Introduction

Big data is important since it enables organizations to collect, accumulate, manage, and use huge volumes of data at the precise swiftness and time to gain insights. As defined by Qi (2020), big data refers to a combination of unstructured or organized data collected by organizations to gain insights to improve their operations. Among the uses of big data are predictive modeling, analytics, and machine learning projects. Big data, therefore, is a tool to increase the value if used correctly. Different sectors such as medical, energy, education, and technology can utilize big data for real-time market data analysis. As such, organizations that use big data generally have a competitive advantage over those that don’t since they can make smart and quick decisions depending on the insights acquired from big data.

Body

            Hiba et al. (2015) state that there is no clear description of big data. Consequently, big data has been defined based on some of its features, commonly referred to as the five Vs of big data. The five Vs of big data are; volume, velocity, veracity, variety, and value which have dissimilar impacts on the business.

5. Volume

Volume concerning data refers to the size and amount of data that exists at a given time. If the volume of data gathered is substantially huge, it can be termed big data. However, what is referred to as big data is relative to the time it's gathered as it will change depending on the existing computing power in the market (Wang et al., 2020). The volume of big data is however dependent on other characteristics like veracity. Huge amounts of data that are of poor quality are of minimal use for an organization. A real-world example demonstrating the relationship is unclean data. Unclean and inaccurate data has little to no use, therefore, making it a worthwhile factor when gathering data.

5. Veracity

Veracity is a characteristic of big data that is equivalent to quality. It is important for data collectors in an organization to consider whether the data they are collecting is clean and accurate. Otherwise, gathering data that has missing pieces could be of little to no use. Also, a huge quantity of data can cause more mix-ups that acumens if it's inadequate. The veracity of big data is directly linked with the value it creates, which is another among the five Vs of big data. For instance, if data about the medications of cancer patients is incomplete, the medications could be ineffective thereby adding no value.

5. Velocity

Velocity refers to how quickly data is produced and transferred where needed. Velocity is an important factor in organizations that require their data to move swiftly, so it’s available at the correct time for decision-making (Oussous et al., 2018). Data could flow from sources such as machines, smartphones, and social networking sites. A quick analysis of the data is needed to provide valuable output for decision-making. Velocity may be associated with variety as another one of the five Vs since a variety of data is important for smart decision-making. For instance, a consumer’s data collected from a smartphone would be more helpful if it contained other brands of the same product that the consumer may purchase. This may be very helpful for gaining insights that would give them a competitive advantage over their competitors.

5. Variety

Variety refers to the multiplicity of data sources. Heterogenous sources can provide structured or unstructured data. Such a varied type of data can be sorted and examined for relationships. However, it may be better in some instances to have a limited set of gathered data than an organization can manage. Notwithstanding, a variety of data collected may be beneficial it can be of value to an organization. Value as a characteristic of big data is significant in determining the type of data to collect. A real-world example is collecting vast image datasets of social media users that may be of no use to the organization. Instead, it would be better to refine the type of images to be collected to reduce the analysis and sorting period.

5. Value

This refers to the significance of the insights that big data can provide once analyzed. It directly relates to what organizations can do with the gathered data. Most organizations have created data platforms with data pools and devoted huge sums of funds intending to generate corporate value from their investments. Value is associated with veracity since quality data can provide valuable insights from the collected data. An instance of value creation using veracity of data is offering unique products to customers which can increase market share if the products are beneficial to consumers.

Conclusion

Big data has proved to be a worthwhile investment for major companies over time. It is therefore important for organizations to ensure that the five Vs mentioned above are implemented for better application of big data. Nevertheless, storing and interpreting big data is a challenging task for the majority of these companies. Security and computing power are tech requirements that few companies can navigate around successfully. As such, cloud computing has been an innovative solution to fix this problem and to ensure success in the big data field. There is a need for imminent study to understand the serious questions of exploiting big data.