What is Big Data?
Before you understand what is big data and why Big Data Analytics is important for a business, it is important to know about ‘data’.
The term data refers to characters, quantities, or symbols that are processed by a computer which can be stored and referred to in the form of electronic signals. These are recorded on optical, magnetic, or mechanical recording devices. Now let us see what is big data.
Big Data Definition
Big data is a collection of a huge volume of data, structured and unstructured, that overwhelms a business on a day-to-day basis. But it is not the quantity of data that is crucial. It’s what businesses do with the data that matters the most. Big data can be scrutinized for perceptions that can lead to good quality decisions and strategic business actions. The big data definition remains the same irrespective of industry. However, the nature of big data differs. Let us see the Big data definition that is universally applicable.
Gartner defines big data as below:
“Big data is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”
Generally, Big Data captured tends to grow gradually over a period as the business continues adding more data frequently.
The high volume of data stored in electronic devices may often be complicated to interpret and make use of it. None of the traditional data storage devices can interpret the data produced and stored by the modern world business entities.
Importance of Big Data
Now let me explain why Big Data Analytics is getting traction in the business world. Big data is not just a storage of voluminous data. It is introduced to solve multifaceted problems faced by business organizations. Fundamentally, the significance of big data never revolves around how much data you have gathered about your business. It is mostly to do with how you make use of big data to solve your business problems. Here is a shortlist of points that indicate the significance of Big Data Analytics.
- Study the behaviour of your suppliers, customers, creditors, borrowers, and other stakeholders to your business.
- Understand the needs and preferences of your customers
- Big data is useful to trace the root cause for your failures and setbacks
- Detect the fraudulent activities of your stakeholders beforehand
- Business expenditure can be reduced by using big data analytics
- The efficiency of the business process and people can be enhanced by using big data analytics
- Business can earn a competitive edge
- Business can face competitors more effectively
- Customer brand loyalty and acquisition of a new customer base is possible
- Right resources can be recruited using big data
Classification of Big Data
Big data can be found in three major formats:
1) Structured Big Data
Structured big data is processed, organized, and stored in a systematic and logical order. The data will be ready for the consumption of systems for pre-defined requirements. The business process and store the data considering requirements for future use. For example, employee data in a table is the best example of structured data. A sample has been shared below:
|Employee Code||Employee Name||Gender||Division||Remuneration in Lakhs|
2) Unstructured Big Data
Unstructured data is a format of data that is derived from various activities undertaken in the business. Unstructured data will not be in the ready to consume format since it is not organized according to the needs of big data analytics. Unstructured data is heterogeneous in nature. For example, when the human resource department is asked to capture the potential data for the recruitment process, they may trace and store textual resumes, video resumes, audio resumes and infographic resumes in a folder. However, the human resource department can not consume this data equally to take recruitment decisions.
3) Semi-Structured Data
Semi-structured data is partially processed data. However, the data is not reliable for your exact requirements since it is not fully processed to your requirement. For example, what you get when you search for something on google. You may not get exact data even when you try keywords. Let us assume you search the term ‘input’ on google. Google gives a recommendation based on what thinks the term ‘input’ must be.
Big Data Analytics in Banking
The notion “Big Data” is no more cramped to the realm of technology. It is expanding in diverse segments of the business world using some advanced statistical and mathematical models such as data mining, artificial intelligence, predictive analysis to gain new acumen ensuring superior and quicker business decisions. As the name itself indicates, it is a concept used to define a huge collection of data whose volume and sizes are beyond the ability of traditional databases to collect, manage, and analyze data with low dormancy. In this context, we can pose a question to ourselves “do banks and financial institutions have the potential to produce a huge volume of data?” The answer is, “Of course, Yes”. You must be curious to know about big data analytics in banking.
Banks record millions of business transactions daily and these entries are real-time in nature. The volume of data generated by banks is not just large but also real-time in nature. Nonetheless, capturing and recording such a huge chunk of data is a challenging job for bankers. Big data analytics help them by providing a platform where these transactions can be recorded systematically.
Structuring and recording the data is useless until and unless there is a plan to make use of such large data. Therefore, identifying the connection between the data captured and possible results is a puzzling task in today’s complex business world. The connections may be anything such as security and fraud detection, risk management, analysis of customer spending & investment patterns, compliance, financial reporting, market segmentation, and product customization, etc.
Decades ago, a typical bank customer would walk into a bank and be greeted by an executive who knew his name, his backgrounds, and how best to serve his personal banking needs. However, the situation changed. People often are engaged in multiple assignments and travel to different geographical locations. If one day he stays in New Delhi, the immediate next day he may have to visit Paris on his business assignments. In such conditions, it is challenging for a bank executive to track his personal preferences and whereabouts to meet his needs. Big data gives insight into many complex areas of an individual’s life including their lifestyle, needs, and preferences of their customers so that it is easy for banks to personalize services to the needs of each individual.
For a long time, the banks miserably failed to utilize the information generated by their own business. Big data has become a game-changer in transforming their business process and conducts to identify business opportunities and potential threats. Generally, banks and financial institutions find big data from sources such as log data, transactions, helplines, emails, social media, external feeds, sponsorship, audio, video, and some other sources.
The introduction of big data in banking has destroyed many ground rules of business and has transformed the landscape of the financial services industry. With a huge volume of data gushing from countless transactions, the banks are trying to find out innovative business ideas and risk management solutions. Each set of the data gathered over a period tells a unique story and shows the goalpost for a definite future period so that a business firm can capitalize on this information to attain a competitive edge in the market. Big data analytics can improve the extrapolative power of risk models used by banks and financial institutions. Big data can also be used in credit management to detect fraud signals and the same can be analyzed in real-time using artificial intelligence.
On a closing note, the banking and finance industry cannot perceive data analytics in isolation. Along with identifying business opportunities, they should identify security threats, the occurrence of fraud, and possible remedies. Further, they should attempt to connect big data across departmental and organizational silos. Many of the traditional banking entities in India have not yet begun their big data activities. The big banks have an edge in capitalizing on those opportunities. Therefore, big data science not only brings new insights to the banks, but it also enables them to stay a step ahead of the game with advanced technologies and analytical tools.
I hope now you are clear about what is big data, big data what is, big data is, big data analytics, big data analysis, big data definition, big data analytics in banking
Please feel free to share your thoughts in the comments section.
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