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Dimensions of Big Data | Volume, Variety, Velocity, Veracity

How do you define big data? Since the term is used across a wide range of mass media environments, it is not surprising that its precise meaning is often unclear. According to IBM data scientists, big data is measured in four dimensions: volume, variety, velocity, and accuracy. Big data is the convergence of these four dimensions, i.e., volume, variety, velocity, and validity. Designed to help businesses realise and cope with the emergence of big data, the 4V’s are a data management trend.

Dimensions of Big Data


Quantity refers to the amount of data, as big data is frequently characterized as massive datasets, with petabytes and zettabytes being commonly used to quantify big data. A vast amount of data is generated every second. Previously, employee-created data was used. Big data today is generated by machines, networks, and human interaction on systems like social media, and the volume of data available for analysis is huge.


Data sources and types are becoming more diverse, and they need to be managed and analyzed accordingly. In the past, we used spreadsheets and databases to store data. These days, data is reached via emails, photos, videos, monitors, PDFs and audio. Therefore, we need to integrate all the various types of data – structured, semi-structured and unstructured – from multiple sources on the internal and external sides. Although, storage, mining, and analysis of unstructured data are challenging due to their variety.


Known as big data velocity, it refers to the rate at which data flows in from different sources, including business processes, machines, networks, social media sites, mobile devices, etc. Data flowing in is massive and continuous. In case you are able to handle the velocity, real-time data can provide business and research with strategic competitive advantages and ROI. Volume and velocity issues can be addressed by sampling data.


Validity means data is accurate and correct for its intended purpose, as with big data veracity. If you intend to use the results of big data analysis to take decisions, they must be accurate.

Other Vs of Data

Aside from these 4V’s, there are other dimensions that are crucial to operationalizing big data, and they are:


In this dimension, bias, noise, or abnormal patterns in the data are considered. What impact does the data storage and mining have on the analysis? In light of the growing amount of data being generated at an increasing rate, at an unprecedented pace, and in a variety of formats, you will need to manage the uncertainty associated with certain types of data.


Big data volatility refers to how long the data is valid and how long it should be stored. In this world of real-time data, you need to determine at what point the data is no longer relevant to the current analysis.


The meaning of data is constantly changing. Computers have a difficult time processing language because words often have multiple meanings. Data scientists must use sophisticated tools to understand context and context meaning in order to account for this variability.


Nontechnical stakeholders and decision makers must be able to understand the data. Visualization is the process of creating complex graphs that tell the data scientist’s story, turning data into information, information into insight, insight into knowledge, and knowledge into advantage.


What can organizations do to improve decision-making with big data? Several McKinsey articles suggested that big-data initiatives might reduce health-care spending by $300 billion to $450 billion, or 12 to 17 percent of current US health-care costs. Big data could lead to a goldmine of business opportunities and savings.

Bringing It All Together

Whatever number of Vs you prefer in your big data, one thing is for sure: Big data is here, and it’s only going to grow bigger. Every organization should understand what big data means to them and how it can help them. There really are no limits to what you can do.

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