Attention all professionals! As businesses and organizations continue to rely heavily on data-driven insights, understanding the characteristics of big data has become essential. Join me in exploring the five V's of big data - volume, velocity, variety, veracity, and value - to gain a deeper understanding of this powerful resource. Check out my latest article on "Characteristics of Big Data: Understanding the Five V's" to learn more. #BigData #DataScience #DataAnalytics #DataInsights #FiveVs

1. www.datacademy.ai
Knowledge world
Characteristics of Big Data: Understanding
the Five V’s
Volume, Velocity, Variety, Veracity, Value
5V’s of Big Data
History
It started in the year 2001 with 3 V’s, namely Volume, Velocity and Variety.
Then Veracity got added, making it 4 V’s. Then Value got added, making it
5V’s. Later came 8Vs, 10Vs etc.
We will discuss on the important ones (5V’s) Volume, Velocity, Variety,
Veracity, and Value.
1) Volume
It refers to the size of Big Data. Data can be considered Big Data or not is based
on the volume. The rapidly increasing volume data is due to cloud-computing
traffic, IoT, mobile traffic etc.

2. www.datacademy.ai
Knowledge world
Data growth prediction
2) Velocity
It refers to the speed at which the data is getting accumulated. This is mainly
due to IoTs, mobile data, social media etc.
In the year 2000, Google was receiving 32.8 million searches per day. As for
2018, Google was receiving 5.6 billion searches per day!
Approximate monthly active users as of 2018:
Facebook: 2.41 billion
Instagram: 1 billion
Twitter: 320 million
LinkedIn: 575 million
Facebook monthly active users growth since 2008
3) Variety
It refers to Structured, Semi-structured and Unstructured data due to
different sources of data generated either by humans or by machines.

3. www.datacademy.ai
Knowledge world
Structured data: It’s the traditional data which is organized and conforms to
the formal structure of data. This data can be stored in a relational database.
Example: Bank statement containing date, time, amount etc.
Semi-structured data: It’s semi-organized data. It doesn’t conform to the
formal structure of data. Example: Log files, JSON files, Sensor data, csv files
etc.
Unstructured data: It’s not an organized data and doesn’t fit into rows and
columns structure of a relational database. Example: Text files, Emails,
images, videos, voicemails, audio files etc.
4) Veracity
It refers to the assurance of quality/integrity/credibility/accuracy of the
data. Since the data is collected from multiple sources, we need to check the
data for accuracy before using it for business insights.
5) Value
Just because we collected lots of Data, it’s of no value unless we garner some
insights out of it. Value refers to how useful the data is in decision making. We
need to extract the value of the Big Data using proper analytics.
What are the other V’s?
Viscosity (complexity or degree of correlation), Variability (inconsistency in
data flow), Volatility (durability or how long time data is valid and how long it
should be stored), Viability (capability to be live and
active), Validity (understandable to find the hidden relationships).