Data Quality Issues

Maintaining high-quality data is a crucial aspect of running a successful organization, regardless of the industry. However, companies often face persistent data quality issues that hamper analytics, distort insights, and lead to costly business mistakes.

This article examines the nature of data quality issues, their underlying causes, common challenges, and strategies for organizations to proactively manage them, thereby ensuring data integrity and reliability.

What Constitutes a Data Quality Issue?

Data quality issues arise when data is inaccurate, incomplete, inconsistent, outdated, or duplicated, reducing its value and trustworthiness. These issues can stem from human errors, system incompatibility, integration problems, or outdated practices. Whether it’s a misspelled name in a customer database or inconsistent date formats across departments, even minor flaws can cascade into major business disruptions. For example, misspelled names can lead to duplicate customer entries, which can lead to incomplete customer activity records. Inconsistent date formats can create confusion across global teams or make it difficult to search for the information teams need.

Good data quality is typically measured by dimensions such as:

• Accuracy: Measures how closely data reflects the real-world values/facts it is intended to represent.
• Completeness: Measures whether all required data is present and fully captured without any missing fields or elements.
• Consistency: Measures whether data remains uniform across different systems, formats, and timeframes without conflicting information.
• Timeliness: Measures whether data is up-to-date and available when needed for decision-making or operations.
• Uniqueness: Measures whether each data record is singular, with no unintended duplicates across datasets.
• Validity: Measures whether data conforms to defined formats, rules, and constraints (such as data type or range).

Any deviation in these areas may result in decisions made on flawed assumptions, causing impacts farther downstream of the data pipeline.

Why Data Quality Matters

Data quality matters because businesses rely on accurate data to make critical decisions, such as forecasting revenue, targeting specific customer demographics, detecting fraud, and managing supply chains. Poor data quality can:

• Lead to incorrect insights and strategic missteps.
• Reduce operational efficiency.
• Damage customer relationships.
• Create compliance and regulatory risks.
• Increase costs due to rework and manual corrections.

According to Gartner, poor data quality costs organizations an average of $12.9 million annually. The sooner businesses recognize and fix these problems, the more resilient and data-driven they become.

Common Data Quality Challenges

Data quality issues often manifest in several predictable forms. Understanding these common problems is the first step toward remediation.

Duplicate Entries

Duplicate records occur when the same data entity is entered multiple times, either due to system integrations, human error, or lack of validation. For example, a customer might appear twice in a CRM with slight variations in their name, leading to skewed marketing metrics and duplicated communications.

How to Solve It

• Use de-duplication software: These tools identify and merge duplicate entries.
• Set unique identifiers: Assign a primary key or unique ID to each record.
• Train data entry personnel to prevent duplication at the source through proper training and standardized data input protocols.

Inaccurate and Incomplete Information

Inaccuracy refers to incorrect data values, while incompleteness indicates missing values. Either issue can cause significant problems for organizations. For example, a client record missing a valid phone number or an incorrect address could impact communication and delivery.

How to Solve It

• Implement mandatory fields: Use form validation to ensure required fields are filled out.
• Integrate external verification tools: For instance, email or address verification services can cross-check data in real-time.
• Use dropdowns and controlled inputs: Minimize free-text fields to reduce human errors.

Inconsistent Data Formats

Inconsistent formats can occur when different systems or teams use varied conventions for dates, currencies, or text entries. This makes data aggregation and analysis difficult and errors more likely.

How to Solve It

• Define and enforce data standards: Establish clear formatting rules organization-wide.
• Normalize data: Use ETL (Extract, Transform, Load) processes to clean and unify data formats.
• Automate formatting checks: Incorporate rules into the data intake process to validate formats upon entry.

Outdated and Irrelevant Data

Over time, data becomes obsolete or irrelevant. A customer might change jobs, move cities, or stop using an organization’s services. Relying on outdated data leads to ineffective targeting and missed opportunities for re-engagement or upsells.

How to Solve It

• Schedule periodic data reviews: Audit records to identify and purge stale data.
• Enable self-service updates: Allow users and customers to update their own data through secure portals.
• Use real-time data feeds: When possible, connect to dynamic data sources that provide up-to-date information.

Identifying the Root Causes of Data Issues

Fixing symptoms isn’t enough. Data teams must tackle the underlying causes to achieve long-term data health. Below are some of the root causes that can lead to poor data quality.

System Integration Problems

Organizations often operate on multiple platforms that don’t seamlessly communicate. Disjointed systems may overwrite or duplicate data without clear logic, leading to inconsistencies.

Solution: Invest in robust integration platforms or middleware that ensure clean, consistent data flows across systems.

Human Errors in Data Entry

Manual data entry is prone to typos, omissions, and inconsistencies. Lack of training or unclear procedures only exacerbates the issue.

Solution: Automate data entry where possible and implement user-friendly forms with real-time validations and autofill suggestions.

Lack of Standardization

Without clearly defined data standards (such as naming conventions, formats, and categorization rules), teams across departments may record and interpret data differently.

Solution: Create and disseminate a data standards guide and enforce compliance using data governance frameworks.

General Strategies to Prevent and Fix Data Quality Issues

Addressing data quality requires ongoing effort. In the previous sections, we’ve laid out some ways to fix specific data quality issues as they arise. Below, check out some general best practices to maintain clean and trustworthy data.

Implementing Data Validation Techniques

Validation is an organization’s first line of defense. By automatically checking data against rules and patterns during entry, data teams can prevent many issues from arising in the first place. Applicable techniques include:

• Syntax validation: Ensure entries conform to the expected format (e.g., email addresses).
• Range validation: Confirm numerical values are within acceptable ranges.
• Reference checks: Cross-reference entries with authoritative datasets.

Regular Data Audits and Cleansing

Data audits help assess the health of a company’s data, while cleansing involves identifying and correcting any issues identified during those audits.

• Schedule monthly or quarterly reviews.
• Use data profiling tools to detect anomalies.
• Deploy automated scripts to flag or remove problematic entries.

Establishing Data Governance Policies

Data governance encompasses the people, processes, and technologies required to manage data as a valuable resource.

• Assign data stewards responsible for specific datasets.
• Document data lineage to track data from source to usage.
• Establish escalation paths for reporting and resolving quality issues.

Leveraging Technology for Better Data Quality

Technology plays a vital role in maintaining high data quality across the organization. Modern data quality tools automate the detection, monitoring, and correction of data issues, often in real-time. Key functionalities include:

• Profiling: Analyzing data to discover patterns and irregularities.
• Cleansing: Removing or correcting inaccurate or incomplete data.
• Matching/Deduplication: Identifying and consolidating similar records.
• Monitoring: Setting up rules and alerts to catch errors as they occur.

Examples include tools like Informatica Data Quality, Talend Data Preparation, and IBM.

Choosing the right tool depends on factors like data volume, complexity, integration needs, and budget.

Actian Data Intelligence Platform Helps Organizations Manage, Govern, and Use Data

To proactively address and manage data quality issues, organizations can turn to the comprehensive Actian Data Intelligence Platform. It provides an end-to-end solution for integrating, cleansing, analyzing, and governing data. With its hybrid cloud architecture, organizations can manage data across on-premise and cloud environments. Features that support data quality include:

• Data Quality Workflows: Automate cleansing and validation routines.
• Governance and Lineage Tracking: Ensure compliance and transparency.
• Real-Time Data Integration: Reduce inconsistencies caused by batch processing.
• Self-Service Data Access: Empower users with reliable data without compromising control.

By centralizing data quality efforts within a powerful platform, organizations can scale their data operations while ensuring trust in every data-driven decision. Schedule a personalized demo of the platform today.

About Actian Corporation

Actian empowers enterprises to confidently manage and govern data at scale, streamlining complex data environments and accelerating the delivery of AI-ready data. The Actian data intelligence approach combines data discovery, metadata management, and federated governance to enable smarter data usage and enhance compliance. With intuitive self-service capabilities, business and technical users can find, understand, and trust data assets across cloud, hybrid, and on-premises environments. Actian delivers flexible data management solutions to 42 million users at Fortune 100 companies and other enterprises worldwide, while maintaining a 95% customer satisfaction score.