Quy Nguyen Kim
Big data & Security proplems in the 4th industrial revolution managements

Big data & Security proplems in the 4th industrial revolution
managements
Quy Nguyen Kim
Institue Mining of Science and Technology
Corespondence:

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

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

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Quy Nguyen Kim
Big data & Security proplems in the 4th industrial revolution managements

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 opensource 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.

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Quy Nguyen Kim
Big data & Security proplems in the 4th industrial revolution managements

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

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Quy Nguyen Kim
Big data & Security proplems in the 4th industrial revolution managements

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
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Big data & Security proplems in the 4th industrial revolution managements

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.
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Quy Nguyen Kim
Big data & Security proplems in the 4th industrial revolution managements

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
/>2. Gartner.com, (2020), “big data”, Retrieved from
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Quy Nguyen Kim
Big data & Security proplems in the 4th industrial revolution managements

3. SAS.com, (2022). “what is big data”. Retrieved from
/>4. Comptia.org, (2019), “4 big data challenges and how to overcome
them”, Retrieved from
/>
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