Big data is an area that represents large amounts of data - both structured and unstructured datasets– whose size or type is beyond the ability of traditional databases to capture, analyze, systematically extract information from, manage and process the data with low latency.
Big data has some characteristics
such as high volume, high velocity or high diversity.
Big data is used in a lot of
industries. Big data applications are effective and powerful tools that make
things easier in various fields.
In this blog, we will explore the big data types, characteristics and their applications in real life.
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| Big data is a huge and very large collection of data in its various forms. |
Big Data: Types and Characteristics and Applications in Real Life
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What is Big Data?
Big data is a huge and very large collection of data in its various
forms of words, images, voice messages, etc.
Experts define big data as any set
of data that could not be handled in the traditional way and that exceeds the ability of commonly used software to capture, process, manage, transfer,
share, store, and analyze that data within an acceptable period of time.
From the service provider's point of
view, big data is the tool and process that organizations need to handle large
amounts of data for analysis.
The Importance of Big Data
Big data is of great importance as
it offers a highly competitive advantage for companies if they can benefit from
it because it provides a deeper understanding of its customers and their
requirements.
Big data helps
companies to make appropriate decisions within the company in a more effective way, based on the information extracted from customer databases, thus
increasing efficiency and profit and reduce losses.
Types of Big Data
Big data is divided into the
following categories:
Structured Data
Structured data is highly
system-wide, usually in written form, such as data in linked databases or data
in Excel tables. For example, there is a database of a company carrying one
million pieces of information for one hundred thousand of its customers.
Structured data contains each
person's name, phone number, and place of residence, job, and others.
These data are in a structured and organized way that you can easily search.
Unstructured Data
Unstructured data is random and
unordered. Examples include data on social media sites, whether written, such
as Facebook, Twitter, and LinkedIn, or videos such as YouTube and images
uploaded randomly on the Internet.
Semi-Structured Data
Between structured data and
unstructured data, there are also semi-structured data that carry some of the
rankings and are not categorized by full randomness, and there are other conditions
that can be defined by the data as big data besides size, speed, and diversity.
What are the Four Characteristics of Big Data?
Big data processing methods have
some characteristics that are different from traditional data processing
techniques.
The concept of big data applies to a
set of data that must meet one of the following characteristics:
Volume:
The volume of big data must be
obviously huge, so the amount of data on our devices is not huge.
Because it generally does not exceed
at best 1 terabyte (TB), but you can imagine that the size of only the images
stored on Facebook in 2015 more than 250 billion images, which increase daily
by 350 million images.
Here we can consider the volume of
images that Facebook stores daily with big data.
We can also consider the number of
tweets on Twitter per day with big data reaching more than 150 million tweets.
But it should be noted that big data
doesn't always have to be that big, of course, can be a bit smaller than that.
This is just an example to show that the size of this data is not small.
Velocity:
The second criterion to classify
data as huge data is the speed of this data flow, as we mentioned Twitter
receives 150 million tweets per day, which gets 2.5 million tweets per hour and
more than 41 thousand tweets per second.
Therefore they are considered
massive data for the speed of this data flow.
Variety:
There are different forms of data,
either in the form of written texts, voice messages, videos and other forms in
which such data can exist.
The diversity of this data can make
it fall under the classification of big data because it takes time to work on
them unlike if they are in one form.
Veracity:
Data is often viewed as something
definitive and reliable. The veracity of the data refers to the reliability of
the data.
The reality of data sets, problem
spaces and operational environments is that data is often inconsistent,
uncertain, and difficult to trust.
Every good manager knows that all
the data collected has inherent inconsistencies.
What are Real-Life Applications of Big Data Analytics?
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| What are real-life applications of big data analytics? |
Applications of Big Data in Real Life
At first, the importance of big data
is not as much in its size as in what we can do with it.
The government and private
institutions are currently interested in analyzing and extracting some
information and analyzes, as well as answers to many of the questions posed to
them through these data.
Big data helps them to develop their
services or products and related to them.
Here are some areas where owners can take
advantage of their big data.
Big Data in Banking Sector
Banks are moving to create a digital
database in which all data related to deposits or withdrawals, different
financial transactions and various customer service records. They are saved to
analyze all such data and thereby enhance their cybersecurity as well as
provide a set of innovative and personal offers for each individual customer to
make an individual and unique experience from the customer's banking
experience.
Big Data in Healthcare
Healthcare is one of the most
important areas that big data contributes to improving it strongly.
In the health sector, Big data
is used to improve services and better understand the overall health situation
so as to try to resolve health-related problems quickly.
In the United States, there is a system that stores, collects and shares data between more than 39 hospitals in
a region.
This data helps them to redefine and
apply the best medical practices to reduce the patient's return, predict the
risk of kidney failure and early intervention to minimize adverse outcomes, as
well as improve the management of hospitals and pharmacies to work more
efficiently to suit patients and their cases.
Big Data in Education Sector
The use of online learning tools and
increasingly interactive programs in education has increased the volume of
data, and the quality of the big data that can be collected from learning
environments varies.
Here we find big data about learners
and their learning experiences.
We also find in-depth data, data on
social interactions within learning environments, and detailed data on learning
activities such as text, media, videos, etc. These data also vary in quality
and depth in different proportions.
The analysis of these types of big
data can be used in education to provide a variety of opportunities and options
to improve the student learning process through adaptive or competency-based
learning.
These data can provide modern and
effective tools for measuring students' performance in educational tasks.
Big data can also help the
learning environment to suit the specific needs of students and can offer
a clearer analysis of individual and group responses to a range of
educational issues and other features.
Big Data in Industries
Big data helps companies enhance the
quality and efficiency of their various products while reducing waste in a more
practical way.
It also helps companies make more
flexible business decisions and quickly resolve problems based on analytics
derived from different data.
Big data also contributes to future
decisions that help the company achieve greater successes in terms of expansion
and profitability.
Big Data in Social Networks
Large companies and institutions
find their way in social networking sites, these sites help them to collect the largest amount of data possible from their customers directly and their
reactions and impressions towards their products or services, whether positive
or negative.
With this data, companies can
predict what customers will demand before they request it.
The users of these sites reveal
their own tastes, what they prefer and dislike, their reactions to many things,
the pages they follow and the posts they share with their friends on these
sites.
Big data also allows companies to
quickly discover problems that most of their customers complain about and
quickly resolve them.
Companies collect and analyze all
the comments, publications, and tweets that their customers and followers write
so that they can identify and monitor problems.
Big Data in Government Sector
When it comes to data management,
most government sectors face the problem of having huge amounts of data in
computer systems and most of these data are unstructured data, which means they
do not fit any predefined data model.
To understand the patterns in these
data, government sectors should apply big data analytics and statistical models
that seek to capture and process vast amounts of unstructured data.
One of the biggest sources of big
data is the data recorded through censuses and registration in government
databases, where governments can draw very valuable information by analyzing
those stored data.
Most government sectors do not have
enough staff or do not have the computational capacity to manage and analyze
all their data. Therefore, it is necessary to use big data tools through cloud
computing.