De-jargoned: Big data
It refers to streams of digital data that are too large for traditional data processing applications
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The term big data refers to streams of digital data that are too large for traditional data processing applications. Right from capturing the data to analysing it, from storing it to transferring it, all processes require special technique and attention.
What is it?
Big data is information in very large volumes. For example, years of transaction-based data from a bank, or even random comments from Internet sites. This data is available and stored but needs to be utilized in an effective manner or analysed by businesses to use for relevant growth. Big data does not refer to only one form of data; rather, it is gathered in many kinds of formats—videos, emails, Internet-linked digital data, financial data and even audio. Comprehensively trying to accumulate this data and make sense of it is the reference to big data.
Every second, millions of bytes of digital data gets stored. To be able to filter this equally fast in a logical manner is another purpose of big data analytics. More recently, analysing data from social media sites, such as reactions, emotions and so on, has picked up pace. While reactions in social media may be easy to understand and filter, there are other kinds of complex data, which, too, come in at a fast pace and in large quantities. Capital market transactions across currencies and assets happen simultaneously in many exchanges. To capture this complex data and filter it will be a part of big data analysis.
Why is it important?
As the world becomes more digitized, human actions and reactions get captured and stored through digital data. Every time you buy groceries from a supermarket, the entire shopping list is entered and stored in a computer. After a year, if you could analyse these, you would know your buying preferences in terms of brands, items and costs; you could tell the frequency with which you visit the store; your average spend each visit; and so on. Such information is useful for research as it indicates buying trends. Similar inferences can be drawn across industries, including financial services.
The challenge lies in managing the large volume of data in a timely and relevant manner. Add to this the endless streams of new information which gets incrementally captured in real time, and the task gets more difficult.
Large organizations realize the importance of big data analysis and are hiring managers specifically for this purpose. In financial services, for example, big data analysis can be used to map customer satisfaction levels and in understanding customer behaviour relative to various types of products and assets. Big data analysis can also give comprehensions for future trends and behaviour. Therefore, investing in big data analysis can help financial services organizations get a better grasp of customer orientation and be in a better position to structure products accordingly. But this will need technological advancements, which all organizations may not be able to afford or even understand. While tech companies are already focusing on this segment, it’s yet to catch on with traditional industries where it can be used profitably.