Did you know there are five ‘V’s in Big Data? The five ‘V’s are characteristics that perfectly describe Big Data. These ‘V’s help data scientists to gain more value from their data and help organizations to gain more information about customer behavior.
Read further to discover what the 5 ‘V’s are about!
The first characteristic of Big Data is Volume. This characteristic refers to the amount of data that is present in an organization. There are increasing amounts of data, both from internal and external sources like an organization’s website or social media account. The traditional data sets used for storing this great volume of information are often not suitable enough, because the datasets are too large to be analyzed.
The second characteristic of Big Data is Variety. An organization can collect data from many different sources. This varies from external data, for example from social media, to internal data from devices from the employees. Besides this, data can be structured, unstructured or semi-structured. 80 Percent of all data is unstructured, but because of new Big Data techniques we can bring this kind of data together and analyze it.
The third characteristic of Big Data is Velocity. This characteristic refers to the speed by which Big Data can be generated, for example an Instagram post that goes viral. Besides this, it also refers to the analytical processing of data. Especially in the case of fraud, it is important to analyze data quickly, because within minutes it can be too late to intercept. When we work quickly with Big Data, we can make the most of it.
The fourth characteristic of Big Data is Veracity. This term refers to the trustworthiness of the data. In Big Data, different sources with different reliability and quality are combined with each other. Thanks to new Big Data technologies, it is possible to use this combined data to achieve valuable results during the analysis process.
The last, but not least characteristic of Big Data is the fifth one: Value. This characteristic refers to the great value Big Data can yield. Data is only useful when you can get value out of it. An example of this is making predictions and discovering patterns and relationships when analyzing Big Data. This ‘V’ is the most important of all five, because you can collect all the data you need, but without adding value to it, it is still useless.