Here, in this blog, we’re zooming in on a hidden villain that’s sabotaging businesses everywhere—bad data in enterprise applications.

Why? Because it creates a ripple effect that impacts the whole bottomline.

We’ll demonstrate how: A sales rep enters a customer’s contact details into your CRM, but an important field is left blank, or a single typo sneaks in. Okay, no big deal, right?

Now imagine this happens 20,000 times over just two months (the number strictly depends on how large or small your organization is).

And that’s all it takes—wrong entries, lost revenue, or wasted time—to clog your system, ruin campaign results, frustrate your sales team, and cost you customers.

Do you see how quickly this spirals out of control?

It’s no longer just a minor inconvenience. This is a business nightmare.

And here's the thing: you need to invest in data quality—there's no way around it. It’s a given. But the real question is how? Without fixing this, your goal of becoming a data-driven company is simply out of reach.

Let’s start by breaking down the real cost of bad data and why tackling it should be at the top of your priority list. Shall we?

What is the Cost of Bad Data?

Let’s talk numbers for a second. We’ve come across a few different sources that really put the cost of bad data into perspective—spoiler alert: it's expensive.

• Experian plc says bad data can cost companies up to 25% of their potential revenue. Imagine losing a quarter of your earnings just because of inaccurate information.
• Harvard Business Review reports that businesses lose a jaw-dropping $3.1 trillion annually because of bad data. That’s not just a few errors here and there; it’s a massive issue that’s costing the economy big time.
• Gartner found that organizations are facing an average loss of $15 million per year due to poor data quality.
• Also, according to the same study, 60% of companies don’t even know how much bad data is costing them because they don’t track it. That’s a problem in itself!

While specific figures may fluctuate, it's clear that the consequences of data inaccuracies can be high & severe, significantly impacting a company's bottom line.

Could It Get Worse? Unfortunately, Yes.

We’re not done yet. These figures are just the monetary costs. But bad data can also have a huge impact on your day-to-day operations, often in ways you don’t see immediately. Let’s talk about the hidden costs of bad data:

1/ Wasted Time on Fixing Errors
Analysts are spending half their time cleaning up the data they’ve already collected. As we move into an age dominated by Generative AI and Large Language Models, your teams are getting pulled into correcting errors instead of focusing on strategic work.

If you think about it, every minute spent fixing data mistakes is a minute that could’ve been spent growing your business.

2/ Resources Spent on Fixing Problems
It's not just time that gets lost. Fixing data errors also takes up valuable resources. Depending on the complexity of your data systems, the mean time to resolve issues can vary, but frequent errors can point to a deeper, systemic problem.

This just keeps getting more expensive day-by-day.

3/ Poor Business Decisions
The most dangerous cost of bad data? Bad decisions. When data is inaccurate, leaders make decisions based on faulty information—decisions that could affect everything from financial outcomes to customer satisfaction and overall market position.

When your business decisions rely on bad data, it creates a domino effect – one bad decision and that’s all it takes to lose to your competition, or worse, lose business!

4/ Reputation and Legal Risks
And then there are the intangible costs that can really hurt: financial reporting mistakes, mishandling customer data, and even compliance issues can lead to fines, legal battles, and a serious hit to your reputation.

This erodes trust—not just with customers but internally as well.

Let’s take a step back and break down what data quality really means. How do we figure out which data is good and which is bad?

What is Data Quality?

Data quality refers to the degree to which data is accurate, reliable, and suitable for its intended purpose. It’s the foundation upon which business decisions, strategies, and operations are built. If the data is solid, everything built on top of it stands strong.

What is Good Data Quality?

In simple terms, high-quality data is data that meets a few key standards:

• Accuracy: It reflects the data is precise & correct.

For example, if customer contact information in a CRM is accurate, sales reps can reach out to clients without unnecessary delays or confusion.

• Completeness: It contains all the necessary information.

In an inventory management system, missing details about stock quantities or item descriptions can create problems when fulfilling orders or tracking items.

• Validity: It conforms to predefined rules and standards.

For instance, in an HR system, making sure employees' birthdates or contact information are entered correctly helps avoid confusion or mistakes later on.

• Consistency: It is free from contradictions and inconsistencies.

In a financial reporting system, consistency means that all figures are aligned across different departments, so there’s no confusion about the numbers.

• Timely: It’s up to date.

In a sales tracking tool, having the most current information about customer preferences or product availability helps make better decisions quickly.

What is Poor Data Quality?

Here’s what bad data looks like:

Inaccurate Data:

• Typos or errors: Mistakes in spelling or formatting.
• Incorrect values: Data that doesn’t match expectations.
• Outdated information: Data that’s no longer relevant.

Incomplete Data:

• Missing values: Key information left out.
• Truncated data: Information cut off or shortened.

Irrelevant Data:

• Unnecessary information: Data that doesn’t serve the purpose.
• Redundant data: Duplicated information.

Inconsistent Data:

• Conflicting information: Data that contradicts itself.
• Data format discrepancies: Inconsistent formats across systems.

These all are add up and create a poor data quality in your enterprise applications.

How Bad Data Impacted a Company's Bottomline

In 2022, a prominent real-time 3D content platform experienced a catastrophic data quality failure. We’re not naming names, but here’s the story.

It started innocently enough—a small data discrepancy from a large customer. But that “small” issue turned into a $110 million disaster, derailing operations and shaking investor confidence to its core.

What went wrong?

• Inaccurate data infiltrated the platform's training sets
• Predictive machine learning algorithms became compromised
• Fundamental performance metrics began to deteriorate

The damage:

• Immediate Revenue Loss
• Model Rehabilitation Costs
• Feature rollouts put on hold indefinitely.
• A 37% drop in stock value.
• Severely affected Investor Confidence

What can we take away from this?

1. Data Integrity is Non-Negotiable
2. Algorithms are only as good as the data they’re fed.
3. Proactive Monitoring Prevents Catastrophic Failures

The harsh reality? Every piece of data matters. Behind every line of code or algorithm lies the power to drive your business transformation—or destruction.

What’s the solution? Focus on data quality like your business depends on it—because it does. Validate & monitor system data quality every step of the way.

Because, it’s not just about data. It’s about trust.

Now that we know what good and bad data looks like, it's time to focus on maintaining quality data. It’s about making sure that the data being entered and processed is validated correctly—so it doesn’t fall into the bad data category.

Manual vs. Automated Data Validation

There are two main methods to know about poor data quality: manual validation and automated validation. Let’s compare both to see how they help maintain clean, reliable data.

Manual Data Validation

What it is:
Involves human intervention to check and correct data. This could include reviewing records, comparing entries, and making adjustments by hand.

Pros:

• Control: The data quality professionals have complete oversight and can spot nuanced errors that might be missed by automation.
• Flexibility: They can adapt to complex or unique situations that need human judgment.

Cons:Reviewing large datasets manually can take hours, even days.

• Humans are more likely to overlook mistakes, especially with repetitive tasks.
• As your business grows and your data volume increases, manual validation becomes inefficient and unsustainable.

Automated Data Validation

What it is:
Uses software or algorithms to automatically check data against predefined rules, making sure it meets quality standards before being used.

Pros:

• Speed: Processes large volumes of data in a fraction of the time it would take manually.
• Accuracy: Reduces human errors by enforcing consistent validation rules.
• Scalability: Easily handles growing datasets without a drop in performance.
• Cost-effective: Saves labor costs and resources by eliminating the need for manual checks.

Cons:

• Initial setup: Setting up automated validation systems can take time and effort to configure.
• Limitations in complex cases: Some data nuances may require human intervention, which automated systems might not handle well.

Which is Better?

For most businesses, a combination of both methods works best. Automation can handle the bulk of validation, while humans can step in when needed for complex or exceptional cases.

How Digital Adoption Platforms(DAPs) Automate Data Quality Within Software Applications?

In a nutshell, DAPs are in-app tools that integrate with enterprise applications to guide users through work processes in real-time. They have walkthroughs, how-to guides, & more, helping users get the hang of complex systems & processes.

But, a DAP does more than just support onboarding and training—it actively helps maintain poor data quality across your systems.

Take Gyde, an AI-powered DAP, as an example.

Think of it as your employee’s digital tour guide. No matter the task your employees have to complete, Gyde will show them exactly:

→what to click,

→what data to enter, and

→why it’s important.

All of this happens in real-time, addressing the specific needs that arise while employees are actually working.

Think of a sales rep using a CRM like Salesforce—these features will pop up to help maintain data quality as they work:

1/ Walkthroughs - These are audio-visual callouts that walk users step-by-step through application processes, helping them complete them start to finish.

2/ Highlight Critical Step

During a walkthrough, Gyde can highlight critical steps in different colours to grab attention and make sure data is entered correctly.

3/ Auto-Trigger Walkthroughs

Gyde doesn’t wait for employees to ask for help. It automatically triggers walkthroughs based on predefined actions. For example, when a new employee logs into a CRM, it will help them complete new workflows (eg. adding a new sales opportunity)

4/ Field validation

Gyde validates data as it’s entered. It checks that the data matches predefined rules and confirms it’s accurate—so your employees aren’t left second-guessing.

5/ Help articles

Gyde offers contextual help articles based on the user's screen, providing quick solutions to common issues—like explaining terms or clarifying fields—without needing IT support.

On top of this, Gyde offers multilingual support, analytics to track how well the walkthroughs are performing, and much more.

So, if you're looking to improve data quality right from the moment it enters your system, adding a DAP like Gyde to your CRM, HRMS, or any other software will automate the process and guide new users easily.

It’s the smart way to consistently enter the correct data into your system!

How to Build a Data-Driven Culture

For Business Leaders (C-Suite, VP-Level), building a data-driven culture means creating a system where trustworthy data is at the core of decision-making.

Here’s how to do it:

1. Set Up a Formal Framework

Establish clear roles and responsibilities for data management within the organization. Define who is responsible for data accuracy and consistency, and outline data policies that guide data usage, storage, and sharing. This prevents costly mistakes and all teams can trust the data they rely on.

2. Assign Data Ownership

Appoint specific people or teams to oversee data quality and enforce data standards. Create accountability and make sure no one overlooks the importance of maintaining clean data.

3. Establish KPIs for Data Quality

Identify key performance indicators (KPIs) that track and measure data quality. Focus on aspects like accuracy, completeness, consistency, timeliness, and validity. These KPIs support informed decision-making, avoiding delays in operations.

4. Conduct Regular Data Quality Checks

Regular audits and checks are important to spot and address data issues before they snowball into bigger problems. By identifying gaps or inconsistencies early, you can correct them proactively, ensuring that all teams have reliable data when needed.

5. Profile Your Data

Data profiling helps you understand the characteristics and distribution of your data. It enables you to assess its quality in-depth and get insights into potential areas for improvement. By profiling your data regularly, you gain a clearer picture of how it supports business outcomes and where improvements can be made.

For L&D and Operations Professionals, creating a data-driven culture is about simplifying workflows and helping employees to work confidently with reliable data:

1. Clarify Data-Driven Training Programs

Design training that’s not just informative but crystal clear. Align these training programs with your organization’s data standards so employees know exactly what’s expected of them. Show employees how their daily actions, like entering a sales lead or updating a project timeline, directly impact overall data quality.

When employees see how their work connects to the bigger picture, they’re not only more productive but also more confident. And that confidence? It fuels a data-driven culture.

2. Standardize Data Processes

When teams follow different approaches for data entry and management, it becomes a perfect recipe for chaos. By standardizing training processes, you create a single, reliable playbook that everyone can follow. This isn’t just about avoiding errors (though that’s a huge perk)—it’s about making work easier for everyone.

After all, consistency removes the guesswork, makes sure data is accurate and simplifies employee training & development.

3. Simplify the Learning Curve for New Tools

Introducing a new tool or system is all good—until the learning curve slows employees down. The way out is digital adoption strategy which integrates intuitive training materials directly into the tool. These materials should guide employees on how to enter and manage data correctly, step by step.

Even better, include built-in quality checks within the system (Remember Gyde’s field validation? Same idea). By doing this, you create an environment where employees can adopt new platforms faster without making costly errors.

In both cases, a strong focus on data quality not only leads to better decision-making but also supports the long-term goals of digital transformation and operational efficiency.

Over to You

In this blog, we’ve outlined solutions and best practices to help you improve data quality across your systems. We’ve also emphasized why addressing this challenge is more urgent than ever.

With the sheer volume of data flowing daily into your CRM, ERP, and other systems, it’s important to support people entering this data. They need the right support and guidance to navigate complex systems and make the most of them.

FAQs

What is CRM data quality?

CRM data quality refers to the accuracy, completeness, consistency, and reliability of customer data stored in a Customer Relationship Management (CRM) system. High-quality CRM data is when businesses can trust the information they use to make decisions, engage customers, and drive sales strategies. Poor CRM data quality can lead to missed opportunities, inefficient processes, and reduced customer satisfaction.

How to fix data quality issues?

The secret to fixing it is to stop the chaos at the source—data entry. Set up validation rules that let only the right entries in. Support your data inputters. Give them proper training or handy guides. Regularly audit the data and provide feedback, but keep it constructive. With these steps, you'll see data quality improve faster than you can say "duplicate record."

What Does Field Validation Mean? How Does It Work in a Digital Adoption Platform (DAP)?

Field validation makes sure that data entered into a specific field meets predefined criteria, such as format, length, or type. It acts as a gatekeeper, preventing incorrect or incomplete information from being saved. For instance, it can verify that an email address includes "@" or that a phone number contains only digits.

In a DAP, field validation is integrated into guided workflows. As users input data, the platform checks entries in real-time against validation rules. If an error is detected, the DAP provides instant feedback, often with tips or tooltips to correct the mistake. This improves data accuracy and offers just-in-time guidance.