves time.  Big data tools such as hadoop and in-memory analytics have a much faster speed.  These tools easily identify new sources of data, thereby analyzing the data very quickly and based on learning, quick decisions are taken.

 6: - New product development: - Which products are customers using and what are their needs, they are detected by big data analytics.  So based on these analytics, we can develop a new product according to the customer's requirement.

 7: - By this we can understand the condition of the market, after analyzing the big data, we get to know what is the condition of the market, for example: - If the company finds out which product customers in the market  If you are buying and which product is being sold the most, then the company will remain one step ahead of its competitors.

 8: - Through this, the company can control its online reputation.  Through the big data tools, the company gets to know that what customers are giving feedback about the company.  If the company wants to monitor and improve its online reputation, it can do it with the help of big data analytics and tools.

 9: - fraud is detected by this.  Nowadays criminals commit fraud online but if any criminal or hacker hacks the company's system, then the company gets to know it immediately and the company's IT department can take necessary action immediately.

 types of big data analytics

 The following are the types of big data analytics: -

 1: - perspective analytics
 2: - predictive analytics
 3: - diagnostic analytics
 4: - descriptive analytics

 1.perspective analytics

 It is the most valuable big data analytics technique, it suggests the best solution out of many choices.  So that the suggested option can be availed.  And future risks can be reduced.

 2.predictive analytics

 predictive analytics is most commonly used.  It predicts a situation what can happen in that situation?

 It uses statistical, data modeling, data mining and machine learning techniques to predict the situation.

 Simply put, "predictive analytics is what predicts events in the future."

 3.diagnostic analytics

 Data scientists use this technique when they want to know why something is happening, that is, what is the reason behind this thing happening?

 Diagnostic analytics is what analyzes past performance to determine what happened and why?

 4.descriptive analytics

 This technique takes a lot of time and gives the least benefit.

 Descriptive analytics provides historically insight into data such as: - summary statistics, clustering and association rules etc.