The Definitive Guide to Understanding the 8 V’s of Big Data

The Definitive Guide to Understanding the 8 V’s of Big Data

Big Data is a crucial aspect of modern-day businesses, and it has become increasingly important for companies to comprehend the different facets of Big Data. One common way of understanding Big Data is through the 8 V’s of Big Data. These V’s are volume, velocity, variety, variability, veracity, visualization, value, and viability. In this article, we will cover each of the 8 V’s of Big Data in detail.

Volume

Volume refers to the size of data that is created each day. With the exponential increase in the number of devices, data is being generated at an unprecedented rate. According to IBM, 90% of the world’s data was generated in the last two years alone. Volume has become a significant challenge for companies that have to manage and store this data. Batch processing or distributed systems are commonly utilized for handling such tremendous volumes of data.

Velocity

Velocity refers to the speed at which data is being generated. With the growing number of devices, data is being generated at an increasingly rapid pace. Therefore, it is essential for businesses to have real-time data processing frameworks and platforms to analyze and make decisions on the data generated at speed.

Variety

Variety refers to the different types of data that are generated. Data can be in the form of structured data, unstructured data, or semi-structured data. Structured data refers to data that follows a well-defined format, such as database tables. Unstructured data, on the other hand, refers to data that does not have a defined structure, such as emails or videos. Semi-structured data is a combination of the two. To handle this array of data types, businesses have to use different methods to store, process, and analyze them.

Variability

Variability refers to the inherent inconsistency in data. Data may not always be in a well-defined format, which makes it a challenge to process. Therefore, businesses must utilize data cleansing tools that can transform data into a structured format.

Veracity

Veracity refers to the quality of data or the degree to which data can be trusted. The quality of data is dependent on various factors such as data source, data collection method, and the process of data cleansing. Businesses must ensure that the data they collect is accurate and trustworthy to provide reliable conclusions.

Visualization

Visualization refers to the process of representing data graphically. The use of data visualization tools is crucial to generate insights that help in decision-making. Visualization enables businesses to represent data in an interactive and digestible manner, which can be used to drive business strategies.

Value

Value refers to the business’s most significant ultimate goal, that is, to derive value from the data collected. Businesses must generate insights from data that would contribute positively to the growth and development of the organization.

Viability

Viability refers to the feasibility of using Big Data for a particular business goal. Businesses must evaluate the potential impact of Big Data on their operations, infrastructure, skills, and cost before implementing it.

Conclusion

To wrap up, understanding the 8 V’s of Big Data can provide companies with insights and guide them in making data-driven decisions. When it comes to Big Data, there is no one-size-fits-all solution; each business must tailor their approach to suit their unique needs. Therefore, it is essential that companies comprehend the different V’s of Big Data and apply them to their specific business operations.