What are the 10 Vs of big data?

Five primary characteristics in big data, known as the 5 Vs: volume, velocity, variety, veracity, and value, are often discussed. These characteristics have been thoroughly explained in a previous Answer, which you can refer to here.

As big data evolves, more characteristics are coming to light, extending the list to the 10 Vs of big data. This answer will delve into the additional five traits: validity, vulnerability, volatility, visualization, and value.

Validity

Validity refers to the correctness and accuracy of data for its intended purpose. Ensuring data validity is essential for guaranteeing dependable analytical outcomes. For example, in a medical context, data scientists typically devote substantial time to cleaning their data before any analysis, ensuring the validity of their results. Robust data governance practices are crucial for maintaining consistent data quality and common definitions.

Vulnerability

With the rise in the volume and variety of data, security risks have also grown. A data breach involving big data can have grave implications. The notorious Yahoo data breach in 2013-14, involving 3 billion user accounts, emphasizes the need for data security. Therefore, implementing strong security measures to safeguard your data assets is vital.

Volatility

Volatility relates to the lifespan of data, i.e., how long the data stays relevant and should be stored. Given the high velocity and volume of big data, it's necessary to establish rules for data currency and availability and ensure swift information retrieval when needed. For example, a social media company might decide to store user data for a specific period due to its volatility and rapidly changing user behavior.

Visualization

With the increasing volume and variety of data, effectively visualizing this data becomes more challenging. Traditional graphs may not suffice when dealing with billions of data points. As such, innovative data visualization techniques such as data clustering, tree maps, sunbursts, parallel coordinates, circular network diagrams, or cone trees are often used. For instance, Twitter employs advanced visualization techniques to represent trending topics worldwide.

Value

Last but by no means least, the value extracted from big data is of utmost importance. All other traits of big data are pointless if they do not contribute to generating business value. The potential value from big data can include a better understanding of customers, process optimization, and improved business performance. For instance, Netflix utilizes big data to understand viewer preferences and offer personalized recommendations, thereby enhancing their service.

Conclusion

Comprehending these traits is critical when undertaking a big data strategy. It assists in preparing for both the challenges and benefits that big data initiatives present. By achieving a comprehensive understanding of the 10 Vs of big data, we can leverage its potential more effectively across various fields.