Big Data is a term which describes a large volume of diverse, complex and fast-changing data, derived from new data sources. These data sets are so extensive that it is difficult to manage it with the traditional data processing software or the traditional software management tools.

1. The Complete Beginner’s Guide To Big Data in
2018
Big Data is a term which describes a large volume of diverse, complex and
fast-changing data, derived from new data sources. These data sets are so
extensive that it is difficult to manage it with the traditional data processing
software or the traditional software management tools.
Now, every organization aims to make their mark in this competitive market.
Effective data management is extremely essential for this. And handling
huge chunks of data is no easy task. This is where Big Data comes into the
picture.
To begin with, data management is not just another competency factor on
the shelf. It is more of a critical differentiator which can determine the
market winners. It’s not the amount of data that is important. What matters

2. the most is how well businesses manage the data and what they do with
that data.
What can Big Data do?
If you are still wondering why to incorporate Big Data development in your
business; here are a few reasons why:
1. Big Data can determine user behaviour
2. Big Data is capable of conducting predictive analysis
3. Big Data helps in deriving insights to frame business strategies
4. Big Data can conduct processes so as to extract value from data sets
Keeping all the above-mentioned factors in mind, organizations and
businesses are looking forward to Big Data development. Also, by
incorporating Big Data, they can come up with new initiatives and reformed
strategies which have the capability to transform any business.
In addition to that, the applications of Big Data are not just limited to
software or application development. Big Data development is used in many
other sectors like:
● Fintech
● Robotics
● Meteorology
● Medicine
● Environmental research
● Informatics and cybersecurity
Since the amount of Big Data keeps on increasing exponentially, it is not easy
to analyze it. But, proper management and study of that data can help you
to make an informed decision for your business. Thus, to simplify things, it is
necessary to understand the different types of Big Data.

3. Types of Big Data:
Big Data can be broadly classified into three categories:
1. Structured
Any data which you can store, process and access in a fixed format can be
classified as structured data. The data is already stored in databases in an
ordered manner. The format of data and how to derive value out of it is
priorly known.
Example of structured data: Information stored in any database software
2. Unstructured
Any data which has an unknown structure or format can be termed as
unstructured data. The data size is massive and it is not easy to derive value
out of it. The data can contain a mix of text files, videos and images.
Example of unstructured data: The output for any Google search
3. Semi-structured
It contains both the above forms of data. Many a time, if the data is defined
but not structured, it can be classified as semi-structured data.
Semi-structured data contains information which contains organizational
properties but is not in the traditional database format.
Example of semi-structured data: Any data stored in an XML file

4. Three V’s Of Big Data
In the early 2000 ’s, Gartner analyst Doug Laney articulated the concept of
Big Data in the form of three V’s. Also, as Big Data comprises of data
creation, storage and retrieval; it can be characterised remarkably in terms
of:
1. Volume
The definition of Big Data itself denotes a copious amount of data. All
businesses have huge amounts of data which includes data collected from —
business deals, transactions and investments, social media stats and other
data as well. Thus, the volume of data is crucial as it gives an idea about how
to extract value out of the data.
Also, whether a certain data set can be determined as Big Data or not
depends on the volume of the data. With Big Data, you will process high
volumes of low-density data. The size of the data could range from terabytes
or petabytes.
Hence, volume is one important parameter which has to be considered
when working with Big Data.
2. Velocity
Velocity refers to the speed at which data is generated. Generating includes
both; receiving of data and the action performed on the data. How fast the
data is generated and processed determines the actual potential of the data.
Also, the stream of generated data is unprecedented and massive.
So, velocity in Big Data deals with how quickly the data flows in and from
what sources. The data inflow is continuous and voluminous as it comes

5. from a number of sources- Business transactions, social media sites,
application logs and other networks.
Processing and functioning with this unprecedented data stream will
determine the real potential of the data. That is why velocity is crucial to Big
Data.
3. Variety
Nowadays, when it comes to data, we are not just limited to plain text data
or structured data in the form of databases. Data means different types of
data — structured, unstructured, numeric, audio, video, pdf, financial
transactions and ticker data. All these different types of data require
different preprocessing and correct handling to derive context out of it.
Also, a variety of data means different ways to mine, store and analyze each
type of data. As applications have evolved to large volumes of users, agile
processing is required and traditional databases are not enough to generate
business value.
Thus, variety is included in the three V’s of Big Data characteristics.
Endnote:
Big Data analysis has a definite business value. Incorporating it into your
business has five major advantages-
● Cost reduction
● Exponential growth
● Smart decision making
● Optimized offerings
● Reduced product development time

6. Nowadays, more and more businesses are using Big Data to outperform
their competition and analyze data. So, don’t wait any longer to exploit this
excellent business opportunity. It will cost your business a lot in terms of
time, money and resources.
Moreover, Big Data development will open up new opportunities for your
business and help you get a better perspective of consumer preferences.
Combining Big Data with high powered analytics will help your business in
accomplishing complex tasks smoothly and without any hassle.