CharacteristicsofBigData:Understanding theFiveV’s
Volume,Velocity,Variety,Veracity,Value
5V’s ofBig Data
History
It started in the year 2001 with 3V’s, namely Volume,VelocityandVariety. Then Veracity got added, making it4V’s. Then Valuegotadded, making it 5V’s. Later came 8Vs, 10Vsetc.
We will discuss onthe important ones(5V’s) Volume, Velocity, Variety, Veracity, andValue.
1) Volume
It refers to the sizeofBig Data. Data can be consideredBig Data or not is based on the volume. Therapidly increasingvolume data is dueto cloud-computing traffic, IoT, mobiletraffic etc.
Datagrowth prediction
2) Velocity
It refers to the speedat which the datais getting accumulated. This is mainly due to IoTs, mobiledata, social mediaetc.
In the year2000, Google was receiving 32.8 million searches per day.As for 2018, Google was receiving 5.6billion searches per day!
Approximate monthly active usersasof2018:
Facebook:2.41billion
Instagram: 1billion
Twitter: 320 million
LinkedIn: 575 million
Facebook monthlyactive usersgrowthsince2008
3) Variety
It refers to Structured, Semi-structured and Unstructured data due to differentsources ofdata generatedeitherby humans orby machines.
Structureddata: It’s the traditional data which is organized and conforms to the formal structure of data. This datacan be stored ina relational database. Example:Bank statement containing date, time, amountetc.
Semi-structureddata: It’s semi-organized data. It doesn’tconform to the formal structure of data.Example:Log files, JSON files, Sensor data, csv files etc.
Unstructureddata: It’s not an organized dataand doesn’tfit into rows and columns structureofa relational database.Example:Text files, Emails, images, videos, voicemails, audio files etc.
4)Veracity
It refers to theassurance of quality/integrity/credibility/accuracy ofthe data. Since the data is collected frommultiple sources, we need to check the data foraccuracybefore using it for business insights.
5)Value
Just because we collected lots ofData,it’s of no value unless we garner some insights out of it. Valuerefers to howuseful the data is indecision making.We need to extract the value oftheBig Data using proper analytics.
WhataretheotherV’s?
Viscosity(complexity or degree of correlation), Variability(inconsistency in data flow), Volatility(durability or how long time data is valid and how longit should be stored),Viability(capability to be live and active), Validity(understandable to find the hidden relationships).