The External and Internal Characteristics of Big Data

Introduction to Big Data

Big data resembles a data flood. The abundance of data extends day by day. Big data focuses on the vast extent of data, which may be in the form of structured, unstructured, and semi-structured formats. Structured data consists of text files that can be displayed in rows and columns, making it easily processed. In contrast, unstructured data cannot be displayed in a relational database. Examples of unstructured data include word processing documents, presentations, audio, video, emails, and many other business documents. The third category is semi-structured data, which includes XML, JSON, and NoSQL databases (McAfee & Brynjolfsson, 2012).

The External and Internal Characteristics of Big Data

The term big data is highly linked with unstructured data. It is estimated that 80% of data in big data is unstructured. In reality, big data refers to data that cannot be handled by traditional databases. Traditional database systems hold data in terms of gigabytes, while big data systems store data in petabytes, exabytes, zettabytes, etc. Companies need to retain or hire highly experienced staff for deep analytical views of big data. The prevalence of big data is continuously increasing on popular social sites like Facebook and Twitter. Big data understanding varies according to business, technology, and industry terms. McKinsey identifies five domains in which data rapidly grows: healthcare, public sector, retail, manufacturing, and personal location data. The main advantage of big data is that it provides scalability and enhanced data analytics (Manyika et al., 2011).

Real-Life Applications and the Five V's of Big Data

Examples of big data in real-life scenarios include banks, social media, web data, and any type of daily transactions. The definition of big data is complete with these five V’s: volume, variety, velocity, veracity, and value. Here, the 5 V’s of big data are elaborated in plain language:

Volume

In big data terms, the word “big” defines volume. In the future, data will be measured in zettabytes. A large amount of data is being shared on social networking sites. Here are some interesting statistics that show the volume of data. According to Internet Live Stats, in 1 second there are:

• 64,551 Google searches
• 7,886 tweets on Twitter
• 822 Instagram photos uploaded
• 72,179 YouTube videos viewed
• 2,655,007 emails sent, including spam mails
• 52,180 GB of internet traffic
• 2.5 million pieces of content shared by Facebook users
• 571 websites created every minute

Variety

As previously discussed, the types of data include structured, semi-structured, and unstructured formats. These types of data are difficult to handle using traditional database systems. The various types of data are referred to as variety. Nowadays, a significant amount of structured data is generated (Gandomi & Haider, 2015).

Velocity

The speed at which data is created is known as velocity. Examples of data spawned from social networking sites include tweets on Twitter, and status updates, comments, and shares on Facebook. Data is generated in real-time, near real-time, hourly, daily, weekly, monthly, yearly, in batches, and so on.

Veracity

Veracity refers to the conformity of data. Attributes of veracity include the accuracy, integrity, and authenticity of data. It leads to the uncertainty of data, whether the data is verified or not. This aspect is critical for businesses to ensure that decisions are based on reliable data (Zikopoulos et al., 2012).

Vagueness

Confusion about big data is termed vagueness. Various tools are used to handle big data, such as Hadoop, Hive, MapReduce, Apache Pig, and others. These tools help in processing and analyzing large datasets efficiently.

Value

Last but not least, value is the most important characteristic of big data. It ensures that the acquired data is useful for the organization. Value-added information can significantly enhance organizational performance and decision-making processes (Davenport, 2014).

References:

- McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68.

- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.

- Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

- Zikopoulos, P. C., Eaton, C., deRoos, D., Deutsch, T., & Lapis, G. (2012). Understanding big data: Analytics for enterprise class Hadoop and streaming data. McGraw-Hill Osborne Media.

- Davenport, T. H. (2014). Big data at work: Dispelling the myths, uncovering the opportunities. Harvard Business Review Press.