Every day I read more and more about data analysis and its importance in various industries. New vendors and new product offerings are hitting the market constantly. What I do not hear enough about though is why a business should get involved with data analysis in the first place. If you are leading a team or leading a firm, your investors (actual investors, members, board members, etc.) will undoubtedly ask about any data analysis that was performed in order to help them make decisions. There are three reasons why every credit union should be considering diving into data analysis.
1) Create improvement and modify processes
Without a suggestion for process modification or improvement creation, data analysis may seem like a waste of time. If the analyst who is reviewing data does not suggest a change, what was the point of the analysis to begin with? Perhaps the answer is that the analysis provided the exact details that management was looking for, but I personally doubt that a true analyst can analyze data and not provide any suggestions for process modification.
For example, if a credit union were to analyze their loan data and find that there is missing collateral information, the suggestion for process modification might simply be that a second set of eyes review all booked loans to ensure that the collateral record has been updated successfully. Data can often look like a slice of swiss cheese because there are many individuals who interact with and create data through our everyday processes. So, one of the easiest suggestions for process modification is to establish steps, processes, and routines that limit the number of holes within the data.
An analyst must ultimately have a knowledge of the data, but also how that data is created/maintained to ensure that the suggestions for improvements and process modifications are consistent with the data creation process. Many vendors may focus on data storage and normalization, whereas when a credit union works with CU*Answers and the Asterisk Intelligence team, many of those decisions have already been made during the software development life cycle.
Other examples of suggestions for improvements, or modifying process include:
- Analyzing employee performance for cross sales efforts and suggesting reassigning sales leads to employees who tend to close more sales.
- Analyzing the amount of debit card transactions per checking product and suggesting selling a specific type of checking product because those members tend to create more interchange income for the credit union.
- Analyzing written off (or charged off…) loans over the past year and determining what the Debt-to-Income ratio was for those members when the loan was first disbursed, and then using that information to suggest alterations to the risk-based-pricing strategies of the credit union.
A true analyst can almost always review a set of data and make a suggestion. If you are working with a data analyst and there is a limited amount of suggestions provided, I would encourage you to ask your data analyst for a list of suggestions.
2) Renew commitment to existing strategies
Imagine a CEO of a credit union who is looking to validate their decisions for their Courtesy Pay (Automated Non-Returns) program. This CEO may hire a data analyst to review and analyze their data to ensure that their statistics are consistent with other credit unions within 10% of their asset or membership size. But the CEO may simply be tasking the data analyst with the goal of affirming their operations. They are simply interested in the insight that helps them to defend their position and to gain grasp of their data, without having to act on it.
Grasp of your firm’s data is one of the most important elements of an effective leader. Having grasp of the data helps the individual defend their positions, defend their decisions, and defend their management style.
You might also consider this reason for data analysis as a means to confirm or deny previous decisions that were made. This could be the basis for proof to an auditor, examiner, or even their board of directors. This strategy in and of itself will lend itself to commitment towards a decision that was made in the past. This will motivate leaders and staff to commit to existing strategies because they have already been proved successful.
Examples of why a credit union may wish to renew their spirit for their activities (commit to their existing strategies) include:
- Analyzing peer data for share/loan/CD rate information and validating that their operational decisions for rates are consistent with the marketplace.
- Analyzing the amount of non-interest income for checking accounts and reviewing the amount of NSF income created because the CEO made the decision to lower NSF fees at the beginning of the year (and this CEO told their board that they would not have a drastic reduction in NSF income).
- Analyzing budget variance figures to validate that assumptions made at the beginning of the year are still true.
- Analyzing members who are opting into a new service and the revenue generated for that service. This would validate that the CEO (or leader) had made the correct decision for providing a new service to the membership.
3) Reconsidering financial model
The desire to shift to a new strategy almost always requires data analysis. Imagine that you work for a department and you want another employee due to the existing workload. Undoubtedly, the CEO of your firm is going to be interested in an analysis as to why the need exists. A natural response may include the number of product and service deployments that are taking place on a daily, weekly, or monthly basis, a projection for future sales to customers, an increase in the number of potential customers (which would therefore increase the potential for sales), etc. All of these details help a leader decide whether funds should be allocated to that department for a new employee.
But staffing is just the tip of the iceberg when it comes to reconsidering the financial model of a firm or shifting to a new strategy. A CEO may be interested in shifting to use a new vendor because costs are lower. A CEO may be interested in altering offline limits because they have a hunch that they can create more non-interest income or provide members a benefit in less than desirable situations.
This style of analysis is often performed during the budgeting season for a credit union. This is the time of year where credit unions explore new initiatives, plan for new products and services, and then make decisions on what they eventually propose to their investors and board members.
It may seem like 2020 would be the perfect year for a CEO or a leader to reconsider their financial model, simply out of necessity. Members were forced to act differently, and that causes CEOs and leaders to think differently. For example, if your credit union had their lobby doors shut at some point this year and members were forced to interact with staff online, over the phone, or through your drive-thru, there may be some new considerations for future investments. Perhaps an upgrade to drive-thru techniques is reasonable. Maybe the design of a new branch needs to be altered for fewer in-person interactions and more drive-thru interactions.
Examples of why a firm should re-consider their financial model include:
- Analyzing historical credit card transaction details to determine what the financial impact would have occurred had a Credit Card Cash Back configuration been in production. This would allow the CEO to make a suggestion for a new service offering to their team and board of directors.
- Analyzing historical checking account data to determine the financial impact had a Qualified Dividends configuration been in production. This would allow the CEO to propose a new set of configurations for their checking account strategies.
- Analyzing the amount of income that would have been earned by a credit union had they been more risky and approved loans that were otherwise denied. This would allow the CEO to suggest changes to their loan policies for future lending decisions.
Got a data analyst yet?
Regardless of the reason for a credit union or firm to pursue data analysis, it is encouraged that there be a stake thrown into the ground for the reason why the analysis is occurring. This is helpful for the credit union as well as the data analyst as this provides the overall context to the study. Recently, a colleague completed research for a credit union who was curious why their dormancy fee income has decreased over time. The first question that was asked of the credit union was why they were interested. Simply asking “why” is often all it takes to have a thought-provoking conversation on why the interest exists.