The Big Data technology has matured in the past few years so that it is possible to store and work with very large data sets. Indeed, the size of the data set has become a non-issue. Instead, enterprises are now wrestling with the up-front processes for collecting and cleansing data, and then with the analytics required to get new insights and value from the large data stores. Banks will discover that the size of the data is not as important as the value that can be obtained when fresh insights are provided by powerful Big Data analytics.
Big Data typically is comprised of historical data which may have some percentage of "noise" or varying data quality. When Big Data is be used to develop general models related to trends and relationships between typical customer attributes, the noise has little overall effect on the models. However, the Big Data suffers in several ways. First, the historical data is just focused on the past. It is like looking in the rear-view mirror of an automobile - you can only see what is behind you and not where you are currently or even what is coming towards you. This is because Big Data also works in generalities on large data sets, it does not provide specific insights about individual customers or their preferences.
The challenge with Big Data is having the knowledge of what information is valuable for a particular scenario. Or, if banks know exactly what they want, how do they obtain the information at the moment it is required? The answer is to speed Big Data up - Fast Data is processed immediately as new information arrives and this gives banks the edge over their competitors. The speed at which insights are gained and ultimately how they are put to use enable the Digital Enterprise to immediately respond to both opportunities and threats.
Fast Data is present in situations where the business value is increased by a faster response. This could be a short-lived opportunity such as a real-time offer, an electronic pricing decision, or an opportunity for an interaction with a partner or supplier. Fast Data is also present in situations that require a response within a short timeframe to avert a negative situation, such as fraud or a regulatory compliance issue.
Fast Data is specific to individual customers or business events. It is about the current status or situation for a specific customer. It is about a real-time contextual offer that is being tailored for a specific customer. It is about a potentially fraudulent transaction that is occurring right now. With Fast Data users are looking at, analyzing, and responding to each and every business event which is directly related to their business.
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