Reviewed by Sep 30, 2020| Updated on
Big data is large and diverse information sets that increase at sustainable growth rates. It embraces the volume of information and the velocity or speed at which it is created and collected. It includes a variety or scope of the data points being covered. Big data takes multiple formats and constitutes of multiple sources.
Generally, big data can be classified as unstructured or structured. Structured data contains information previously managed by the organisation in databases and spreadsheets. It is often numeric in nature. Unstructured data refers to information that is unorganised and does not fit into a pre-determined format. It also consists of data collated from social media sources, which assists institutions in gathering information on customer needs.
It can also be characterised using the following classes:
Big data can be obtained from publicly-shared comments on social media and websites. It can be voluntarily gathered from electronics and apps, by questionnaires, product purchases, and electronic check-ins. Using sensors and other inputs in smart devices will allow data to be gathered across a wide spectrum of situations and circumstances.
Big data is usually stored in computer databases and is analysed using software particularly designed to handle large and complex data sets. Several Software-as-a-Service (SaaS) companies specialise in managing this kind of complex data.
To determine whether a correlation exists, the data analysts check out the relationship between various types of data, such as purchase history and demographic data.
Such verifications may be done internally within a company or outside by a third-party who centres around the processing of big data into digestible formats. Businesses regularly use the assessment of big data done by such experts to translate it into actionable information.
Almost every department in a company can utilise findings from data analysis, ranging from human resources and technology to marketing and sales. The objective of big data is to raise the speed of introducing products into the market. In turn, it reduces the time and resources needed to gain market adoption and wins the target audiences along with an assurance that customers remain satisfied.
In India, there are a lot of sectors that are being benefited through big data applications. Mostly, the top sectors are government, fraud detection, agriculture, marketing, and healthcare. Privacy is a big concern for handling the customer data, especially in BFSI industry.
Big data analytics have the potential to disclose sensitive personal information by tapping hidden connections between pieces of seemingly unrelated data. As per several research studies by experts, approximately 62% of bankers avoid using big data applications, due to privacy issues.
Outsourcing of data analysis deepens the security risks as information such as the customers’ earnings, savings, mortgages, and insurance policies are to be shared. The persisting concern about these data ending up in the wrong hands discourages customers from sharing their personal information in return for customised offers.