The Four Pillars of Big Data in Banking: Variety, Volume, Velocity and Value
Posted: Mon Jan 06, 2025 10:09 am
With the expression “ Big Data in banking ” we therefore indicate a specific reality of a much broader phenomenon: the set of all those activities that generate a digital footprint in the financial products market. Let us pause for a moment to examine in greater depth how the distinctive qualities of Big Data – variety , volume and speed – are expressed in the particular case of the banking sector and to introduce a fourth pillar, value , which concretely translates the data analysis and management operations into the business results of a company .
Big Data , literally “big flows of data” can be described through four key attributes: variety, volume, velocity and value.
Variety: The different types of data that are processed at any given israel whatsapp resource moment. In the case of banks , the data spans a wide range: from transaction details to credit scores to risk assessment reports and much more.
Volume: The space that data will take up to be stored. In the case of financial institutions , from international credit giants to the smallest local start-ups, the data generated and stored every day is quantifiable in the order of terabytes.
Velocity: The speed with which new data is added to the existing data in the database. We can perhaps have a minimal idea of the average speed if we think that banks today handle thousands of transactions daily.
Value . Value for banks is the real-time application of Big Data analytics results to business decisions.
Let's now try to contextualize, bringing the previous reflections back to the level of reality that we experience daily. Today, practically every time we come into contact with our bank, we interact with it through data-driven banking services .
Big Data , literally “big flows of data” can be described through four key attributes: variety, volume, velocity and value.
Variety: The different types of data that are processed at any given israel whatsapp resource moment. In the case of banks , the data spans a wide range: from transaction details to credit scores to risk assessment reports and much more.
Volume: The space that data will take up to be stored. In the case of financial institutions , from international credit giants to the smallest local start-ups, the data generated and stored every day is quantifiable in the order of terabytes.
Velocity: The speed with which new data is added to the existing data in the database. We can perhaps have a minimal idea of the average speed if we think that banks today handle thousands of transactions daily.
Value . Value for banks is the real-time application of Big Data analytics results to business decisions.
Let's now try to contextualize, bringing the previous reflections back to the level of reality that we experience daily. Today, practically every time we come into contact with our bank, we interact with it through data-driven banking services .