new information communication technology e information about big data

UNIVERSITY OF ECONOMICS AND LAW FACULTY OF INFORMATION SYSTEMS

GROUP 7 /MIDTERM REPORT

NEW INFORMATION COMMUNICATION TECHNOLOGY (E)

INFORMATION ABOUT BIG DATA

Supervisor: Ph.D Nguyen Thi Thuy Hanh Student:

Nguyễn Yến Nhi - K234111443 Hạ Thanh Thảo - K234101310 Nguyễn Hoàng Đức - K2341 I 1430 Ngô Thị Phương Thảo - K234111450 Nguyễn Thị Bảo Trân - K234111455

Ho Chỉ Minh City, November /2023

TABLE OF CONTENT

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CHAPTER I: INTRODUCTION OFE BIG DATA - eeteetee l

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CHAPTER IT: ROLES OF BIG DATTA - c2 2212122221222 m2 2 4

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CHAPTER HI: ADVANTAGES AND DISADVANTAGES 6

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CHAPTER IV: HOW BIG DATA CAN BE USED IN BUSINESS II

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LIST OF TABLES

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LIST OF FIGURES

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Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and “70s when the world of data was just getting started with the first data centers and the development of the relational database The earliest examples we have of humans storing and analyzing data are the tally sticks, which date back to 18,000 BCE The Ishango Bone was discovered in 1960 in what is now known as Uganda and is thought to be one of the earliest pieces of evidence of prehistoric data storage

The government had to keep track of contributions from 26 million Americans and more than 3 million employers IBM got the contract to develop a punch card-reading machine for this massive bookkeeping project.The first data-processing machine appeared in 1943 and was developed by the British to decipher Nazi codes during World War II This device, named Colossus, searched for patterns in intercepted messages at a rate of 5,000 characters per second, reducing the length of time the task took from weeks to merely hours

Then, in 1965, the United States Government decided to build the first ever data center to store over 742 million tax returns and 175 million sets of fingerprints They decided to do this by transferring those records onto magnetic computer tape that had to be stored in a single

1.3.2 Unstructured Data:

Unstructured data in Big Data is where the data format constitutes multitudes of unstructured files (images, audio, log, and video) This form of data is classified as intricate data because of its unfamiliar structure and relatively huge size An example of unstructured data is an output returned by ‘Google Search’ or “Yahoo Search `

1.3.3 Semi-structured Data:

In Big Data, semi-structured data is a combination of both unstructured and structured types of data This form of data constitutes the features of structured data but has unstructured information that does not adhere to any formal structure of data models or any relational database Some semi-structured data examples include XML and JSON 1.4, Six Vs of big data

Table 1.4 Six Vs of big data

A social media platform generates millions of posts per day

data

A customer profile might include structured data (name, address, email address), semi- structured data (purchase history), and unstructured data (social media posts)

Velocity

The speed at which data is generated and processed

Sensor data from a manufacturing plant might be streamed to a cloud-based analytics platform in real time

A retailer might use customer purchase data to identify trends and develop targeted marketing campaigns

Variability The changing nature of

and quality can change over time For example, the way that people use social media has changed dramatically in the past decade

The role of Big Data is to combine structured data with unstructured data to obtain insights, bringing new solutions and previously unthinkable actions The differential that can take your company to another level in the digital world is knowing how to look at your data, both structured and unstructured, and having the keen sensitivity to pinpoint a detail that can be combined with existing numbers Having a large amount of data allows you to optimize the search for trends to foster business growth The possibility of having this large bank is essential for the analyses to be efficient and help performance and decision-making

Table 2.1 Statistics show that big data is important

market is expected to reach $274.3 billion

2.2 Big data use cases

In the energy industry, big data helps oil and gas companies identify potential drilling locations and monitor pipeline operations; likewise, utilities use it to track electrical grids

Financial services firms use big data systems for risk management and real-time analysis of market data

Manufacturers and transportation companies rely on big data to manage their supply chains and optimize delivery routes

Other government uses include emergency response, crime prevention and smart city initiatives

In business, employees can use Big Data for many tasks Include: Audit and manage all customer data to enhance their experience and provide direction for customer retention

Analyzing the activities of businesses and companies helps improve work performance and operate more organized and effective

Minimize business risks by analyzing, controlling and detecting fraudulent activities

Optimize prices to increase revenue

CHAPTER III: ADVANTAGES AND DISADVANTAGES

3.1 Advantages of big data

Figure 3.1 Increase in research into big data!

3.1.1 Better Decision Making

Many businesses including travel, real estate, finance, and insurance are mainly using big data to improve their decision-making capabilities Since big data reveals more information in a usable format, businesses can utilize that data to make accurate decisions on what consumers want or not and their behavioral tendencies

Big data facilitates the decision-making process by providing business intelligence and advanced analytical insights The more customer data a business has, the more detailed overview it can gain about its target audience

Data-driven insights reveal business trends and behaviors and allow companies to expand and compete by optimizing their decision-making Furthermore, these insights enable businesses to create more tailored products and services, strategies, and well-informed campaigns to compete within their industry

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3.1.2 Improved customer service

Improving customer interactions is crucial for any business as a part of their marketing efforts Since big data analytics provide businesses with more information, they can utilize that data to create more targeted marketing campaigns and special, highly personalized offers to each individual client

The major sources of big data are social media, email transactions, customers’ CRM (customer relationship management) systems, etc So, It exposes a wealth of information to businesses about their customers’ pain points, touchpoints, values, and trends to serve their customers better

Moreover, big data helps companies understand how their customers think and feel and thereby offer them more personalized products and services Offering a personalized experience can improve customer satisfaction, enhance relationships, and, most of all, build loyalty

3.1.3 Fraud Detection

Financial companies, in particular, use big data to detect fraud Data analysts use machine learning algorithms and artificial intelligence to detect anomalies and transaction patterns These anomalies of transaction patterns indicate something is out of order or a mismatch giving us clues about possible frauds

Fraud detection is significantly important for credit unions, banks, credit card companies to identify account information, materials, or product access Any industry, including finance, can better serve its customers by early identification of frauds before something goes wrong

For instance, credit card companies and banks can spot fraudulent purchases or stolen credit cards using big data analytics even before the cardholder notices that something 1s wrong

3.1.4 Increased agility

Another competitive advantage of big data is increasing business agility Big data analytics can help companies to become more disruptive and agile in markets Analyzing huge data sets related to customers enables companies to gain insights ahead of their competitors and address the pain points of customers more efficiently and effectively

On top of that, having huge data sets at disposal allows companies to improve communications, products, and services and reevaluate risks Besides, big data helps companies improve their business tactics and strategies, which are very helpful in aligning their business efforts to support frequent and faster changes in the industry

3.1.5 Increased productivity

According to a survey from Syncsort, 59.9% of survey respondents have claimed that they were using big data analytics tools like Spark and Hadoop to increase productivity This increase in productivity has, in turn, helped them to improve customer retention and boost sales

Modern big data tools help data scientists and analysts to analyze a large amount of data efficiently, enabling them to have a quick overview of more information This also increases their productivity levels

Besides, big data analytics helps data scientists and data analysts gain more information about themselves so that they can identify how to be more productive in their activities and job responsibilities Therefore, investing in big data analytics offers a competitive advantage for all industries to stand out with increased productivity in their operations 3.1.6 Reduce costs of business processes

The surveys conducted by New Vantage and Synecsort (now Precisely) reveals that big data analytics has helped businesses to reduce their expenses significantly 66.7% of survey respondents from New Vantage claimed that they have started using big data to reduce

expenses Furthermore, 59.4% of survey respondents from Syncsort claimed that big data tools helped them reduce costs and increase operational efficiency

Moreover, Big data analytics tools like Cloud-Based Analytics and Hadoop can help reduce costs for storing big data

3.2 Disadvantages of big data

Data Data Perfomance Skills Data Velocity Cost

Big data experts and data scientists are two highly paid careers in the data science field Therefore, hiring big data analysts can be very expensive for companies, especially for startups Some companies have

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to wait for a long time to hire the required staff to continue their big data analytics tasks

3.2.2 Security risks

Most of the time, companies collect sensitive information for big data analytics Those data need protection, and security risks can be demerits due to the lack of proper maintenance

Besides, having access to huge data sets can gain unwanted attention from hackers, and your business may be a target of a potential cyber- attack As you know, data breaches have become the biggest threat to many companies today

Another risk with big data is that unless you take all necessary precautions, important information can be leaked to competitors 3.2.3 Compliance

The need to have compliance with government legislation is also a drawback of big data If big data contains personal or confidential information, the company should make sure that they follow government requirements and industry standards to store, handle, maintain, and process that data

So, data governance tasks, transmission, and storage will become more difficult to manage as the big data volumes increase

3.2.4, Data Quality Issues

Even the most advanced big data platforms and cutting-edge technologies can’t compensate for poor quality information Duplicate records, inaccurate details, and formatting errors are just a few of the many data quality issues and anomalies that can lead to incorrect conclusions

As businesses gather more information on an ever-expanding scale from disparate sources and try to make it all actionable, it becomes increasingly difficult to ensure consistent quality across the board