3 big data security proplems in the 4th industrial revolution managements

Bigdata và security là các vấn đề quan trọng được đề cập trong phát triển cách mạng công nghiệp 4.0. Đặc biệt bigdata là một thách thức rất lớn của thế giới trong việc quản trị dữ liệu, sắp xếp và phân luồn sao cho hiệu quả để đảm bảo việc truy cập được nhanh nhất, lưu trữ khoa học nhất, an toàn dữ liệu nhất. Bên cạnh đó, sự phát triển tăng trưởng nhanh mạnh của bigdata tiếp tục tạo ra thách thức về an ninh bảo mật dữ liệu khi không gian mạng dễ dàng truy cập và cũng dễ dàng bị đánh cắp thông tin

Big data & Security proplems in the 4 th industrial revolution

managements

1 Historical

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

Quy Nguyen Kim

Institue Mining of Science and Technology

Corespondence:quynguyenkim.90@gmail.com

Abstract

Big data and cloud are two ideas and was developed in the third industrial revolution When internet expaned and keep opening with amazing speed, the storage needs and keep acessing on data was limited if data was storaged on normal physic without internet transfering, especialy for remote access That’s reason to born of Cloud with internet accessing appliances at everywhere, everytime only with internet access protocol Beside, with upgraded of connections and growth up of internet equipments –especialy IoT devices to create super big data was transfer and storaged on internet, on cloud which we’ve must managed, sorted, access quickly with realiable security It’s make a really challenge to system management where we can use for big data & Cloud to bring opportunities and benefits

Keywords : fourth industrial revolution, bigdata, cloud, security

getting started with the first data centers and the development of the

relational database

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services Hadoop (an open-source framework created specifically to store and analyze big data sets) was developed that same year NoSQL also began to gain popularity during this time

The development of open-source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they

make big data easier to work with and cheaper to store In the years since then, the volume of big data has skyrocketed Users are still generating huge amounts of data—but it’s not just humans who are doing it

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance The emergence of machine learning has

produced still more data

While big data has come far, its usefulness is only just beginning Cloud computing has expanded big data possibilities even further The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data And graph databases are becoming

increasingly important as well, with their ability to display massive

amounts of data in a way that makes analytics fast and comprehensive

2 Big data defined and how it works?

Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information

processing that enable enhanced insight, decision making, and process automation

Volume Organizations collect data from a variety of sources, including

transactions, smart (IoT) devices, industrial equipment, videos, images, audio, social media and more In the past, storing all that data would have been too costly – but cheaper storage using data lakes, Hadoop and the cloud have eased the burden

Velocity With the growth in the Internet of Things, data streams into

businesses at an unprecedented speed and must be handled in a timely manner RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in near-real time

Variety Data comes in all types of formats – from structured, numeric

data in traditional databases to unstructured text documents, emails,

videos, audios, stock ticker data and financial transactions

Before businesses can put big data to work for them, they should consider how it flows among a multitude of locations, sources, systems, owners and users There are five key steps to taking charge of this ”big data fabric” that includes traditional, structured data along with unstructured and

semistructured data:

- Set a big data stragety

- Identify big data sources

- Acess, manage and store the data

- Analyze the data

- Make intelligent, data-driven decisions

3 Big data at where?

Big data can help you address a range of business activities, from customer experience to analytics Here are just a few

Product development: Companies like Netflix and Procter & Gamble use big data to anticipate customer demand They build predictive models for

new products and services by classifying key attributes of past and current products or services and modeling the relationship between those

attributes and the commercial success of the offerings In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store rollouts to plan, produce, and launch new products

Predictive maintenance: Factors that can predict mechanical failures may

be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log

entries, sensor data, error messages, and engine temperature By analyzing these indications of potential issues before the problems happen,

organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime

Customer experience: The race for customers is on A clearer view of

customer experience is more possible now than ever before Big data

enables you to gather data from social media, web visits, call logs, and

other sources to improve the interaction experience and maximize the value delivered Start delivering personalized offers, reduce customer

churn, and handle issues proactively

Fraud and compliance: When it comes to security, it’s not just a few rogue hackers—you’re up against entire expert teams Security landscapes and compliance requirements are constantly evolving Big data helps you

identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster

Machine learning: Machine learning is a hot topic right now And data— specifically big data—is one of the reasons why We are now able to teach machines instead of program them The availability of big data to train machine learning models makes that possible

Operational efficiency: Operational efficiency may not always make the news, but it’s an area in which big data is having the most impact With big data, you can analyze and assess production, customer feedback and

returns, and other factors to reduce outages and anticipate future

demands Big data can also be used to improve decision-making in line with current market demand

Drive innovation: Big data can help you innovate by studying

interdependencies among humans, institutions, entities, and process and then determining new ways to use those insights Use data insights to

improve decisions about financial and planning considerations Examine trends and what customers want to deliver new products and services Implement dynamic pricing There are endless possibilities

4 Challenges

Managing Big Data Growth

With a name like big data, it’s no surprise that one of the largest challenges

is handling the data itself and adjusting to its continuous growth It is

estimated that the amount of data in the world’s IT systems doubles every two years and is only going to grow

The best solution for companies is to implement new big data technologies

to help manage all of it Below are a few different types of big data

technologies:

- Storage technology to structure big data

- Deduplication technology to get rid of extra data that is wasting

space and in turn, wasting money

- Business intelligence technology to help analyze data to discover patterns and provide insights

Delayed analyzing big data

Data is constantly coming in and from all directions, so how do we keep up and process it in a timely manner? The most efficient way is to exclude some data from our analysis Determine which data is most relevant and focus on that This will save our organization time and money

Securing big data

Using a variety of big data and analytics tools without putting proper

cybersecurity measures in place first could make your organization

vulnerable to cyberattacks And when a breach happens and you use a number of tools, it can be hard to identify where the breach came from or which tool has been compromised

The solution is to enhance our cybersecurity practices to cover our big data tools and initiatives Grow the team’s knowledge on data security in particular and test the security parameters often to ensure they are

protecting our information

If we haven’t already embraced big data, it’s time to do so It’s important for organizations to work around these challenges because the fear of big data should not outweigh the benefits it can provide Leverage the data to create better insights and blow our competition out of the water

Disclosure statement

No potential conflict of interest was reported by the author(s)

Notes on contributors

Quy Nguyen Kim is a researcher and writer in Institue Mining of Science and Technology (Vietnam)

References

1 Oracle, (2022), “”What is big data”, Retrieved from

https://www.oracle.com/big-data/what-is-big-data/

2 Gartner.com, (2020), “big data”, Retrieved from

https://www.gartner.com/en/information-technology/glossary/big-data

3 SAS.com, (2022) “what is big data” Retrieved from

https://www.sas.com/en_us/insights/big-data/what-is-big-data.html

4 Comptia.org, (2019), “4 big data challenges and how to overcome

them”, Retrieved from

https://www.comptia.org/blog/4-big-data-challenges-and-how-to-overcome-them