The Cost of Bad Data

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Good data is essential for businesses, especially credit unions. But what many credit unions need to realize is that bad data can come with a hefty price tag. From decreased operational efficiency to missed opportunities to grow your business, the cost of bad data is real and often underestimated.

To help you understand these costs and how they can affect your credit union, we’ll examine the cost of bad data quality, how to identify it, and how you can prevent it from happening in the first place.

Data Quality: Why Bad Data is So Bad

We’ve featured bad data insights previously on the IMS blog in a past article about the ways bad data can harm your credit union. But the world is increasingly data-driven, and businesses are more reliant on intelligent data analytics, backups, recovery, and targeted solutions than ever before. And the amount of data being processed by financial institutions continues to grow in volume every year.

Improving data quality is a concern for businesses across all industries, and businesses in the financial sector are often under higher scrutiny than others. Because of this, keeping accurate data banks is so important for compliance items and for the reputation of your brand.

Bad Data Main Causes

You can’t escape bad data, you can only minimize it. And that means recognizing the causes of poor-quality data.

One of the most common causes is incorrect and incomplete data from members and/or employees. Miss one number in a member’s address on their loan application – that’s bad data. And that bad data can live on in a system for years if it’s not caught and corrected. Everyone knows the prevalence of the typo – in fact, if your surname isn’t one of the common ones in the USA like Smith, Johnson, or Williams, we can all but guarantee you’ve gotten one medical bill, marketing email, or other official correspondence with your name spelled wrong. Even when people are copying data or collecting information slowly and deliberately, missed keystrokes happen.

This is also true with incomplete data. There’s a reason why almost every form you fill out online has the little “*” next to items that are required, and every missed box triggers a “you missed something” notification that prevents you from even moving forward with the form submission. These are commonplace now because it’s a great and proactive way to make sure the bad data doesn’t come from someone submitting an incomplete document or request.

Bad data is also easily propagated through poorly maintained vendor and third-party files. This data is vital in helping your credit union learn more about financial trends across organizations and understand more about the current markets and business climate. But it also comes with the added knowledge that everyone stores and manages their data a bit differently.

This point leads us to another big cause of bad data – lack of standardization. Because handling and managing such massive amounts of electronic data is fairly new, especially for credit unions, there’s a lot of room for error in execution. And when your data is keeping track of people, it’s easy to create duplicate entries for the same person.

Think about it – even just one woman can be in your system several times – once with her maiden name, once with her maiden name and middle initial, once with her married name, and on and on it goes.

This Credit Union Times article goes into more detail about some of the most common causes of bad data.

The Cost of Bad Data for Your CU

Bad data management is a lot like learning how to drive a car, but never getting inside one. You can understand the value and benefits that knowing how to drive can give you, but you can’t reap any of those benefits, and if you don’t interact regularly with the car, you can’t improve your driving skills.

The same is true with data – if it’s not accessible, it’s not useful. Having all the data and not using it (or not investing in high data quality measures) is the same as not having it, in many cases.

When it comes to the monetary value of bad data costs, the figures are in the trillions, and that’s just for certain singular business ventures. Recently, IBM reported a loss of $3 billion due to bad data, and Gartner reports that poor data quality costs organizations $12.9 million on average.

And because bad data means a loss of trust in your credit union, the monetary losses are compounded by the decline in member numbers. And because banking services are necessary for nearly all facets of work and life, those members then turn to big banks and fintechs to house and store their financial and personal data. And younger generations aren’t messing around with their brand loyalty – they are more than willing to leave banks that break their trust.

Discover, Classify, Report: Positive Data Management

Bad data come in all shapes and sizes. Without a quality data management strategy, your credit union could fall

down a rabbit hole of data corruption, misplacement, and ultimately, member dissatisfaction.

To help combat bad data practices, IMS has an amazing SaaS application that uses machine learning to discover, classify, and report on sensitive data without impacting your day-to-day. We do this through our Polaris Sonar compliance technology.

With Polaris Sonar, you can use machine learning to automate processes and policies, identify exposures of sensitive data, and stay in compliance with all applicable privacy laws while mitigating the cost of bad data.

Reach out to us today to learn more about this and other IMS tools that are expertly tailored to promote higher data quality in your credit union.

 

Data Quality for Credit Unions: Best Practices

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Data quality for credit unions is an essential component of effective CU management. Keeping accurate and up-to-date records, such as member information, enables credit unions to provide the best services to their members.

To ensure data quality, it is important for credit unions to employ best practices in data handling and regularly review existing procedures.

Let’s discuss the various data quality best practices that credit unions can adopt in order to remain competitive and be prepared for future growth.

Keep Your Small Data a Priority

Many of the biggest issues credit unions and other financial institutions run into when it comes to data quality and management stem from the small data.

Addresses, zip codes, name changes – these member data items are considered minute details until they become inaccurate. Maintaining accurate addresses is a great way to ensure your credit union communications don’t get sent to the wrong people. Anyone who has sent invitations via snail mail – for weddings, anniversaries, birthday parties, and other formal events – knows that even if you are only planning to send the invitations out two or three months in advance, there will be at least one address change on your list.

Because more of your members are renting than ever before, it’s important to request periodic confirmation of names and addresses so you can make sure you are reaching as many of your members as possible in an accurate and helpful way.

One way to ensure you are capturing the most accurate information and storing it correctly is to have systematic and periodic backups of your credit union data.

Define Your Goals

There are hundreds of ways to improve data quality at your credit union. But it’s important to start your process by defining the goals you hope to achieve within this effort.

What is the main issue you are trying to solve? Do you want to be able to update data faster? Are you trying to cull unnecessary data from your systems? Organize your data so it’s more accessible?

While all of these goals are worthy of your CU’s time and resources, you have to start somewhere. Many credit unions fall into this trap where they are overwhelmed by their current data and the vastness of their spread-out systems, and they want to start with a clean slate. But you’ll want to start working through those systems in manageable chunks, which then allows you to gain some momentum as you are choosing your new data solutions and crafting a future operation plan to maintain that data quality.

Track Your Data Quality Strategy

When professionals create strategies aimed at improving data quality for credit unions, it’s important to stick to that strategy and evolve as it evolves.

There is no end to data quality work. Much like there is no moment when you can stop optimizing your cybersecurity protocols and infrastructure, your data needs are constantly evolving.

And the only way to keep track of that evolution is to create a living, breathing document that tracks your strategies and performance, and allows for internal review and feedback. Of course, your first data quality effort may be massive, in scope and in project hours. But your periodic maintenance of that new data integrity must be maintained, and the best way to do that is to track your progress even after your initial undertaking is complete.

Work in Layers

You offer your credit union members a whole host of services, from in-person classes and financial counseling to online solutions, mobile banking apps, and more.

If you look at each of your services and features, you’ll often see linear member data trends based on usage, demographics, and more. We mentioned above that working in manageable chunks is always helpful. The same is true when you are trying to improve data quality.

You can use things like gender, age, economic status, location, and more to tailor your data efforts by building a strategy that captures and stores the data you need within specific parameters. Those parameters can be based on your efforts and services, rather than just being thrown together in a massive data catch-all location, and then working backwards to pull out the specifics you need for each service or effort.

Start with Personalization

Data is how you figure out what your members’ needs, behaviors, and preferences are. You can use your member relationships to initiate and expand on the personalized experiences you want each member to have when they work with you and your team.

By creating a data-driven culture with personalization at the heart of your operations, you can tailor each interaction with your member to make it memorable and efficient, without losing any of that stellar customer service quality that credit unions are known for.

IMS – Ensuring Data Quality for Credit Unions

Data quality will continue to be a hot topic in the credit union industry as we transition more and more operations to digital locations. And in a world full of increasing data storage and consumption, you’ll want the best tools to help you archive, restore, discover, and protect your members’ precious information.

That’s why IMS has a whole host of private cloud services that were created by professionals for the credit union industry specifically, including:

Reach out to us today and let us help you learn more about data quality for credit unions. 

4 Ways Bad Data Can Harm Your CU

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Not all data is created equal. Just because you are collecting mountains of data, it doesn’t necessarily mean you have good data. In fact, it’s much easier to collect and use bad data.

What is Bad Data?

Bad data can be a host of things. It can be incorrect or outdated information. It can include incomplete or partial information that creates an incorrect picture of a member’s needs or preferences. The difference between good and bad is often subtle, and having the correct tools to analyze and categorize this data can help your credit union make better decisions, both for you and your members.

Here are some ways bad data can harm your credit union.

Bad Data Can Breed Distrust

Bad data can create redundancies and incorrect outcomes in your credit union team’s workflow. For example, if you have incomplete data that is passed on to third-party vendors, like collections agencies, those vendors will treat every account the same, even if some of them aren’t actually past due on their payments.

If your members, who are current on all loan or mortgage payments, receive notices from your third-party collections vendors saying they are past due, this could create distrust between you and your members.

It Affects Your Lending Ability and Reputation

Lending is your credit union’s primary source of revenue, and keeping the program strong often comes down to how accurate and timely your data is.

CUManagement talks a lot about the integrity of data. They use the example of a credit card interest rate: if your member’s interest rate on their credit card goes up due to late payments, it should also come back down if their payments start coming on time. But if you don’t have a solid system of checking these rates and what affects them, this can upset your members and also go as far as pushing you out of compliance with certain rules and regulations.

It Affects Your Ability to Stay Compliant

If your data isn’t properly organized and assessed, it can decrease your credit union’s ability to stay compliant. And that non-compliance can affect your credit union’s revenue streams, as well as its reputation and bottom line. And trying to set your data right after years of mismanagement will be a long and expensive process.

It Also Affects Your Marketing Success

Data has helped revolutionize marketing, especially when used correctly. You or someone you know has likely said this in the past several years: “Sometimes, I think my phone (or computer) can hear me think. Just the other day, I was thinking about how I’d love to buy (insert product here), and then today I see an ad for it on my Facebook page.”

Intuitive data collection and utilization can be a game-changer for your credit union, but it can also cause problems if you’re working with bad data. You could send emails to people with the wrong name or other personal information, or you could target the wrong potential customers for a new service you are rolling out. All of that decreases your brand’s reputation and costs you money.

Keep Your Data Safe and Up-to-Date

IMS offers virtual desktop and backup services to help you keep your data in check no matter how many of your employees work from the office, home, or somewhere in between.

Contact IMS for more information.