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Executive Summary
As the costs of storing progressively larger datasets have gone down, and the amount of data managed within financial institutions has increased, applications that would have been impossible or prohibitively expensive are now within the realm of real possibility. For example, a scenario wherein withdrawal patterns signal that a client may be about to leave an institution can trigger a real time response, thanks to analytics working in conjunction with customer service teams.
Standing in the way of a more vivid picture painted by big data are the issues of siloed databases and non-standardized data markers. Legacy systems created before the advent of big data must be brought up to date with an eye towards compliance, as well as creating the means to turn unstructured data into a source of insights through analytics.
Increasingly, data will be viewed at a higher level, with multiple databases creating more informed analysis. The benefits of this include an easier time with compliance, increased data quality, improved automation processes, and greater risk reduction overall. Ultimately, the changes required to bring this standardization about will come from the top down. A clear mission to create a standardized data management strategy is a prerequisite for competitiveness within the currently developing big data paradigm.