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Business decisions are only as effective and good as the data behind them….
Drowning in data but still thirsty for real insights? You’re probably nodding if you’re a CRM analyst, data manager or anyone working with data day in and day out. The reality is that fancy algorithms can’t fix a fundamentally flawed foundation. We keep throwing money at BI tools, but are we truly addressing the garbage-in, garbage-out problem?
A cold, hard fact is that poor data quality isn’t merely confined to the tech. It’s a drain on resources and impacts revenue. A Harvard Business Review states the bad data costs the U.S. economy around $3.1 trillion per year, affecting everything from marketing to decision-making. Think about what your team could do with that kind of money if it wasn’t being wasted on cleaning up messes.
So, where’s the real issue? Well, The disconnect lies in ownership. Because we know that data quality often gets pawned off on IT, while marketing and sales are left to pick up the pieces of misdirected campaigns and lost leads. It’s time to stop treating data quality as a back-end task and start recognizing it as a core business strategy.
In this article, we’re not going to insult your intelligence with Data Quality 101. Instead, we’ll dig into data quality challenges, look at why fixing data quality should be everyone’s priority, and explore realistic ways to build a culture of data integrity within your organization.
Here’s how and why organizations should prioritize data quality – today.
What Are Data Quality Issues and Where Do They Come From?
Dirty data doesn’t just appear out of nowhere. It’s a byproduct of everyday business operations. It sneaks in through rushed inputs, overlooked details, and outdated records. These issues build over time, turning small mistakes into big problems.
Data quality issues are
╰┈➤The typos your sales rep rushed into the CRM.
╰┈➤The duplicate entries your marketing team ignored.
╰┈➤The outdated customer address no one bothered to update.
Dirty data is a multi-million dollar problem that keeps growing if left unchecked. We broke down exactly why dirty data happens, the biggest culprits behind it, and how to prevent it in this video.
Most businesses don’t notice the damage until it’s too late. So, where does all this dirty data come from?
What Are The Causes Of Poor Data Quality
Here are a few common culprits:
1️⃣ Human Errors
People make mistakes. A CRM entry is rushed. A sales rep misspells a customer’s email. A support agent logs an interaction under the wrong name.
The result?
🔹 Duplicate contacts that lead to conflicting customer profiles.
🔹 Incorrect contact details that make outreach impossible.
🔹 Mismatched records that cause sales and support teams to operate on different versions of the truth.
2️⃣ System Limitations
Most businesses don’t rely on one system to manage customer data. Instead, they use a patchwork of CRMs, ERPs, spreadsheets, and third-party tools—all storing data in different formats and structures.
🔹 Marketing uses HubSpot.
🔹 Sales uses Salesforce.
🔹 Finance uses an internal ERP.
Each system holds a fragment of the customer’s data, and none of them match. Without proper integration and standardization, errors multiply, creating conflicting records that no one knows how to fix.
3️⃣ “Quick Fixes” That Create Bigger Problems
Instead of fixing data issues at the root, many teams patch them up with temporary workarounds:
🔹 “Just export it to Excel and clean it manually.”
🔹 “Merge these files and remove duplicates later.”
🔹 “We’ll fix it next quarter when we have time.”
Sound familiar? These temporary fixes become permanent problems. A small data inconsistency today turns into a broken reporting system next year.
4️⃣ Data Decay
Data isn’t static as it changes constantly. And do you know that 30% of customer data becomes outdated every year.
🔹 People change jobs.
🔹 Companies rebrand.
🔹 Customers switch emails and phone numbers.
Without regular data maintenance, businesses unknowingly operate on stale, inaccurate information which then leads to failed outreach, missed sales, and outdated reports.
5️⃣ Duplicate Data: When Your Systems Work Overtime (For No Reason)
Duplicates don’t just clutter your database but they also inflate customer counts, ruin analytics, and frustrate customers.
👉 A customer’s email appears twice in the database. Marketing sends duplicate emails, the customer gets annoyed, and unsubscribes. Meanwhile, sales sees two separate accounts for the same person and fails to track engagement history properly.
6️⃣ Data Silos
When different teams hoard their own datasets, businesses lose visibility into their most valuable insights.
🔹 Sales sees high-value leads that marketing never follows up on.
🔹 Customer support resolves recurring complaints, but product teams never hear about them.
🔹 Finance reports revenue growth, but marketing’s engagement data tells a different story.
👉 Example: Marketing launches a campaign targeting existing customers, but sales doesn’t see it in real-time. Instead of reinforcing the marketing message, the sales team pushes a different offer, causing confusion and reducing conversions.
7️⃣ No One Owns the Data, So No One Fixes It
Who’s responsible for keeping customer data accurate?
🔹 Sales assumes IT will handle it.
🔹 IT assumes Marketing owns customer records.
🔹 Marketing assumes Data Ops will clean it up.
No one fixes anything. As per Data Science Central. 44% of organizations struggle with ambiguous or unclear data ownership.
Without active data quality management, your CRM turns into a graveyard of outdated contacts. Businesses don’t fail because they don’t have enough data. They fail because they don’t have the right data.
Why Data Quality Should Be Everyone’s Problem
Data quality is something that affects every person in a company, especially those handling the data daily. Whether you’re a junior analyst, a CRM manager, or a business user, if the data you rely on is bad, your work suffers.
Data issues usually start small. A misspelled customer name, a missing field, or outdated information. These seem like minor problems, but over time they add up, creating bigger issues that ripple across the entire organization.
Data quality is everyone’s responsibility. When everyone takes ownership of the data they work with, the entire company benefits. This is how good decisions are made, and how bad ones are avoided.
Let’s say you work for a company that sells software. One day, a small mistake happens in the CRM system. A customer’s email address is entered with a typo. It seems minor, right?
But that one error triggers a chain of problems.
The marketing team sends out a campaign email to this customer, but it bounces back. They don’t notice, and the customer misses out on an important product update. A few weeks later, the sales team tries to contact the same customer for an upsell opportunity, but they can’t reach them because the email is still wrong. Meanwhile, customer support has logged a ticket for this customer about a software issue, but because their contact information is outdated, no one follows up properly.
Now you’ve got a frustrated customer who feels ignored, a lost sales opportunity, and a marketing effort that’s been wasted. All because of one small data error that no one caught early.
Let’s break down the four biggest data quality issues wrecking your business from the inside.
Top 4 Data Quality Issues That Hurt Your Business
The real problems with data quality aren’t just the obvious mistakes like duplicate records or missing fields. They run deeper.
Inconsistent Data Across Systems
Think of this as a communication breakdown between departments. Your sales team might have one version of a customer’s information, while customer service has something totally different.
Let’s say a customer calls support with an issue, but the agent doesn’t have the full sales history. The customer gets frustrated, and your team wastes time verifying details. Or worse, marketing is trying to personalize campaigns, but the messaging is off because the data doesn’t match up.
You can’t try to build a house with mismatched blueprints.
Data Decay
“But we cleaned our CRM last quarter!”
Another overlooked issue is data decay. Information gets old fast. People change jobs, move, and update their contact details. If your CRM is full of outdated information (GDPR fines for storing ex-employee data), you’re actively hurting your ability to build relationships and close deals. Gartner says around 3% of data decays each month.
It hurts this way: Sending emails to old addresses? Calling people who no longer work at a company? You’re probably using a map from 1950 to navigate a modern city. Good luck with that.
Hidden Data
Marketing hoards campaign analytics. Sales hides lead feedback. IT locks away API logs.
This is data that’s trapped in one part of your organization and never shared with others. Your sales team might have critical insights about a customer that marketing never sees, simply because the systems don’t talk to each other.
Let’s say a tech startup’s engineering team built a feature using 6-month-old customer feedback, unaware that Support had updated pain points in Zendesk. Do you think the launch will be successful?
Data Bias
Your “customer persona” is built on 80% Gen Z survey responses but your product team is all millennials.
Last but not least, data bias is a silent killer. If your data is skewed or incomplete, it leads to biased decisions. Whether it’s sales forecasting, customer segmentation, or product development, decisions based on bad data can steer the entire business in the wrong direction.
If you’re using skewed data to target a specific customer segment, your assumptions are wrong. You could be alienating potential customers or missing out on new markets.
It’s more like wearing tinted glasses where you think you’re seeing the world clearly, but your perception is distorted.
These are the deeper data quality issues that cause real damage and fixing them takes more than just cleaning up a few records. It requires a commitment to maintaining data accuracy, consistency & transparency across the board.
What Data Issues Really Costs You
Bad data doesn’t show up in flashing red alerts. It doesn’t cause system crashes or immediate financial losses. Instead, it quietly drains resources, trust, and growth without you even noticing. Most people think data issues are just about fixing errors, but the real costs run much deeper.
Here’s what bad data is really costing you…
Wasted Time & Resources
Every time your team chases down bad data, they lose time they’ll never get back. But it’s not just about time. Constantly dealing with messy data creates frustration, demoralizes teams, and increases burnout.
Your sales rep spends 47 hours chasing “Megan Lee,” a lead who quit her job 8 months ago. Her LinkedIn says “Open to Work.” Your CRM says “Decision Maker.”
The cost isn’t just lost hours. It’s the demoralizing grind of dead-end work. In fact, up to 50% of employees waste time on preventable data fires. That’s not “productivity loss.” It’s paying people to dig ditches and fill them back up.
Missed Sales, Lost Growth
Imagine missing out on a high-value customer because their contact info was wrong, or sending offers to the wrong people who aren’t interested. More than sales lost, it’s about the customers who never even had a chance to convert.
⇒ A high-value lead was misclassified as a low-priority account.
⇒ A VIP customer never received their loyalty discount because their profile was outdated.
⇒ A prospect ignored your email because their name was misspelled in the subject line.
Small data errors don’t hurt the deal right in front of you, they create invisible revenue leaks across your entire pipeline. Actually, 40% of B2B companies lose deals due to outdated data. Worse? They don’t even know which ones.
Lost Trust with Customers
When customer service calls go unanswered, orders are shipped to the wrong address, or personalized marketing feels off, your customers notice. Customers might forgive one mistake. They won’t forgive repeated ones.
⇒ A customer updates their shipping address, but your system still sends their order to the old one.
⇒ A support ticket goes unresolved because their email is mismatched across databases.
⇒ A long-time subscriber gets a ‘Welcome’ email like they’ve never interacted with your brand before.
They may be flagged as minor mistakes but they send a message
❌ “We don’t care enough to get your details right.”
❌ “We don’t actually know who you are.”
❌ “You’re just another record in our system.”
And here’s something most businesses don’t think about: trust once broken can spread quickly through word of mouth or social media, hurting your reputation far beyond the immediate mistake.
Invisible Operational Inefficiencies
Bad data disrupts the day-to-day in ways that are hard to spot. It’s expensive in ways you don’t even track.
⇒ Inventory forecasts are wrong, so you over-order stock that won’t sell.
⇒ Employee payroll data has inconsistencies, leading to overpayments and compliance risks.
⇒ Financial reports are off because revenue tracking data doesn’t match across systems.
And because these inefficiencies aren’t obvious in the short term, they build up silently which then leads to millions in unnecessary expenses, process bottlenecks, and hidden losses.
And then there’s the hidden financial cost. Gartner estimates $12 million per year is lost to poor data quality for an average business, but that’s just the surface. The real cost is in lawsuits, layoffs, and lost market confidence.
With increasing data privacy regulations like GDPR, CCPA, and others, bad data can lead to hefty fines. If you’re not managing data properly, you could be at serious risk.
How to Fix Data Quality Issues: Start with People, Not Tools
Bad data starts with people. It’s not a technology issue but a habit issue. Companies invest in data cleansing software, deduplication tools, and automation workflows, thinking they’ll solve the problem, but tools don’t fix the behaviors that create bad data in the first place.
Things like
➜ A sales rep entering customer info without checking for duplicates,
➜ A marketing team rushing to import leads without verifying them,
➜ A manager assuming the report they’re reviewing is accurate without cross-checking the source
They’re the everyday mistakes that compound over time, turning a few inconsistencies into a system-wide data disaster.
And yet, no one sees it as their problem.
No software can solve a culture where data is treated as an afterthought. That’s why fixing data quality is about changing the way people interact with data.
A data-driven culture demands clear roles, accountability, and a structured team. The ISO 8000-61 framework lays out exactly how to assemble a high-performing data quality team that ensures clean, reliable data from the start.
Ownership is everything. Companies that build a culture where employees take responsibility for the accuracy of the data they touch will always outperform those that rely on IT to clean up the mess later. Because in the end, clean data is never just confined to technology but the people who enter, use, and trust it every day.
Smarter Ways to Handle Data Quality Today
Handling data quality today is about being smarter. Anticipating problems before they occur and using innovative approaches to keep data clean and reliable from the start.
One of the lesser-known strategies is data democratization. This means making data accessible to more people in the organization, not just the IT or data teams. When more people can access, analyze, and interact with data, they’re more likely to spot issues early. The key here is training—making sure everyone knows how to handle data responsibly.
AI-driven tools are also changing the game. Instead of relying on people to manually check for errors, AI can constantly scan for inconsistencies, duplicates, or anomalies. AI learns from the data, adapting and improving as it processes more information.
But here’s where it gets interesting: no-code tools are allowing non-technical users to handle data quality. You don’t need to be a data scientist to fix data anymore. No-code platforms let CRM managers, sales teams, and other non-technical staff clean and merge data without writing a single line of code.
Other smart ways to manage data quality include:
☑️ Proactive monitoring: Regular checks to catch issues early, not after the damage is done.
☑️ Real–time validation: Ensuring data accuracy the moment it’s entered, reducing errors from the start.
☑️ Automated workflows: Streamlining repetitive tasks like deduplication or formatting, freeing up time for more strategic work.
The smartest approach to data quality today is empowering every team member to play their part in keeping data accurate and valuable.
A Real-World Example: Data Quality in Life-or-Death Situations
Take the Wadhwani Institute for Artificial Intelligence. In the middle of the COVID-19 pandemic, they developed a groundbreaking technology to detect COVID-19 risk through cough sounds, collected via smartphones. The problem?
The data needed to power this solution was coming from multiple sources, patient registrations, cough sound data, and COVID test results. And as you’d expect, there were inconsistencies everywhere: misspelled email addresses, incomplete entries, and records that didn’t match up. These gaps could delay diagnosis, leaving at-risk patients undetected.
The Wadhwani Institute needed a solution that could quickly and accurately match patient records across different datasets, without any margin for error. They turned to WinPure for its best-in-class data matching technology. By using WinPure, they were able to link patient records efficiently, ensuring the right data was connected to the right person.
This was about saving lives by identifying patients more quickly, helping healthcare systems focus their resources on those most likely to be infected.
When data quality is this critical, the stakes are higher than ever. It’s a powerful reminder that clean, reliable data is more than just a business asset—it’s a human one.
Simple Ways to Keep Your Data in Check
Keeping your data in check doesn’t have to be complicated. In fact, the most effective strategies are often the simplest. The key is consistency. Small actions, done regularly, can prevent major data problems from piling up.
One of the simplest practices is regular audits. Set aside time each week to review your data for inconsistencies, duplicates, or missing information. It’s easy to assume that someone else is handling it, but a quick check can save you hours of headache later.
Standardize your data entry process. This is where most data quality issues begin. If everyone is entering data differently—names in different formats, addresses with varying abbreviations—you’re setting yourself up for a messy system. Create clear guidelines for how data should be entered, and stick to them.
Another simple but effective method is to use automated validation rules. Many tools allow you to set rules that automatically check for errors or missing fields when data is entered. This catches problems right at the source.
Some simple ways to stay on top of your data:
✅ Set up alerts: Get notified when certain fields are missing or inconsistent.
✅ Clean as you go: Don’t wait until the data pile is overwhelming. Fix errors as they appear.
✅ Backup regularly: Keep a backup of your data so you can revert if something goes wrong.
✅ Use deduplication tools: Run a deduplication check regularly to ensure you don’t have redundant records.
The key is to stay proactive. Data quality management is an ongoing process.
Final Thoughts
Data quality issues aren’t just an IT problem. They impact every part of your business, from sales to customer service. What many overlook is that data quality starts with people, not tools. When everyone, from junior analysts to managers, takes ownership of the data they handle, the entire organization benefits. Clean, accurate data fuels better decisions, growth, and customer loyalty. Fixing data is about building a culture that values and protects data. The sooner you make this shift, the stronger your business becomes. Don’t wait for data problems to grow, act now, because the cost of inaction is far greater.
