Depending on who you speak to, the definition of a data scientist seems to mean different things to different people. Some see it as a glorified number crunching role, others believe the position requires someone more inquisitive to spot and respond to key trends. The role can also be linked closely to the chief data officer (CDO), but this position is more about devising an enterprise-wide strategy to share the best data across the business.
The truth is that when people refer to the role of a data scientist, they essentially mean people who examine the interrelationships between diverse sets of data as well as the disparate systems, processes and locations which house them. Across certain sectors, such as retail, the role is very mature. For some time now, this has been a space that has mastered the art of using the right information at the right time. Amazon is the blueprint for this: by analyzing behavior across multiple accounts, it knows exactly when and why to push a certain product to a customer. However, it is a slightly different story in financial services, where the role is a little bit like an unformed puzzle: all the pieces are there, it just hasn’t been put together.
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One of the reasons for this unformed jigsaw is the inherent complexity of the industry, with so many different areas needing the position to fulfill specific tasks.
Take the retail banking industry. This is one area that has seen the data scientist excel. Big Data analytics is used across the industry from fraud and sanctions management to improving account management processes using enhanced customer insight. Ultimately, the analysis of Big Data provides the potential for banks to create new income streams. By analyzing each aspect of a customer’s buying behavior, banks develop an insight into behaviors that they may be able to monetize. For example, a payments company may decide to partner with a retailer to send discount offers to cardholders who use their cards in the vicinity of the retailer’s stores. But while retail banking is starting to reap the rewards, the sector as a whole – including the middle and back offices – has only scratched the surface when it comes to deriving value from the vast quantities of information at their disposal.
In order to link the pieces across the entire financial services space, there has to be someone within an institution that focuses on looking for the relationships between data across disparate sources. Did the price spike due to a corporate action, or did it fall because a rating dropped for an issuer? What is the effect of this drop to the bottom line? While all the required skill sets already exist across the sector to find the answers, the first step is to find a way to piece them all together.
Financial institutions have people with the skills to do modeling and statistical analysis, but this needs to be married with the skill set of someone who is able to spot key trends. At present, the two pieces aren’t coming together. Once they do, the final piece is ensuring that they have the right tools to mine through the different data sets. It is no good having the combined skills if the technology isn’t underpinning them. Only once the puzzle is fully formed, will we start to see the rise of the data scientist in across the wider financial sector, similar to what we have witnessed in other industries.
Dev Bhudia is VP of Product Management at GoldenSource.