Creating and managing a data-driven culture at a financial institution is no easy task. Even the most seasoned analytics leaders will tell you that the analytics path is never straight and often bumpy. And while every credit union and bank is different in their structure, needs, goals, and tech stack, there are several common data analytics challenges we see in the financial services industry.
In this article, we’ll share what data-driven hurdles we are seeing in the industry to help you better recognize (and prepare) for them. Let’s dive in.
1. Operational Alignment and Clear Goals
One of the most common challenges we see credit unions and community banks face when managing their data analytics program is a lack of operational alignment across departments. Combined with a lack of clear goals, which often sits hand-in-hand with operational alignment, a two-pronged challenge emerges that creates confusion, redundancy, and low morale.
These issues usually rear their ugly heads around strategic planning season, when strategic goals are top of mind for leadership. Suddenly, it seems like data isn’t able to provide answers to big questions: Is the credit union growing? Did we achieve X goal from last year? Is our loan portfolio strong? We have a data program, but why aren’t we able to answer these strategic questions? So often we see the problem stemming from inconsistencies across various departments’ understanding of goals, how they are measured, and what is expected of them.
Data teams feel the pressure of this issue with overwhelmed request queues, difficulty prioritizing requests, and excessive infrastructure growth that later becomes a burden to manage. Too many requests and not enough awareness creates operational challenges that can be surprisingly pesky.
2. Sufficient Tech Stack
Existing credit union and bank technologies often struggle with specific tech stack limitations, including:
- Scalability issues, like data purging
- Limited reporting, such as specific fields only available
- High latency, such as time spent retrieving analysis
- Significant costs, like server upgrades
- Lack of transparency, also known as the “black box” effect
Even in the absence of the above issues, a great tech stack can be limited by the fluency of the data team. Credit unions and banks should invest time and resources into growing their human capital to be able to leverage a powerful tech stack. This can include online training, in-person events, personal development time, mentorship, or partnership for training purposes.
The key to solving this pain point lies in 1) the critical and careful selection of a powerful, scalable, tech stack, and 2) have all teams live, eat, breath everything there is to know about the given stack.
3. Ownership of Data Quality
Data quality is a common issue because it only becomes apparent in the final analysis. While it would be great if data teams could “solve” the issue, the vast majority of data cleanliness is managed at the input, not the output. Data teams can be responsible to provide exception reporting, but the business teams need to accept ownership and therefore accountability to the completeness, accuracy, and quality of the data they enter.
Leaders within their departments should expect data quality from their teams in the same way they manage other types of audit exceptions. Actions that credit union and bank leaders can choose to take include:
- Lobbying for awareness and educating teams
- Holding teams accountable data quality
- Supporting processes, like data cleanup initiatives, that close the gaps that naturally occur through human error or erroneous business logic
We hope you found this article useful in recognizing and preparing for the common data analytics challenges that credit unions and banks face. With clients ranging from $300M to $10B in assets, we have experienced first-hand the issues that teams face in their data journey. Here at Gemineye, we’re create resources like this article to empower credit unions and community banks of all sizes to become data-driven through education.
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