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Business decisions are only as effective and good as the data behind them….
Data quality issues affect business outcomes at a deeper level than we’d expect. The problem? Data quality is always treated as an IT challenge and not as much as a business challenge even if business departments such as marketing and customer service, are the most affected when data is corrupt or unreliable. Misspelled names, missing details, outdated contacts, are some examples of data quality issues that can snowball into big problems, costing time, money, and opportunities.
A Harvard Business Review found that bad data costs the U.S. economy around $3.1 trillion per year, affecting everything from marketing to decision-making. Clearly, this statistic indicates that data quality needs to be an organizational concern and not just an IT problem to fix. However, the lack of involvement and training of business users, and the outdated approach of making data solely a technical challenge prevents organizations from taking a deeper look at their DQM challenges.
The solution? Data quality must be a key part of organizational processes just like marketing, finance, or sales is. It can no longer be a “separate” entity left to IT or Data Managers to figure out. When businesses get hit with penalties, customer complaints, or GDPR, it becomes everyone’s problem.
Here’s how and why organizations should prioritize data quality – today.
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. And often, you won’t even see the damage until it’s too late.
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 A misspelled customer name, a missing field, or outdated information.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. Marketing teams waste time and money on bad leads. Sales teams lose track of opportunities. Customer service fumbles interactions.
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.
The Real 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.
Take inconsistent data across systems, something most businesses deal with every day. For example, a customer’s information in the sales system might not match what’s in the customer service database. This inconsistency creates confusion and miscommunication. Teams waste time verifying details, leading to delays & frustration.
Another overlooked issue is data decay. Information gets old. People move, change jobs, update their contact details, but many companies don’t have processes to refresh their data regularly. A CRM full of outdated data actively hurts your ability to build relationships and close deals.
Then there’s the problem of hidden data. Data that gets siloed in one part of the organization and is never shared with others. Sales might have critical insights about a customer that marketing never sees, simply because the systems don’t talk to each other. This lack of data flow results in lost opportunities & incomplete customer profiles.
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.
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 is like a slow leak in your business—one that 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. It’s not the obvious mistakes; it’s the damage that builds up over time in ways that aren’t immediately clear.
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. It’s an emotional and mental drain that silently erodes productivity and creativity, leading to disengagement and turnover.
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. These aren’t just sales you didn’t make—these are long-term relationships lost before they even had a chance. Bad data doesn’t just kill leads—it shuts the door on potential growth, and you may not even realize it until it’s too late.
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. It sends the message that you don’t care. 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. Over-ordering stock because inventory data is off, mismanaging budgets due to inaccurate financial records, or delaying production because of poor scheduling data. These operational inefficiencies pile up silently, bleeding money in small ways that add up to massive losses over time.
And then there’s the hidden financial cost. Poor data quality doesn’t just hit you in one place—it seeps into every corner of your business. Gartner estimates $12 million per year is lost to poor data quality for an average business, but that’s just the surface.
Fixing Data Starts With People, Not Just Tools
Fixing data starts with people because tools alone can’t fix the mindset or habits that cause data problems in the first place. Most data issues don’t happen because the tools aren’t there. They happen because people aren’t trained to think about data the right way.
Here’s something that doesn’t get discussed enough: many data errors come from everyday actions. A sales rep entering customer info without checking for duplicates, or a manager who assumes the data they’re using is always accurate without double-checking. These small, human errors build up over time. When people don’t see the value of clean data, they treat it as someone else’s job.
The real challenge is changing the way people interact with data. It’s about creating a culture where everyone, from junior analysts to CRM managers, feels ownership over the data they handle. This is about shifting the mindset. When people understand how even a small mistake can ripple through the company, causing lost sales, bad decisions, and wasted time, they start treating data differently.
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 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.
