ppt is about how big data helping current giants in telecom and how useful it for market. How jio used big data for there call issue and how telecom can benefited by it.

1. BIG DATA IN
TELECOM
INDUSTRY

2. Context
1. Introduction
2. Telecom Industry-overview
3. Big data Application in Telecom
4. Jio case study
5. References
6. Conclusion

3. • big data is larger, more complex data
sets, especially from new data sources.
These data sets are so voluminous that
traditional data processing software just
can’t manage them. But these massive
volumes of data can be used to address
business problems you wouldn’t have
been able to tackle before.
• IDC even defines big data projects as
projects that contain a minimum of 100
terabyte of collected data
What Is Big
Data ?

4. TELECOM INDUSTRY
• India’s Telecom market changed after internet price cut and
now
5G Technology is Also in Pipeline.
• India had nearly 700 million internet users across the
country and tele-density reached 1,177.97 million in 2019 4th
Quarter
• Gross revenue of the telecom sector stood at Rs 1,85,291
crore (US$ 26.51 billion) in FY20 (April-December 2019).
• The revenue generated from Telecom Industry is 6.5%
Of National GDP and It gives More then 4 million
People employment.

5. WHY TELECOM
NEEDS BIG DATA?
• With the constantly-evolving way consumers interact and
react to content, media, entertainment, and telco companies
have faced increased pressure to change their business
models
• A new IBM study on how telecoms are using Big Data shows
that 85% of the respondents indicate that the use of
information and analytics is creating a competitive advantage
for them.
• Big Data can increase Average revenue per user (ARPU),
reduce customer churn, drive revenues and enhance market
shares
• The number of internet subscribers in the country is
expected to double by 2021 to 829 million and overall IP traffic
is expected to grow four-fold at a CAGR of 30% by 2021

6. APPLICATIONS OF BIG
DATA IN TELECOM
• 1) Customer Experience
• 2) Network Optimization
• 3) Operations Analysis
• 4) Data Monetization

7. 1) Customer Experience
1) Targeted Marketing
• Based on users behaviour The offers are going to show him up
ex Vodaphone VI app suggest data packs according to users data usage
• location based marketing can also useful by giving offers on the local festivals.
• Giving offers of streaming site packs with data packs
2) Predictive churn Analysis
• The right of use ML in big data starts here it is term to predict the timeline when user is going
to leave your
services entirely .once company get to know can make solution for lowering churn rate
• Ex if user is going to leave and you cant lower your rate the introduce new service like
Vodaphone is doing.
the AI model working largely on it use social medias sentiment analysis As its subpart

8. 2) Network Optimization
1)Capacity Planning:-
• capacity planning is all about how the bandwidth will be shared in area and make sure
that user
can get the same experience. Big data help to find out new data trend in areas which
make
easier for provider get decision
• it is used for forecast the network growth and identify the areas of investments, it
primarily
covers investment in areas going to exhaust capacity in near future and capacity
requirements for
rolling out new services and technologies like VoLTE, VoLTE, IoT, OTT, LTE-Advanced
Pro, 5G, etc.

9. 2) Network Optimization
2) Investment Planning:-
--Telecom companies need to plan their investments and resources based on
several
parameters such as future connectivity needs, strategic objectives, projected
ROI, forecasted
traffic, customer experience, etc. The effective combination of network traffic
data, customer
experience metrics, revenue potential, and location data along with customer
value data
ensures that the investment is most effectively utilized.

10. 3) Operational
Analytics
1)Revenue Leakage:-
• Revenue leakage is unnoticed loss from the company. In telecom if number of user
increasing but no profit is showing according to it. Big data AI can help fraud detection
because its capability of dealing with structured and unstructured data. Use of AI can
cut the cost.
2)Network Security:
• Major concern is security newer days. The big data can used for make sure Data
related to these risks can be analyzed in real-time to mitigate risk, detect incidents,
and respond to breaches.

11. 4) Data
Monetization
1) Data Analysis:-
• The telecom sector has started providing data analytics as a service to other
key verticals. There is a wide variety of applications and use cases for such
analytics.
2) IoT/ M2M Analytics
• Telecom companies have started providing complete M2M solutions. With
ever-increasing IoT devices in the network, network analytics around IoT
sensors traffic in the next area of exploration. They now can add location-
based and geospatial elements to the streaming data. ultimately provide
valuable insights to enterprise verticals.

12. Market Players

13. Use Case Analysis Of Reliance JIO
 Reliance Jio is one of the world’s largest and fastest growing data service
operators with nearly 400 million subscribers.
 The India’s biggest service service provider, which has disrupted the market
with its affordable data plans and unlimited calling benefits, has created a
completely digital experience for its users – ranging from data services on
smartphones, to gigabit Internet at home, along with a portfolio of media
offerings and IoT devices such as smart speakers and switches for the
smart home.
 In order to achieve these objectives, Jio set up a big data and analytics
strategy and partnered with Guavus to execute it.
 They deployed our AI-enabled analytics solutions to measure real-time
customer experience and predictive analytics to automate troubleshooting
of the network and generate subscriber insights for use in marketing.

14. • “Our networks generate 4 to 5 petabytes
of data each day. If this data can be
properly analyzed in real-time using big
data analytics and predictive analytics
techniques, we can both improve the
health of our network through intelligent
automation and offer multiple, customized
personal services to our customers.
Guavus’ solutions enable us to do this – we
can make data-driven decisions that allow
us to deliver a great experience to our
customers while bringing intelligent
automation to our operations,”
• Anish Shah, President of IT, Reliance Jio
Use Case Analysis Of Reliance JIO

15. Use Case Analysis Of Reliance JIo
How Jio had Improved after Big data?
• Address the call muting problems on VoLTE, actively identifying call muting issues
and potential call drop behavior in real-time, enabling resolution of network issues
at up to 5 times faster. This new capability has also enabled Jio to reduce the
likelihood of poor quality of experience (QoE) for VoLTE subscribers by 50%.
• Attribute a QoE score to an individual subscriber, allowing them to identify factors
such as mobile subscriber devices that have a higher propensity to mute calls.
Such insights helped Jio’s customer care organization to accurately identify
problematic devices as the root cause of the call muting issues 100% of the time.
• Identify the exact population of subscribers experiencing call muting issues, which
improved the mean time to repair (MTTR) by 50%.

16. How Jio had Improved after Big data?
• Thanks to the Guavus Reflex analytics platform, they can identify missing data
feeds that were critical to their analytics needs. This discovery capability helped
Jio improve the completeness and accuracy of data analysis and their data
ingestion architecture for their big data lake.
• Utilizing the Guavus solution, Jio’s data engineering and data science teams are
now able to create new analytics use cases and accelerate the delivery of
analytics-powered applications to the business.
Use Case Analysis Of Reliance JIo

17. Use Case Analysis Of Reliance JIo
Net sales of Reliance Jio Infocomm Limited from
financial year 2018 to 2020(in billion Indian rupees)

18. References
https://www.tatateleservices.com/downloads/WhitePapers/resources/Big-Data-
and-the-Telecom-Industry.pdf
https://www.guavus.com/resources/case-studies/reliance-jio-guavus-success-
story/
https://www.statista.com/

19. Conclusion
 Smartphones have become a basic necessity these days. People can connect
with each other located anywhere in the world, eradicating the distance barrier.
We can collect and process information faster than ever and there’s no
stopping it.
• Big Data Analytics will facilitate telecom industries to thrive in this ever-
advancing digital world. Without Big Data, telecom companies will be lost in a
freeway and there’s no coming back
• In This Era more number of social media user expose most of human behavior
and reaction for any service and product. Companies can use it for betterment
of structure.
• Data is increasing exponentially . the more number of data requires more
space and proper management of it can be archived by big data methodologies