Reliable data is the lifeblood of any thriving business. From streamlining operations to crafting personalized customer experiences, data drives virtually every aspect of modern organizations. Yet, when your organization has data quality issues, the ripple effects are more than inconvenient — they’re costly.
In fact, according to Gartner, the cost of poor data quality is a staggering $12.9 million annually. But the price organizations pay for poor data quality goes beyond finances. It impacts productivity, decision-making, and even brand reputation. In fact, businesses often find themselves dedicating up to 30% of their revenue to managing these issues.
The challenges of data quality are multifaceted and pervasive. Whether it’s duplicate customer records, missing information, inconsistent data formats, or outdated entries, these errors can erode trust in analytics and lead to flawed business strategies.
The problem? Data quality isn’t just a technical issue—it’s a strategic one. C-level executives are increasingly aware that unreliable data creates blind spots in crucial areas like forecasts, growth metrics, and customer insights.
This blog will address these pressing concerns head-on. We’ll explore common data quality problems, why they occur, and perhaps most importantly, practical and actionable steps to fix them. With compelling real-world examples, you’ll see how resolving these obstacles unlocks the full value of your data, paving the way for informed decision-making and sustained business success.
So, what are the most common data quality challenges?
Data quality challenges come in many forms, and each can significantly disrupt a business’s operations. Let’s break down the most common issues organizations face.
Inaccurate or incomplete data
Have you ever received an email addressed “Dear John” when your name is actually Michael? That’s an example of inaccurate data. Whether due to typos during manual entry or processing errors, mistakes like this can reduce trust in communications and skew analytics.
Calling customers by the wrong name not only damages the personal connection but can also lead to missed opportunities and decreased customer satisfaction. Such inaccuracies can have far-reaching consequences, from failed marketing campaigns to misguided business strategies based on faulty customer information.
Other types of inaccuracies spring from incomplete data. Fields with missing values — such as a customer’s phone number or email address — limit a company’s ability to effectively market, engage, or even logistically coordinate with its audience.
Duplicate data
Duplicate data is another common offender. When multiple systems collect overlapping information without deduplication strategies in place, storage resources are wasted, and decision-making becomes unnecessarily complicated.
Moreover, duplicate data can lead to significant operational inefficiencies. For instance, a customer might receive multiple copies of the same marketing email, leading to frustration and potential unsubscribes. In financial contexts, duplicate transactions can wreak havoc on accounting processes, causing discrepancies in reports and potentially triggering compliance issues. Identifying and resolving duplicate data requires robust data management practices and often specialized tools to ensure data integrity across the organization.
Inconsistent data formats
Imagine trying to merge two datasets when one records a date as “June 10, 2024,” while another formats it as “6/10/24.” Such inconsistencies can make combining, analyzing, or interpreting data an uphill battle. Outdated data, like customer addresses from years ago, can also provide a misleading picture of trends or customer needs.
Additionally, the rise of unstructured data — information stored in emails, PDFs, or similar formats — has rendered traditional databases unequipped to handle valuable but undeveloped data assets. Compatibility issues, such as mismatches between metric and imperial units, only add to the challenge of aligning diverse master data types into a cohesive whole.
Shadowed and dark data
Some data remains in silos — isolated within specific departments or systems — where its potential value goes untapped. This phenomenon, sometimes referred to as “shadow data,” creates blind spots that impair enterprise-level decision-making.
Meanwhile, “dark data” (irrelevant or unidentified data) can inflate storage costs and even distort analytics when left unmanaged.
Why do data quality problems happen?
Understanding why data quality problems happen is key to addressing them effectively. These issues don’t arise in isolation — they’re often the result of organizational, human, and technological factors interacting in complex ways.
Human error
A significant proportion of inaccuracies, inconsistencies, and duplicates stem from manual processes. Employees tasked with entering or handling data may inadvertently type incorrect values, leave fields incomplete, or misinterpret formatting requirements.
Without proper training on data-handling protocols or adequate understanding of data governance policies, errors are bound to creep in. Additionally, a lack of clear data governance frameworks makes it difficult for organizations to enforce quality standards across multiple departments and systems.
Integration of multiple data sources
As businesses expand, they often integrate datasets from a range of sources, such as legacy systems, external vendors, and business partners. The challenge? These sources may use conflicting formats, standards, or definitions.
For instance, a legacy system might record customer data in outdated formats, while third-party platforms input the same data differently. The result is duplication, inconsistency, and confusion that complicates analytics and decision-making.
Technological limitations
Even the most advanced organizations face roadblocks when navigating today’s increasingly unstructured and complex datasets. Traditional databases and manual methods often fail to keep up, leaving valuable data — like insights hidden in social media strings or PDF documents — untapped.
Without a master data management (MDM) solution, managing high volumes of data increases the chance of human intervention errors. Another issue is failing to account for the management of metadata — information such as timestamps or file origins — which can lead to critical mistakes during migration or integration efforts.
Issues with AI adoption
According to research from Semarchy, nearly all (98%) of organisations have encountered AI-related data quality issues — primarily due to data privacy and compliance constraints (27%), a high volume of duplicate records (25%), and inefficient data integration (21%) — leading to cost overruns, delays, and unreliable outputs.
Together, these factors create significant hurdles, preventing businesses from unlocking the full value of their data.
How to overcome data quality challenges
Tackling data quality challenges requires a strategic mix of foundational best practices and modern technology. Here’s how to transform messy data into a trustworthy asset.
1. Build a strong foundation
To start, establish clear guidelines for data formats. Consistency across fields like dates, names, and measurement units ensures that all your systems communicate effectively. Use Extract, Transform, Load (ETL) tools to automate the enforcement of these standards. At the organizational level, MDM strategies act as the backbone — linking and synchronizing critical data points.
2. Leverage the right tools
Modern problems require modern data management solutions. Automated data validation systems can quickly verify accuracy, flag incomplete entries, and clean errors. Deduplication technology is also essential for streamlining databases by merging redundant records. In addition, AI-powered tools can help harmonize information from disparate platforms, reducing inconsistencies and manual workloads.
3. Establish data governance
Success doesn’t stop once initial corrections are made. Regularly audit datasets for inconsistencies, duplicates, and stale records. Organizations should also define clear processes for when to refresh, archive, or delete data to maintain relevance. Embedding ongoing training programs ensures your data stewards and other employees stay aligned with updated best practices.
At Semarchy, we’ve seen that a clear governance policy, when coupled with accountability, enhances long-term data reliability.
4. Implement data lineage tracking
Understanding the journey of your data from its origin to its final destination is crucial for maintaining data quality and trust. Data lineage provides a clear map of how data moves through your systems, where it’s transformed, and how it’s used. This visibility not only helps in troubleshooting data issues but also ensures compliance with data regulations.
By implementing data lineage tracking, you can quickly identify the source of data quality problems, understand the potential impact of changes, and make informed decisions about data governance. Tools that automate data lineage documentation can significantly reduce the time and effort required to maintain this valuable insight into your data ecosystem.
5. Use AI and machine learning
AI and machine learning technologies can automatically transform data, flag outliers, and enrich incomplete entries by cross-referencing them with verified external data. For example, predictive algorithms can anticipate inconsistencies before they arise, while self-learning systems adapt to your organization’s specific data patterns.
Here are some examples of poor data quality issues being addressed
Businesses of all kinds are solving data quality issues with the right strategies:
Establishing a retail 360˚ view
Semarchy customer Red Wing Shoes needed a central data hub as the explosion of e-commerce saw great growth for the business. By generating a 360˚ view for all divisions of the company, data literacy improved and allowed for customer data to be leveraged more effectively, while also improving data quality through eliminating silos across different departments.
Standardizing healthcare data
Sanofi, a global healthcare leader, worked with Semarchy to implement MDM into their internal systems. This integration has since led to an increase in operational productivity and significant cost savings. By improving data quality, Sanofi is much less likely to suffer any data breaches and incur large fines, keeping patient data secure.
Boosting data quality in financial services
Semarchy has worked with AAIS, a well-known insurance and financial services business on improving data management processes at the business to minimize common data quality issues such as data duplication and unstructured data. Since implementation, AAIS has seen a 75% improvement in overall efficiency across the business.
Closing the gap between chaos and clarity
High-quality data isn’t just a behind-the-scenes necessity — it’s a cornerstone of business success. When data is accurate, consistent, and enriched, it empowers organizations to make reliable decisions, improve efficiency, and boost their bottom line. On the other hand, failing to address data quality challenges — such as inaccuracies, inconsistencies, duplicates, and siloed information—can lead to missed opportunities, wasted resources, and flawed analytics.
Addressing these challenges starts with a commitment to robust data governance, embracing MDM automation, and maintaining consistency through training and accountability. When combined with cutting-edge tools like AI-powered validation and deduplication, businesses lay the groundwork for a more comprehensive and scalable data strategy.
Don’t wait for data quality issues to snowball. Addressing these concerns today creates a strong foundation for operational success, advanced analytics, and long-term innovation.
At Semarchy, we specialize in turning data chaos into clarity. Ready to unlock the full potential of your data? Let’s create solutions tailored to your needs — contact us today!
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