The Cost of Bad Data


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


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. 

Ransomware Concerns: Why You Should Be Fixing Data Management Problems


Ransomware concerns are often categorized as cybersecurity issues rather than a result of data management problems. But there are emerging insights from the cybersecurity industry that underscore the importance of data management and other proactive technology programs and software in the fight against ransomware threats.

Here’s why you should be using data management to combat ransomware threats.

The Cost of a Ransomware Attack Is More Than Just the Ransom

Ransomware costs businesses more in the resulting downtime than it does in the ransom payment, according to TechCrunch.

Downtime causes a ripple effect that can be felt throughout your organization. From incident response measures to legal fees and support, not to mention the impact to customer experience, downtime caused by ransomware can get pricey fast.

Depending on the size of your business, that ransom amount can also be quite high. Your data management problems are only exacerbated by a successful ransomware attack.

Credit unions and other financial institutions are seeing a huge uptick in ransomware attacks, and that trend doesn’t look like it will slow down anytime soon.

IMS can help set you up for success and save up to 80% of primary storage costs, leaving you with more capital to run your business.

Good Data Management Is Always Learning

If your credit union were a smart house, data management is the technology that runs all your settings and cycles. Imagine your smart thermostat, for a moment. You can set it to learn how you manage your home’s temperature throughout the day. Pretty soon, the system will use these patterns to create a schedule that best fits your usage.

That’s how IMS’s machine learning works with your data. The software learns how your credit union employees and executives use data and where it travels, and it will notice when things go off track, like when someone tries to hack into your servers or gain access to sensitive data without authorization.

As your data management problems emerge, your IMS software can help you navigate to the most comprehensive solutions through compliance and data discovery tools.

Don’t Forget about the Big Picture

Your data is likely housed across physical servers, cloud systems, and other legacy processes. But because these fragmented processes can’t give you a good idea of how much data you have, or what it looks like in its entirety, you are setting your credit union up for failure.

To continue the house analogy: imagine you are at home, and a fire breaks out. When you submit your losses to your home insurance, you also submit photo and video evidence of your assets that were in the home – televisions, gaming systems, children’s toys, clothes, furniture, kitchen appliances, etc. Your insurance company then cuts you a check for your loss of those items.

If you don’t have this big picture view of your home, you may miss important things that were lost.

Your credit union data is the same. If you have no concept of the scope of your data, where it is, and how much exists, ransomware attackers can steal and corrupt files that are crucial to your credit union operations, but that you didn’t realize were missing until much later. Data management problems often require looking at specific data items and areas, but also need the context of your big picture data usage and storage to use as a framework or blueprint.

Visibility and pattern analysis (like those solutions offered through IMS Data Discovery) can show you what’s happening in every part of your data management system. Once you have this framework, you can see how your data is being used, who is using it, and when. And with machine learning, those patterns we talked about earlier – and any disruptions that happen within them – are easily traceable and visible.

The Role of Data Backup & Recovery

Preventing attacks should be at the top of the list of ransomware concerns. Much like driving a car or keeping your house locked, it’s easier to use defensive tactics to prevent issues, rather than to wait until the issues arise to address them. Even so, your credit union data is still likely going to be targeted by bad actors, and that means your reaction to an attack has to be as stronger or stronger than your attempts at prevention were.

Two important pieces of pulling yourself out of data management problems include data backup and disaster recovery.

Because ransomware often targets the data that will halt operations and cripple your credit union’s business, your data backups need to be housed safely, and at least one copy should – by best practice – be housed offsite.

And disaster recovery is your failsafe. Once the cybersecurity walls have been breached, disaster recovery is your most important next step.

Ransomware cripples people and businesses by leveraging the lifeblood of their operations – digital data. If you don’t set up ways to get that data back without going through the hackers, your credit union could face huge issues, up to and including the shutdown of your business operations.

Invest in the Best Credit Union Data Management Solutions

Data management problems require multi-faceted solutions. And IMS is your guide for cohesive and comprehensive data management strategies that fit your credit union.

For those looking to increase your effectiveness at preparing for and preventing ransomware incidents, IMS offers Anomaly Detection through a technology called Polaris Radar. You’ll be able to recover faster while increasing your system’s intelligence. See how your data moves and changes and let Polaris Radar use machine learning to detect and alert you of anomalous behavior. 

4 Ways Bad Data Can Harm Your CU


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.