Big Data

Data is everywhere today. All the institutes, organizations, companies, websites, social media, and everything handle data, manipulate data, and generate data. For example, a social media giant like Facebook generates more than five hundred terabytes of data per day. Likewise, a jet engine, a stock exchange, or any other significant entity must deal with large amounts of data each day, thus the term ‘Big Data.’

Big Data refers to a massive amount of data, too complex to be analyzed using simple traditional methods, growing exponentially with time. Leveraging these massive datasets would benefit in solving the business problems and finding more fruitful insights.

There are three kinds of big data, namely structured, unstructured and semi-structured. Structured Data can be stored, accessed, and processed in a fixed format. For instance, Data stored in a relational database management system is an example of structured data. On the other hand, any data is unstructured if it is in an unfamiliar form or structure. Unstructured data has its challenges as, despite being huge in volume, the processing can be complex, and arriving at insights will be an arduous task. A combination of data in different file formats is a simple example of unstructured data. Semi-structured data contains both kinds of data, but such a case is usually referred to as unstructured data itself. Big data is defined generally using four characteristics viz., volume, velocity, variety, and variability.

Characteristics of Big Data

Volume

Companies collect data from multiple sources such as smart devices, social media, application software, etc. This piles up vast amounts of data. Therefore, big data will always have high levels of unstructured data consisting of unknown values and parameters in terabytes or petabytes. Thus size or volume of data is a critical factor in deriving insights out of data, as the purpose of data science stands.

Velocity

Velocity points out the speed at which data is generated, received, and acted upon. The swiftness in which the data generation takes place and organization-specific demands are met is crucial in big data analysis. The evolution of the Internet of things, i.e., IoT, has led to an enormous speed of data generation, and handling this incoming data quickly and efficiently is a must. Thus, real-time evaluation and action gain importance in big data analysis.

Variety

The massive amount of data, i.e., the big data collected, need not be homogeneous. On the contrary, it can consist of several kinds of data collected from heterogeneous sources. Leveraging traditional data was done with the help of relational databases and spreadsheets, which were the only sources of data accepted by many applications. However, with the upsurge of big data and the encounter of unstructured data types, such as audio, video, texts, PDFs, emails, more storage mining and data analysis challenges rose, demanding additional preprocessing.

Variability

Variability is the inconsistency that can arise from big data, hindering the effective handling and management of data.

Importance of Big Data

Big data is highly significant considering its volume, velocity, variety, and variability, and more importantly, the various prospects that the analysis and manipulation of big data that it offers. Furthermore, efficient usage and analysis of big data and its solution can help companies strategically and optimize their resources and assets. As a result, big data analytics has several advantages in its kitty.

Hadoop and cloud-based analytics, the big data technologies, can fetch notable cost optimizations and advantages with the help of their big data storage facilities and identify more resourceful ways of carrying out business. Also, the swift Hadoop and in-memory analytics, along with the capability to analyze data from heterogeneous sources, facilitate organizations to analyze information more effectively, resulting in faster decision-making. In addition, big data analytics can also help in gauging customer interests leading to more customer satisfaction.

Companies need to scrutinize market conditions, which is possible with big data analysis. For instance, customer purchase behavior analysis can give insightful prospects for an e-commerce company, which would keep it ahead of competitors. Moreover, sentiment analysis, if appropriately employed in social media with the help of big data tools, the organizations can assert their presence more effectively, thereby increasing profit prospects.

Conclusion

Thus, it has been ascertained that big data analysis plays a huge role in today’s data-driven world. However, though it holds numerous promises, big data also comes with its own set of challenges. This article throws light on giving an overview of big data and stressing its importance in the contemporary world.