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Data reusability: The next step in the evolution of analytics

Data reusability will lessen the response time to emerging opportunities and risks, allowing organisations to remain competitive in the digital economies of the future.

If data’s meaning can be defined across an enterprise, the insights that can be derived from it expand exponentially
When financial institutions work together to identify useful data analytics solutions they can produce great results and add a lot of value to their customers
The analytic systems of tomorrow should be able to take the same data set and process them without modifying them
If data is the new oil, then many of the analytical tools being used to value data require their own specific grade of gasoline, akin to needing to drive to a particular gas station with a specific grade of gasoline with only one such gas station within a 500-mile radius. It sounds completely ridiculous and unsustainable, but that is how many analytical tools are set up today.

Many organisations have data sets that can be used with a myriad of analytical tools. Financial institutions, for example, can use their customer data as an input to determine client profitability, credit risk, anti-money laundering compliance, or fraud risk. However, the current paradigm for many analytical tools requires that the data to be used must conform to a specific model in order to work. That is often like trying to fit a square peg in a round hole, and there are operational costs associated with maintaining each custom-built tunnel of information.

The advent of big data has opened up a whole host of possibilities in the analytics space. By distributing workloads across a network of computers, complex computations can be performed on numerous data at a very fast pace. For information-rich and regulatory-burdened organisations such as financial institutions, this has value, but it doesn’t address the wasteful costs associated with inflexible analytic systems.

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