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TOPIC- INFORMATION TECHNOLOGY

NAME- MOHD ADIL

COURSE- MBA

UNIVERSITY- GOLDEN GATE UNIVERSITY

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INTRODUCTION 03
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CURRENT SITUATION 05

PROBLEM STATEMENT 05

Overview of problem statement 06

The problem statement and key insights 06

Requirements for Identifying a Solution 07

Analysis of Possible Solutions Using Information Technology 10

Why Edge Computing is the Best Solution 11

Recommendations Based on the Analysis 12

PROJECT CONCLUSION 14

EXECUTIVE SUMMARY 15

REFERENCES 17

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INTRODUCTION

Imagine joining a progressive phone firm and taking on the position of IT solution consultant.

This company, which is already involved in 5G mobile

services, cloud computing services, and internet solutions for corporations and individual custom

ers, is now focusing on edge

computing, which is expected to be the next big thing.

In essence, edge computing moves the processing capability of data closer to the point of data

generation.

Through onsite data collection, processing, and analysis, edge computing significantly lowers lat

ency and

eliminates need on cloud services. It optimizes storage requirements, reduces bandwidth use,

and guarantees real-time analysis.

This new method fits in wonderfully with the rapidly expanding Internet of Things (IoT), where

commonplace items, like cars and kitchen appliances, may link to each other and automatically

gather and share data without the need for human interaction.

There are now bandwidth and communication latency problems as a result of the explosion of da

ta produced by Internet of Things sensors and devices surpassing the capacity of traditional cloud

processing in recent years. But edge computing provides an answer, which is

particularly important for realtime data processing in applications such as autonomous vehicles

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and drones.

It is your responsibility to spearhead the implementation of edge computing technologies.

You will spearhead the integration of edge computing with the company's current 5G towers,

internet access, and connectivity services by coordinating organizational goals, suggesting deplo

yment plans, and broadening the responsibilities of stakeholders.

In addition to improving connections between individuals and companies, this change will open

the door for more intelligent, real-time decision-making across a network of networked objects.

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CURRENT SITUATION

It is your responsibility as the IT solution expert to lead the business through this change.

Utilizing the advantages of edge computing, which entails gathering, processing, and analyzing

massive amounts of data near to the source in order to improve speed and efficiency while

lowering latency and dependency on cloud services, is the aim.

PROBLEM STATEMENT

The amount of data produced by sensors and devices has skyrocketed with the Internet of Things

Since this data is now processed on the cloud, there are significant network capacity and

communication latency bottlenecks.

The trick is to use edge computing to reduce these problems by moving data processing closer to

its source.

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Overview of problem statement

In order to deal with this, the plan entails:

deploying edge computing units to process data locally at key sites, such cellphone towers.

leveraging the internet and 5G networks that the corporation already has in place to support the

edge computing framework.

involving IT security, project managers, network engineers, data scientists, customer service, and

other departments as stakeholders to guarantee smooth integration and security.

demonstrating use cases in the fields of manufacturing, smart cities, and healthcare to demonstrat

e the immediate advantages of edge computing.

The problem statement and key insights

The phone operator plans to incorporate edge computing into its current infrastructure in an effor

t to profit from the expanding Internet of Things (IoT) industry.

The difficulty is in resolving the present network bandwidth and data processing limitations brou

ght on by the reliance on cloud computing. Reducing latency and improving real-

time data analysis are the objectives of processing data closer to the source.

Key Takeaways: Edge computing increases efficiency by capturing, processing, and analyzing

data close to its source, which lowers latency and depends less on cloud services.

IoT Growth: Quicker and more effective data processing solutions are required due to the

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explosion of data coming from IoT devices.

Strategic Deployment: Supporting edge computing nodes by utilizing the company's current 5G

networks, internet services, and cell towers.

Stakeholder Involvement: To guarantee a comprehensive strategy to implementation, include net

work engineers, data scientists, project managers, IT security, and customer support. Real-

Time Benefits: Showcasing use cases including smart cities, healthcare, and manufacturing to

highlight the advantages of edge computing.

Requirements for Identifying a Solution

The following specifications should be taken into account when attempting to incorporate edge c

omputing into a phone company's current infrastructure in order to handle IoT data processing ch

allenges:

1. Ease of scaling

Scalability is required to manage the growing amount of data produced by IoT devices.

Supporting an increasing number of linked devices and data processing requirements over time is

part of this.

2. Minimal Latency

Minimal latency in communication and data processing must be guaranteed by the solution.

Applications like driverless cars and smart city infrastructure that need real-

time data processing and response need this.

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3. Safety

To safeguard data both in transit and at rest, strong security measures must be in place.

To stop data breaches and illegal access, this includes encryption, safe access controls, and frequ

ent security assessments.

4. Cooperation

It should be easy for the solution to function with the current infrastructure, which consists of clo

ud services, cellular towers, and 5G networks.

In order to provide interoperability across multiple manufacturers and technologies, it must also s

upport a variety of IoT devices and sensors.

5. Reliability

High uptime and dependability are necessary to guarantee data availability and ongoing functioni

ng.

Redundancy and failover techniques should be a part of the solution to reduce downtime and gua

rantee service continuity.

6. Economy of Cost

Cost-effective implementation should strike a balance between one-

time investment expenditures and ongoing operating savings.

This involves using resources as efficiently as possible and depending less on pricey cloud

services.

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7. Management

Ease It should be simple to implement, oversee, and maintain the solution.

This includes complete monitoring capabilities, automation tools, and user-

friendly management interfaces to guarantee seamless functioning.

8. Instantaneous Processing

One essential necessity is the ability to process and evaluate data in real-time.

This makes it possible to make decisions and take action right away using the information gather

ed by IoT devices.

9. Stakeholder Engagement

It is imperative to involve important stakeholders, such as IT security teams, network engineers,

data scientists, project managers, and customer support. Their expertise and collaboration are

crucial for a successful implementation. 10. Future-Proofing The solution should be flexible and

adaptable to future technological advancements and evolving business needs. This includes

supporting new IoT use cases and emerging technologies as they become relevant.

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Analysis of Possible Solutions Using Information Technology

1: Improvement of Cloud Computing

By adding more storage and processing power, cloud computing capabilities could be improved t

o meet the data processing requirements of Internet of Things devices.

This does not, however, solve the bandwidth and latency problems that come with centralized dat

a processing. It can also result in a greater need for data centers and more expenses.

Decentralized IoT Networks:

The Second Option

Some latency problems can be solved by implementing decentralized IoT networks, in which

devices speak with one another directly.

But maintaining these networks and guaranteeing data security and consistency can be difficult a

nd resource-intensive.

Option 3: Utilizing Edge Computing The most well-

rounded approach is to place edge computing nodes at key sites (such as cellphone towers)

to process data locally.

This method improves operating efficiency by lowering latency, easing bandwidth constraints,

and offering real-time data analysis.

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Why Edge Computing is the Best Solution

Decreased Latency

By processing data nearer the source, edge computing dramatically reduces latency.

Grand View Research reports that edge computing can cut latency by 30–

50%, which is important for real-time applications like driverless cars and smart cities.

Cost-Effectiveness

By lowering the dependency on cloud services, edge computing can minimize operational costs.

According to Gartner's projections, 75% of data generated by enterprises by 2025 will be created

and processed outside of traditional data centers, underscoring edge computing's financial

advantages.

Enhanced Security of Data

By processing and storing data locally, edge computing reduces the possibility of significant data

breaches.

According to an IDC analysis, endpoints were the source of 70% of successful breaches,

highlighting the significance of protecting data near to its source. Scalability

Because edge computing allows for localized data processing, it offers scalability.

Edge nodes can be added when IoT devices proliferate in order to effectively handle the

expanding data volumes.

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Data/Evidence Supporting Edge Computing Efficiency Gains:

According to McKinsey, businesses using edge computing can see up to 20% increases in operati

onal efficiency. Real-

Time Processing: Applications like predictive maintenance in manufacturing that need quick insi

ghts must be able to process data in real-time.

Savings on Bandwidth: Edge computing saves bandwidth by processing data locally, minimizing

the quantity of data sent to central servers.

This is especially crucial for Internet of Things applications that produce large amounts of data.

Recommendations Based on the Analysis

Strategically Place Edge Computing Nodes

In order to process data locally and lower latency and bandwidth consumption, install edge nodes

in strategic sites like data centers and cellular towers.

Make Use of the Infrastructure Already in Place

To guarantee smooth connectivity and data flow, integrate edge computing with the business's cu

rrent 5G networks, cellular towers, and internet services. Strengthen Security Protocols

To safeguard data processed and stored at the edge, implement strong security measures, such as

data encryption, secure access controls, and frequent security audits.

Include Important Parties To guarantee a thorough and well-

coordinated deployment, involve network engineers, data scientists, IT security teams, project

managers, and customer support in the process.. Make Training and Development Invested

Staff members should receive training on edge computing best practices and technologies to

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facilitate a seamless transition and optimize the advantages of the new infrastructure.

Keep an eye on and improve

Maintain constant monitoring of edge computing node performance and make required modificat

ions to maximize dependability and efficiency.

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Project Conclusion

Adding edge computing to the telecom company's current infrastructure offers a solid way to

deal with the problems brought on by the growing amount of data coming from Internet of

Things.

The organization can achieve considerable improvements in operational efficiency, latency

reduction, and real-

time data processing by placing edge nodes strategically, utilizing its 5G networks and internet se

rvices, and implementing strong security procedures.

This change promotes innovation and provides clients with new services in addition to reducing

network bottlenecks. Relevant Ideas Knowledgeable Edge Computing

gathers, prepares, and evaluates data close to its source.

lessens the need for cloud services and latency. increases the effectiveness of operations and real-

time data analysis. The Internet of Things a physical item network that gathers and transfers data.

includes commonplace items and sensors, such as air quality, motion, and temperature sensors.

produces enormous amounts of data that call for effective processing methods.

Privacy and Data Security

The significance of putting secure access controls in place and encrypting data.

Frequent security audits are necessary to protect data integrity and stop breaches. Scalability

Scalable solutions are required to manage growing volumes of IoT data.

extension of edge nodes in response to rising processing demands. Stakeholder Participation

network engineers, data scientists, project managers, IT security, and customer support working

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together. necessary for the effective installation and management of edge computing systems.

Efficiency of Operations real-time processing and analysis that boosts productivity and decision-

making. Reduced bandwidth usage and storage requirements.

Executive Summary

I suggest incorporating edge computing into the phone company's current infrastructure as an IT

solution consultant to improve operational effectiveness and manage the increase in data from

Internet of Things (IoT) devices.

There are currently difficulties in network latency and bandwidth due to cloud-

based data processing.

These problems can be solved by edge computing, which gathers, handles, and evaluates data

closer to the source. Important Findings and Suggestions:

Install Edge Nodes: Install edge computer nodes in key places, like data centers and cellular

towers. This will improve real-

time data analysis by lowering latency and dependency on cloud services.

Leverage 5G Infrastructure: To support the edge computing framework and ensure smooth data

flow and connectivity, make use of the company's current 5G networks and internet services.

Boost Security: To safeguard data while it is in transit and at rest, put strong security measures in

place, such as data encryption and secure access controls.

Involve network engineers, data scientists, project managers, customer support, and IT security

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teams as stakeholders to guarantee a thorough and well-coordinated deployment.

Showcase Use Cases: To illustrate benefits in real time and promote corporate alignment, highlig

ht real-world applications in industries like manufacturing, smart cities, and healthcare. Benefits:

Diminished Latency: Dramatically reduces latency in data processing, which is necessary for real

-time applications.

Cost-Effectiveness: Reduces dependence on cloud services, which lowers operating costs.

Enhanced Data Security: By processing data locally, the danger of data breaches is reduced.

Scalability: Offers a scalable way to deal with growing volumes of IoT data.

This strategy not only tackles the present issues but also establishes the business as a pioneer in

edge computing, spurring innovation and providing clients with fresh services.

Implementing edge computing has the potential to improve real-

time data processing capabilities, lower costs, and increase operational efficiency.

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REFERENCES-

1. Gartner. (2025). Enterprise data processing trends and predictions. Retrieved from htt

ps://www.gartner.com/en/research

2. Grand View Research. (2023). Edge computing market size, share, and trends analysi

s report by component (hardware, software), by application (smart cities, industrial

IoT), by region, and segment forecasts, 2023 – 2030. Retrieved from https://www.gra

ndviewresearch.com

3. IDC. (2024). The state of data security in edge computing. Retrieved from https://ww

w.idc.com

4. McKinsey & Company. (2024). The impact of edge computing on operational efficien

cy. Retrieved from https://www.mckinsey.com/industries/technology

5. National Institute of Standards and Technology (NIST). (2018). Framework for imp

roving critical infrastructure cybersecurity (Version 1.1). Gaithersburg, MD: National

Institute of Standards and Technology. Retrieved from https://www.nist.gov

6. Statista Research Department. (2023). IoT data growth worldwide 2021-

2025. Retrieved from https://www.statista.com

7. World Economic Forum. (2024). Harnessing the potential of edge computing for rea

l-time data processing. Retrieved from https://www.weforum.org/reports

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