Transforming your workforce into citizen data scientists

December 13, 2021

“Citizen data scientist” is a term coined by Gartner® to describe “a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.”

For many years, Big Data initiatives have been out of reach to small and mid-tier financial services companies because of the high cost and low supply of resources, the uncertainty of success relative to ROI and the opportunity cost of not pursuing other projects. Expense factors also multiply quickly given the average data scientist salary of $117,212 per year. There is also a well-known shortage of available resources pushing that salary expense even higher, especially when you require a specialist that also has financial services industry experience. However, in the absence of that business acumen a vernacular chasm forms between the business users and the data scientists that leads to communication misfires, multiple attempts to capture the true spirit of the requirements and, at the end of the day, underwhelming results.

The top cloud, analytics and enterprise resource planning (ERP) software providers have taken proactive steps to make data exponentially more accessible in an effort to raise historically low success rates of data-driven projects. Working with a partner who has constructed a purpose-driven ecosystem of these tools, including a business-oriented data dictionary and single sign-on, makes all the difference when it comes to empowering your current employee bench to become citizen data scientists and participate more efficiently in these projects, if not lead them in earnest.

Citizen data scientists can explore data in terms they understand, without switching between applications and tools. Moreover, your data can exist in a single data lake behind security, governance and ETL layers to ensure that it is consistent across functional silos. Discovering proportional and inverse relationships between operational variables is the first step to uncovering correlation and causation between how you are running your business and how your customers are responding. There is no universal remedy, as each use case is unique, and it may take multiple attempts, just like when a business user works with a professional data scientist, but the driving benefit is that these attempts are fast to fail as well as cheap and easy to restart when existing employees are behind the wheel.

In a time when increasing disruption and competition are forcing you to make decisions even faster, it is more critical than ever to be able to understand the impacts of those moves. Your transformed workforce of citizen data scientists can leverage business intelligence (BI) tools to get answers immediately at a level of depth and analysis previously reserved for uber-sophisticated, top-tier providers, allowing you to outmaneuver those coming after your customer base.

In other words, citizen data scientists who are subject matter experts in your business and customers can help you compete not only more effectively but ultimately more proactively as their expertise with the data and the BI ecosystem grows. As they say, the best defense is a good offense.

About the Author
Kimberly Zyndorf, Data Solutions Group, FIS
Kimberly ZyndorfData Solutions Group, FIS

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