Big Data Analytics Explained | What Is Big Data Analytics? | Big Data Tutorial | Simplilearn

Summary

This video by Simply Learn introduces big data analytics, a crucial technique for analyzing vast datasets across various industries. It covers the importance, types, life cycle, tools, and advantages of big data analytics, showcasing its role in decision-making, product development, and risk management. Examples from Spotify, Amazon, and Google illustrate its practical applications in enhancing customer experience and operational efficiency.

Takeaways

• 📈 Big data analytics is essential for analyzing vast amounts of data from various sectors, including retail, education, banking, and clothing brands.
• 🎵 Spotify uses big data analytics to generate song recommendations based on user behavior, showcasing the power of data-driven decision making.
• 📊 The importance of big data analytics lies in its ability to provide cost-efficient storage, swift analysis, and insights that inform product development and customer behavior understanding.
• 🔍 Types of big data analytics include descriptive, predictive, diagnostic, and prescriptive analytics, each serving different analytical needs.
• 🔄 The life cycle of big data analytics involves business case evaluation, data identification, filtration, extraction, aggregation, analysis, and visualization, culminating in informed decision making.
• 🛠️ Tools like MongoDB, Hadoop, Tableau, and Cassandra are pivotal in big data analytics, offering capabilities from data storage to visualization and processing.
• 💡 Big data analytics offers advantages such as innovation, risk management, improved decision making, customer engagement, and operational efficiency.
• 🛒 E-commerce giants like Amazon use big data to manage vast datasets, personalize user experiences, and enhance services.
• 🎥 Netflix leverages big data to understand customer preferences, enabling the creation of tailored content that meets viewer expectations.
• 💳 American Express uses big data to detect fraudulent transactions, demonstrating the role of analytics in security and risk mitigation.
• 🔍 Google utilizes big data to interpret user intent and provide relevant search results, highlighting the impact of analytics on user experience.

Q & A

What is big data analytics?

Big data analytics is the technique to analyze valuable data sets of any format, such as structured, unstructured, or semi-structured, to extract insights and support decision-making processes in various industries.

How does Spotify utilize big data analytics?

Spotify uses big data analytics to generate suggestive songs through smart recommendation techniques based on user likes, shares, search history, and other data, creating playlists automatically and enabling other automated processes.

What is the importance of studying big data analytics?

Studying big data analytics is important because it helps organizations to better understand data, make cost-efficient and swift decisions, develop new products based on customer behavior, and improve overall operational efficiency.

What are the types of big data analytics mentioned in the script?

The types of big data analytics mentioned are descriptive analysis, predictive analysis, diagnostic analysis, and prescriptive analytics.

Can you describe the life cycle of big data analytics?

The life cycle of big data analytics includes business case evaluation, identification of data, data filtration, extraction, data aggregation, data analysis, visualization, and decision-making.

What tools are commonly used in big data analytics?

Some common tools used in big data analytics are MongoDB, Apache Hadoop, Tableau, and Apache Cassandra.

How does MongoDB support big data analytics?

MongoDB is an open-source, NoSQL document-oriented database that supports data storage and manipulation, offering features like CRUD operations, aggregation framework, text search, and map-reduce capabilities.

What is the role of Apache Hadoop in big data analytics?

Apache Hadoop is an open-source software utility used for storing and processing large datasets. It uses the MapReduce programming model for parallel computation on data to analyze massive datasets efficiently.

What advantages does big data analytics offer to businesses?

Big data analytics offers several advantages, including innovation leading to product development, risk management, improved decision-making, enhanced customer engagement, and operational efficiency through improved data quality.

Can you provide some real-life use cases of big data analytics mentioned in the script?

Some real-life use cases mentioned are Amazon using big data for managing vast amounts of data, Netflix leveraging it to understand customer preferences, American Express using it for detecting fraudulent transactions, and Google using it to understand user search patterns and preferences.

Outlines

00:00

📊 Introduction to Big Data Analytics

The video script introduces the concept of big data analytics, emphasizing its widespread importance across various sectors like retail, education, banking, and clothing. It outlines the video's agenda, which includes understanding big data analytics, its importance, types, life cycle, tools, advantages, and real-world use cases. The script also highlights how companies like Spotify use big data to create personalized user experiences through smart recommendation techniques based on user data.

05:01

🔍 Types and Life Cycle of Big Data Analytics

This paragraph delves into the different types of big data analytics, including descriptive, predictive, diagnostic, and prescriptive analytics. It explains each type's purpose and application. The life cycle of data analytics is also discussed, starting from business case evaluation to data identification, filtration, extraction, aggregation, analysis, visualization, and decision-making, providing a comprehensive view of the process involved in harnessing big data.

10:03

🛠️ Tools and Advantages of Big Data Analytics

The script introduces key tools used in big data analytics, such as MongoDB, Apache Hadoop, Tableau, and Apache Cassandra, detailing their functionalities and applications. It then outlines the advantages of big data analytics, focusing on innovation, risk management, improved decision-making, customer engagement, and data quality enhancement. The paragraph emphasizes how these advantages translate into real-world benefits for businesses.

15:05

🚀 Real-World Use Cases of Big Data Analytics

The final paragraph presents real-world examples of how big data analytics is utilized by companies like Amazon, Netflix, and American Express. It discusses how Amazon manages vast amounts of data to enhance services, Netflix personalizes content for its customers, and American Express detects fraudulent transactions. Google's use of big data to understand user preferences and deliver targeted search results is also highlighted, showcasing the practical impact of big data analytics on business strategies and operations.

Keywords

💡Big Data Analytics

Big Data Analytics refers to the process of examining, cleaning, transforming, and modeling large data sets with advanced analytics to extract valuable insights. It is central to the video's theme, illustrating how analyzing vast amounts of data from various sources can help in decision-making and strategic planning. For instance, the script mentions Spotify using big data analytics to generate song recommendations based on user behavior.

💡Data Processing

Data Processing is the collection of procedures involved in transforming raw data into useful information. It is integral to big data analytics, as the script explains how companies process data to uncover patterns and trends. An example from the script is the use of data filtering tools to collect and filter data for Spotify's recommendation engine.

💡Structured Data

Structured data is information that is organized in a specific format, making it easily searchable and analyzable. The script mentions structured data as one of the data types that big data analytics can handle, emphasizing the importance of organizing data for effective analysis.

💡Unstructured Data

Unstructured data is data that does not have a pre-defined format or organization, such as text documents, emails, and multimedia files. The video script discusses how big data analytics can process unstructured data, which is often the majority of data collected by companies, to extract meaningful insights.

💡Semi-Structured Data

Semi-structured data is data that has some organization but does not adhere strictly to a schema like structured data. The script touches on semi-structured data as another format that big data analytics can analyze, highlighting the flexibility of these techniques to handle various data types.

💡Descriptive Analysis

Descriptive analysis is a type of data analysis that summarizes and describes data to understand the basic features and characteristics. The script explains that descriptive analysis in big data analytics involves processing past and present datasets to identify trends and patterns.

💡Predictive Analysis

Predictive analysis is a forward-looking approach that uses historical data to make predictions about future outcomes. The video script describes how predictive analysis in big data analytics aids in decision-making by forecasting future trends based on past data.

💡Diagnostic Analysis

Diagnostic analysis delves deeper into data to understand the reasons behind observed outcomes. The script mentions this type of analysis as a way to introspect why certain events have occurred, allowing for more informed decision-making based on underlying causes.

💡Prescriptive Analytics

Prescriptive analytics goes beyond predicting outcomes to recommending the best course of action. The video script explains that prescriptive analytics uses data to determine optimal actions, which is crucial for strategic planning and execution.

💡Data Filtration

Data filtration is the process of removing irrelevant or corrupt data from a dataset to improve data quality. The script discusses the importance of data filtration in the life cycle of big data analytics, where it is necessary to ensure that the data used for analysis is clean and relevant.

💡Data Aggregation

Data aggregation involves combining data from multiple sources or datasets into a single dataset for analysis. The script uses the example of combining student academic records with personal details via a common field, such as a roll number, to illustrate how data aggregation can provide a comprehensive view for analysis.

💡Data Visualization

Data visualization is the graphical representation of data to analyze and communicate patterns and trends more effectively. The video script highlights the role of data visualization in influencing the interpretation of results and allowing users to discover answers to unformulated questions.

💡Hadoop

Hadoop is an open-source software framework used for distributed storage and processing of big data sets. The script mentions Hadoop as a key tool in big data analytics, using the MapReduce programming model to perform parallel computations on large datasets.

💡Tableau

Tableau is a data visualization tool that helps in organizing and analyzing business information. The script describes how Tableau is used by organizations to visualize data and uncover patterns for business intelligence purposes.

💡Cassandra

Apache Cassandra is a highly scalable, distributed NoSQL database designed to handle large amounts of data across many servers. The script mentions Cassandra as a tool used in big data analytics for its high availability and ability to manage large datasets without a single point of failure.

💡Risk Management

Risk management in the context of big data analytics involves using insights about customer behavior and market trends to mitigate financial and operational risks. The script explains how big data analytics can help in risk management by detecting potential issues and informing strategic decisions.

💡Customer Engagement

Customer engagement refers to how customers interact with and perceive a brand. The video script discusses the importance of big data analytics in understanding customer engagement, which is crucial for improving products and increasing revenue per customer.

Highlights

Big data analytics is essential for analyzing valuable data sets in various formats for decision-making across different industries.

Spotify uses big data analytics to generate song recommendations based on user behavior and history.

72% of e-commerce companies rely on big data analytics for business insights and decision-making.

Big data analytics can be cost-efficient, allowing for the storage and analysis of large amounts of data at minimal cost.

Descriptive, predictive, diagnostic, and prescriptive analytics are the four types of big data analytics.

The life cycle of data analytics includes business case evaluation, data identification, filtration, extraction, aggregation, analysis, and visualization.

Tools like MongoDB, Hadoop, Tablo, and Cassandra are used for big data analytics to support data storage, processing, and visualization.

Big data analytics enables innovation, risk management, improved decision-making, and enhanced customer engagement.

Amazon uses big data to manage vast amounts of data and improve customer experience.

Netflix leverages big data to understand customer preferences and personalize content.

American Express uses big data to detect fraudulent transactions and improve security.

Google utilizes big data to understand user intent and provide relevant search results.

Big data analytics helps in developing new products, targeting new markets, and increasing revenue streams.

Improving data quality through big data analytics enhances operational efficiency and drives data-driven decisions.

Visualization plays a crucial role in interpreting results and discovering answers to unformulated questions.

The use of big data analytics in real-world scenarios showcases its practical applications in various business domains.

Transcripts

everyone knows there is an enormous

amount of data generated every second it

has become crucial to analyze those data

as they can be very useful everyone is

aware of the importance of big data

analytics from

big retail markets to education from the

banking sector to clothing brands it is

everywhere how do you handle them how do

you process them at all the answer to

all these questions is big data

analytics hi this is sahana from simply

learn today we will learn interesting

terms about big data analytics which has

become a buzzword everywhere error

before we start please make sure you

subscribe to our channel and press the

bell icon to never miss an update

so let's go through the topics to be

covered in today's video

we start by getting introduced to what

is big data analytics

next we will learn about importance of

big data analytics

types of big data analytics

followed by life cycle of big data

analytics

tools

and advantages of big data analytics and

finally will go through use cases of big

data analytics

first we must understand what do you

mean by big data analytics

big data analytics is the technique to

analyze valuable data sets having any

format like structured unstructured or

semi-structured for example music

industries like spotify the company has

nearly 96 million users that generate

tremendous amount of data every day

using this information the application

generates suggestive songs through smart

recommendation techniques based on likes

shares search history and many more

playlists are created automatically and

many more automated processes can happen

what enables this is the techniques

tools and frameworks that are a result

of big data analytics if you are a

spotify user then you must have come

across the top recommendation sections

which is based on your life past history

and other things utilizing a

recommendation engine that leverages

data filtering tools that collect data

and then filter it using algorithm

this is how spotify works

by collecting and analyzing proper data

and incorporating it into business plans

which will further help them in decision

making this is how big data analytics

helps big

business organizations

72 percent of e-commerce companies rely

on data produced from big data analytics

and this data is increasing day by day

any youtube channel can use data

analytics to analyze user interest and

develop his next project based on that

what is the importance of the topic why

do we need to study big data analytics

is the biggest question it is observed

that every organization adopts this

technique for a better understanding of

data and its use

cloud based analytics can store a large

amount of data with minimal cost for

example tools like zoho analytics

microsoft power bi and many more thus

big data analytics can result as cost

efficient

the way of analyzing it is very swift

which will help to analyze all the

sources this will contribute to quick

decision making in any organization

the result of such analysis will help to

develop new products as due to these

products analysis of the companies will

know about their customer likings and

behavior due to all these efficient

strategies big data analytics is very

crucial

let's move forward and understand what

are the types in big data analytics

types of big data analytics

first let's try and understand

descriptive analysis it includes

processing past and present data set

which will lead to help in trend

analysis

in predictive analysis it makes future

predictions based on past or historical

datasets which will help in decision

making

the diagnostic analysis is an advanced

analysis system in which introspections

are made based on why that has happened

and further decisions are taken based on

the available datasets

prescriptive analytics is the process of

using data to determine an optimal

course of action

the next topic we are covering is

life cycle of data analytics

first is business case evaluation will

help decision makers to understand the

source of business in this case the

learner or the team will learn about the

business domain which presents the

motivation and goals for carrying out

the analysis in this point the problems

get identified and assumptions are made

based on the available datasets

in the identification of data once the

business case is identified now it's

time to find the appropriate datasets to

work with in this stage analysis is done

to see what other companies have done

for its similar datasets

in data filtration once the source of

data is identified now it's time to

gather data from such resources this

kind of data is mostly unstructured then

it is subjected to filtration such as

removal of corrupt data or irrelevant

data

which is of no scope further now the

data is filtered but there might be a

possibility that some of the entries of

such data is incompatible to rectify

this issue a separate phase is created

called as extraction phase in this phase

the data which don't match the

underlining scope of the analysis are

extracted and transformed

in data aggregations data sets are

validated and combined with common field

for instance we can

take data set of a student

one data set can be named as student

academic section and the other can be

named as student personal details then

both can be joined together via common

field we can take it as role number

depending on the nature of big data

problem analysis is carried out data

analysis can be classified as

confirmatory analysis and exploratory

analysis in conformity analysis the

cause of the phenomenon is analyzed

before the assumption is called

hypothesis the data is analyzed to

approve or disapprove the hypothesis

such kind of analysis will provide

definitive answer to some specific

questions and confirms whether an

assumption was true or not in

exploratory analysis the data is

explored to obtain information that why

a phenomenon has occurred this type of

analysis answer why a phenomenon

occurred this kind of analysis doesn't

provide definitive information meanwhile

it can provide discovery of certain

patterns

visualization or data visualization is

said to influence the interpretation of

the results moreover it will allow the

user to discover answer to certain

questions that are yet to be formulated

the analysis is done the results are

visualized now it's time for the

business user to

make decisions

using the result the result can be used

for optimization to define business

processes it can be used as an input for

the system to enhance performance

this is all about life cycle of data

analytics

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there are multiple tools available on

the market which can help companies in

big data analytics let us go through

some of these tools

mongodb

hadoop

tablo

cassandra these are the main tools which

are used in big data analytics

let's go for mongodb

mongodb is an open source tool that

support data storage it is a nosql

document oriented database mongodb is

used by facebook and google for data

storage mongodb is best suited for big

data by resulting data and further

manipulation for the desired output some

of the powerful resources are crud

operations aggregation framework text

research and the map reduced features

apache hadoop is the most famous tool it

is an open source software utility to

store and process gigabytes and

terabytes of dataset it uses the

mapreduce programming module to solve

problems of analyzing massive data

mapreduce is a framework that helps

programs do the parallel computation on

data the mark task takes

input data and converts it into a data

set that can be computed in key value

pace the output of the map task is

consumed by reducing task to aggregate

output and provide the desired result

tableau is a software company that

offers collaborative data visualizations

software for organizing working with

business information analytics

organizations use tableau to visualize

data and reveal pattern for analysis in

business intelligence

apache cassandra is highly scalable high

performed distributed database designed

to handle large amount of data across

many commodities like servers providing

high availability with no single point

of failure it is a type of no sql

database

now we have covered the important tools

used by big data analytics let us go and

cover distinct advantage of big data

technology

advantages of big data analytics

innovation of new ideas leading to

product development which means

developing new products provides a means

to target new markets increase market

share sell more and increase revenue

streams meanwhile redesigning existing

products enables cost to be cut margins

to be decreased and ultimately more

profits to be made

risk management insights about customers

likings and behavior and market trends

will help decision maker to take their

position on top of that it will help to

get rid of financial risk it will also

assist to detect potential cyber risk

one benefit from big data and business

analytics can help improve decision

making by identifying patterns

identifying problems and providing data

to back up the solution is beneficial

whether the solution is solving the

problem improving the situation or it

has an insignificant effect

here customer engagement specifically

how your customers view and interact

with your brand is a key factor

big data analytics provides the business

intelligence you need to bring about

positive changes like improving existing

products or increasing revenue per

customer

next improving data quality will improve

operational efficiency and valued

feedback from customer which will help

businesses to handle vast amounts of

data it will also help in enabling data

driven decision

but how do these advantages related to

the real world let us cover some real

life use cases empowered by big data

analytics

use cases of big data and analytics

amazon is a well-known name to all of us

it is among the leading e-commerce

platforms apart from offering online

shopping amazon serves us with different

services like amazon pay amazon web

services and many more for a company

like amazon the amount of data collected

on a regular basis is very big to manage

such vast amount of data companies

leverage big data technology

for any company like netflix one of the

most valuable asset is the customer base

because it is the customer who turns the

company into a brand and if a company

fails to meet the expectations of the

customer that probably leads to its

decline big data is a technology that

helps in the management of large amount

of data

big data is like a heart for american

express decision making

their main goal is to detect fraudulent

transaction as soon as possible for

reducing laws and in this big data plays

a very important role they use big data

for anticipating and analyzing customers

behavior by looking at recorded

transactions and incorporating more than

100 variables the company assigns modern

predictive models instead of customary

businesses

next is

very important that is google

google uses big data to understand what

we want from it based on several

parameters such as search history

locations trends and many

more after that it goes through an

algorithm where complex estimations are

done and afterwards google easily shows

the arranged or positioned index list

as far as significance and authority

intended to coordinate the users

this is all about use cases of big data

analytics

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