"Unlocking Insights: A Comprehensive Guide to Big Data Analytics for Transformative Decision-Making, Enhanced Business Performance, and Predictive Modeling in the Digital Age of Information Overload and Technological Advancement"

Big Data Analytics involves the examination and interpretation of vast datasets to uncover patterns, trends, and insights that drive informed decision-making. By utilizing advanced tools and techniques, organizations can transform raw data into actionable intelligence, enhancing operational efficiency, customer experiences, and strategic planning. In today’s data-driven world, mastering big data analytics empowers businesses to stay competitive, anticipate market shifts, and innovate effectively, making it an essential component of modern strategies across industries.
Big Data Analytics encompasses the processes and technologies used to analyze and interpret large, complex datasets that traditional data processing software cannot handle. It involves techniques like data mining, machine learning, predictive analytics, and natural language processing to extract meaningful insights from structured and unstructured data.

By leveraging big data analytics, organizations can:

1. **Enhance Decision-Making**: Data-driven insights lead to better, faster decisions, minimizing guesswork and enhancing strategic planning.

2. **Improve Customer Experiences**: Analyzing customer behavior and preferences helps tailor services and products, fostering loyalty and satisfaction.

3. **Optimize Operations**: Identifying inefficiencies and trends within operations allows for streamlined processes and cost reductions.

4. **Drive Innovation**: Insights from big data can inspire new products, services, and business models, keeping companies ahead of the competition.

5. **Predict Trends**: Predictive analytics enables businesses to foresee market changes, customer demands, and potential risks, allowing for proactive strategies.

As businesses continue to generate and collect vast amounts of data, the ability to harness and analyze this information becomes increasingly crucial. Big Data Analytics not only empowers organizations to make informed decisions but also transforms how they interact with their customers and adapt to a rapidly changing marketplace. Embracing these techniques positions companies to thrive in an era defined by information and innovation.

1. BIG DATA
ANALYTICS

2. TABLE OF
CONTENTS
Type of big data analytics03
What is big data?01
What is big data analytics02
Characteristics of big data analytics?04
Process of big data analytics?05
06
07
08
Big data application domains
Benets of big data analytics
Some Realtime application of big data analytics

3. What is Big Data?What is Big Data?
Big data is a term for
handling large and
complex datasets that
traditional tools can’t
manage. It involves
processing massive
amounts of diverse data
quickly, using advanced
tools to extract valuable
insights.
Big data is a term for
handling large and
complex datasets that
traditional tools can’t
manage. It involves
processing massive
amounts of diverse data
quickly, using advanced
tools to extract valuable
insights.
3

4. Type of Big Data 
Unstructured Data
Structured Data
Semi-Structured Data
4

5. Characteristics of Big Data
Volume
ValueVeracity
Variety VelocityThe 5 V’s
of Big
Data
5

6. Analyzing huge amounts of data
from social media (like Facebook
or Twitter) to understand user
behavior and trends is an example
of big data.
Example of Big Data 
6

7. What is Big Data
Analytics?
Big data analytics is the process of
using advanced tools to analyze large
and complex datasets. The goal is to
find patterns and insights that can
help make better business decisions.
7

8. Big data analytics is
used for risk
management
Big data analytics is used
for product development
and innovations
Big data analytics is
used to improve
customer experience
Big data analytics
helps in quicker and
better decision making
in organizations
Big data
analytics
8

9. What is Big data Analytics ?
Banco de Ore, a
Philippine banking
company uses big data
analytics
Identifying fraudulent activities and
discrepancies is easier using big data analytics.
Thus the organization was able to narrow down
the list of suspects using big data analytics
9

10. What is big data Analytics ?
Rolls-Royce manufactures
massive jet engines.These
enginers are used by airlines
and armed forces across the
world
The company uses big data analytics to
analyze how good the engine design is
and if there has to be any more
improvement
Big data analytics is used here in
designing a product of higher quality
10

12. What is big data Analytics ?
Starbucks uses big data analytics
for important decisions. For
example, big data analytics is used
to decide if a particular location
would be suitable for a new outlet
or not
The analysis is done based on factors such as
population demographics, accessibility of the
location, competition in the vicinity, economic
factors, parking adequacy and so on
The business grows if the right location
is chosen wisely by considering the
above parameters
11

13. What is big data Analytics ?
Delta airlines uses analysis
to improve customer
experience
They monitor tweets to nd out their
customers’ experience regarding the
journey, delays and so on
Airline identies negative tweets and does
the needful by upgrading the customer’s
ticket for the next journey if it is found out
to be the airline’s fault. This helps the
airline build good customer relations
12

14. Lifecycle of Big Data
Analytics

15. Identication
of data
Data
extraction
Data
?O??9Z-
Business case
evaluation Stage 1
Stage 2
Stage 3
Stage 4
14

16. Data
analysis
Final analysis
result
Visualization
of data
Data
aggregation Stage 5
Stage 6
Stage 7
Stage 8
15

17. Lifecycle of big data
analytics
The Big Data analytics lifecycle
begins with a business case,
which defines the reason and
goal behind the analysis.
Here, a broad variety of data
sources are identified.
All of the identified data from
the previous stage is filtered
here to remove corrupt data.
Identication
of data
Data ltering
Business case
evaluation
Data extraction
Data that is not compatible
with the tool is extracted
and then transformed into a
compatible form.
16

18. Lifecycle of big data
analytics
In this stage, data with the
same fields across different
datasets are integrated.
With tools like Tableau, Power
BI, and QlikView, Big Data
analysts can produce graphic
visualizations of the analysis.
Data is evaluated using
analytical and statistical tools
to discover useful
information.
Visualization
of data
Data analysis
Data
aggregation
Final analysis
result
This is the last step of the Big
Data analytics lifecycle, where
the final results of the analysis
are made available to business
stakeholders who will take
action. 17

19. Types of Big
Data Analytics

20. Diagnostic
analytics
Descriptive
analytics
Predictive
analytics
Prescriptive
analytics
Type of Big Data Analytics

21. Type of Big Data Analytics
Descriptive analytics
It summarizes past data into a
form that is interpretable by
humans.
This analytics helps in creating
reports like company’s revenue,
pro?t( sales and so on'
Tabulation of social media metrics
like Facebook likes and tweets are
done using descriptive analytics
20

22. Type of Big Data Analytics
Diagnostic analytics
An ecommerce company’s report
shows that their sales have reduced
although customers are adding
products to the cart
A lot of things could have
gone wrong:
The form didn’t load
correctly.
The shipping fee was too
high.
Not enough payment
options available.
Using diagnostic analytics, we
can nd out the reason why this
happened
21

23. Type of Big Data Analytics
Predictive analytics
Paypal determines what kind of
precautions they have to take to
protect their clients against fraudulent
transactions
Using predictive analytics, the company
uses all the historical payment data, the
user behavior data and builds an
algorithm which predicts fraudulent
activities
Looks into the historical
and present data to make
predictions of the future
22

24. Type of Big Data Analytics
prescriptive analytics
Prescriptive analytics can be used
to maximize an airline’s prot
This analytics is used to build an
algorithm that will automatically adjust
the ight fares based on numerous
factors, including customer demand,
weather, destination, holiday seasons
and oil prices
This type of analytics
prescribes the solution to a
particular problem
23

25. Tools used in Big Data
Analytics
MongoDB
Handoop
Cassandra
Storm
Talend Spark
24

26. 25

27. 26
1. Storage unit

28. 27
2. Map Reduce
MapReduce is a programming model used for efficient
processing in parallel over large data- sets in a distributed
manner. The data is first split and then combined to produce the
final result.

29. 28
3.Yarn

30. Application Domain
Marketing : Big Data analytics helps to drive high ROI marketing
campaigns, which result in improved sales
Education : Used to develop new and improve existing courses
based on market requirements
Healthcare :  With the help of a patient’s medical history, Big Data
analytics is used to predict how likely they are to have health issues
Media and entertainment : Used to understand the demand of
shows, movies, songs, and more to deliver a personalized
recommendation list to its users
29

31. Application Domain
Banking : Customer income and spending patterns help to predict
the likelihood of choosing various banking oers, like loans and credit
cards
Telecommunications : Used to forecast network capacity and
improve customer experience
Government : Big Data analytics helps governments in law
enforcement, among other things
30

32. Advantages & Disadvantage
of Big Data Analytics
Smart Decisions
Better Work
Happy Customers
Seeing the future
Safe and Security
Advantages
Privacy worries
Costly and Complex
Ethical Questions
Technical Challenges
Possibility of Bias
Disadvantages

33. Future of Big Data Analytics
 Smarter Computers
 Quick Decision-Making 
 Computing Everywhere 
Real-Time Information  
 Better Predictions
 Stronger Security
32

34. THANK
YOU!