new information communication technology e information about big data

History 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 f

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

2.1 Why do we need big data? - - Q1 01122011211 1115211 1111201111118 1 k1 4

CHAPTER HI: ADVANTAGES AND DISADVANTAGES 6

3.1 Advantages of big data Q0 n1 1112112111101 111011101 1110111110111 6

3.2 DIisadvantages of big data Là 1111211211101 1101 1110111101112 11 tru 9

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

4.1 Applications of big data In some doma11s 55-522 225* 22s x22 s+2 II

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4.1.3 Big Data and Cloud Computing - - c2 1 2222211122112 x+2 12

CHAPTER VY: OPPORTUNTTIES AND CHALLENGES 13

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GROUP CONTRIBUTION 0S 2.12211121221211 1011 022212 k ray 13

LIST OF TABLES

Table 1.4 Six Vs of big data 0.0 ee ccc ee cee ce cee ce cee einen eer e eres

Table 2.1 Statistics show that big data 1s Important

LIST OF FIGURES Figure I l Vietnam Internet user ffom 1996 - 2 2c 222111211115 5111 82x cay

Figure 1 2 Vietnam Internet User Behavior 20 lỐ -¿ 5c 222222 22xssxcss

Figure 1 3 Top ten Asia Selfpaced e-learning - -c c ccns2n v22

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1.1

1.2

CHAPTER I: INTRODUCTION OF BIG DATA

Definition

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity This is also known as the three Vs Put simply, 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

History 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

location The project was later dropped but is generally accepted as the beginning of the electronic data storage era

Types of big data:

1.3.1 Structured Data:

Any data that can be processed, is easily accessible, and can be stored in a fixed format is called structured data In Big Data, structured data is the easiest to work with because it has highly coordinated measurements that are defined by setting parameters

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

trustworthiness of data

Data from a trusted source, such as a government agency, is likely to be more veracious than data from an unknown

source

Value

The potential of data to

be used to create insights and improve decision-making

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

2.1

CHAPTER IT: ROLES OF BIG DATA Why do we need big data?

Big Data is one of the major trends in digital technology today, and

is even presented as the hallmark of the next transformations in the market and society According to a recent survey, 95% of collected data never gets analyzed That’s a lot of untapped potential that you can use

to take your business to greater heights This is where analyzing big data can help Big Data works with the objective of improving the work processes of its users to obtain quick and valuable information about market trends, consumer behavior and potential opportunities

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

90% of the world's data was IBM created in the last two years

2.5 quintillion bytes of data are Domo created every day

90% of the data in the world is IDC unstructured

By 2025, the global big data Research and Markets

market is expected to reach $274.3 billion

97% of companies that use big Forbes data say it has helped them

improve their business

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

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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

*https://edition.cnn.com/2014/11/04/tech/gallery/big-data-techonomics-graphs/index.html

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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