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Meaning and Motivation: The Role of Data-Driven Analytics in Financial Institutions

By: Tara Kenyon, CEO, Kentara Analytics

Tara Kenyon, CEO, Kentara Analytics

In Greek mythology, Sisyphus was a deceitful king who was punished for his malicious craftiness and treachery in the afterlife by being made to roll a huge boulder up a hill. The boulder would roll away from him before he reached the top, sentencing him to an eternity of frustration and uselessness. For financial institutions, new regulations and changes in the way business is conducted can appear to be “Sisyphean”—seemingly pointless or endless tasks and projects. But what if they are not meaningless at all?

What if those “pointless” tasks are iterative and meaningful? Financial institutions are made up of people. When those people feel that there is little or no meaning in their tasks, they fall into a “Sisyphean condition.” On the other hand, when people perceive that their work has meaning, they are motivated to work harder.

Here is where analytics can help financial institutions. Data-driven analytics provide meaning and in doing so, they motivate employees to give their best to their institutions. And motivated employees are alert to those things that might otherwise go unnoticed—such as attempts to defraud organizations as well as how to spot opportunities to better the lives of their customers. Overall, advanced data-driven analytics should:

• Enhance decision-making and risk management,

• Shine light on possible opportunities for growth, and

• Boost organizational performance.

Data can be both blessing and curse. Curse, because data oftentimes contribute to the Sisyphean condition—that is, without form or reason, they can be meaningless noise. Blessing, in that it can provide remarkable insight into the business of financial services. As British economist and Nobel Laurate Ronald Coase once said: “If you torture the data long enough, it will confess.” Compliance and direction change do not have to be viewed as necessary evils on a never-ending journey. You can use data-driven analytics for planning, decision-making, and improved performance: Plan, Predict, Perform.

By following data-driven analytics best practices (see upcoming FinTech issue), financial institutions can provide rich context for their data and minimize the effects of meaninglessness. Analytical decision-making allows bank management to make important business decisions with solid data to reap the benefits of deliberate and thoughtful approaches to strategy and performance.

In the iterative process of Plan-Predict-Perform, banks circle back to the Plan part of the analytics process at the end of the strategic period and start a new. We benefit from the wisdom acquired in the previous iteration so that we can improve in the next. Our efforts have not been Sisyphean after all.

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