How to Improve Data Quality

Every organization, whether a business or a non-profit, relies on accurate and reliable data to make critical strategic decisions. Whether it’s improving customer experience, streamlining operations, or driving revenue, high-quality data plays a pivotal role.

However, achieving and maintaining top-notch data quality remains a significant challenge for many. In this guide, we explore how to improve data quality by understanding its fundamentals, addressing common challenges, and adopting strategic steps and tools for sustainable data excellence.

Understanding Data Quality

First, let’s break down what data quality means. Typically, data experts use five pillars or sets of attributes to evaluate the quality of a dataset.

Defining Data Quality

Various attributes, when taken together, constitute a dataset’s given quality, such as accuracy, completeness, consistency, timeliness, and relevance. High-quality data should accurately represent the real-world construct it describes, be free of errors and duplications, and be suitable for its intended purpose.

Here’s a brief breakdown of the dimensions that define data quality:

• Accuracy: The degree to which data reflects the real-world entities or values it represents.
• Completeness: The presence of all required data fields and entries.
• Consistency: Uniformity of data across multiple datasets or platforms.
• Timeliness: Data being up-to-date and available when needed.
• Validity: A measure of whether the data conforms to syntax and format rules.

The Importance of High-Quality Data

The consequences of poor data quality can be far-reaching. Inaccurate or incomplete data can lead to flawed business decisions, reduced operational efficiency, and loss of customer trust. Conversely, high-quality data enhances analytical capabilities, ensures regulatory compliance, and fuels innovation.

Some key benefits of high data quality include:

• Improved Decision-Making: Clean and reliable data supports accurate forecasting and business planning.
• Operational Efficiency: Reduces time and resources spent on correcting data issues.
• Customer Satisfaction: Personalized and timely customer interactions are only possible with reliable data.
• Regulatory Compliance: Accurate data helps meet industry regulations and standards, reducing legal risk.

Common Data Quality Challenges

Maintaining high-quality data is often easier said than done. Organizations face a range of obstacles that can compromise data quality, particularly as the volume, variety, and velocity of data increase. These challenges are frequently interconnected. Failing to address one can negatively impact the others. Understanding these common pitfalls is the first step toward building more resilient data management practices.

Identifying Data Errors

Data errors are among the most common issues affecting quality. These can include typographical errors, missing fields, duplicate records, and incorrect formatting. Errors often arise from manual data entry, system inoperability, or integration from disparate sources.

Common types of data errors include:

• Input Mistakes: Human errors during manual entry.
• Duplicate Records: Multiple entries for the same entity.
• Outdated Information: Stale or no longer valid data points.
• Inconsistent Formats: Varying data formats across departments or systems.

Detecting these issues early is critical to maintaining data quality. Regular audits and automated error detection tools can help catch problems before they escalate.

Overcoming Data Silos

Data silos occur when information is isolated in separate departments or systems, making it difficult to get a unified view. Silos hinder collaboration, reduce transparency, and often lead to inconsistent or redundant data.

Organizations facing siloed data often experience:

• Redundant Data Storage: Duplicate datasets that waste storage and cause version confusion.
• Lack of Accessibility: Teams struggle to access or share data across functions.
• Inconsistent Data Definitions: Different interpretations of the same data field.

Breaking down silos requires integration strategies and centralized data management systems that promote data sharing and standardization across the organization.

Steps to Enhance Data Quality

Improving data quality requires a structured, proactive approach. It’s not enough to react to data problems as they occur—organizations must build systematic processes and policies that prevent issues before they arise. This means implementing a comprehensive data observability system in tandem with data quality measures.

Below are three foundational steps that can help any organization elevate the integrity, usability, and reliability of its data assets.

Conducting a Data Quality Assessment

A data quality assessment is the foundation for any improvement initiative. It involves evaluating existing datasets against quality dimensions and identifying areas for improvement.

Steps in a data quality assessment include:

1. Inventory of Data Assets: Catalog data sources and flows across systems.
2. Define Quality Metrics: Establish criteria for evaluating accuracy, completeness, etc.
3. Data Profiling: Analyze data for anomalies, patterns, and outliers.
4. Gap Analysis: Compare current state to desired quality benchmarks.

The assessment reveals root causes of poor data and guides remediation efforts. It’s important to note that assessing data quality should be an ongoing effort.

Establishing Data Governance Frameworks

Data governance refers to the policies, procedures, and standards used to manage data across its lifecycle. A strong governance framework defines roles, responsibilities, and accountability for data quality.

Key components of data governance include:

• Data Ownership: Assigning responsibility for data assets to individuals or teams.
• Standardization: Creating consistent definitions, formats, and taxonomies.
• Policy Enforcement: Enabling compliance through rules and guidelines.
• Training and Awareness: Educating staff on data quality principles.

Effective data governance ensures that data quality is not an afterthought, rather data should be handled with governance by design – as a built-in process.

Implementing Data Validation Techniques

Data validation involves verifying that data is accurate, clean, and useful before it’s stored or used. These techniques can be applied during data entry, data migration, or integration.

Examples of data validation techniques:

• Format Checks: Ensuring fields match expected formats (e.g., dates, email addresses).
• Range Checks: Validating values fall within acceptable boundaries.
• Lookup Validation: Cross-checking values against known reference tables.
• Duplicate Detection: Identifying and eliminating duplicate entries.

Automating validation rules helps reduce manual errors and increases confidence in data reliability.

Strategies for Maintaining Data Quality

Improving data quality is only half the battle—maintaining it over time is where many organizations fall short. As businesses grow, data sources expand, and new technologies are introduced, the risk of data degradation and poor data quality increases.

Long-term success requires an ongoing commitment to monitoring, collaboration, and investment in the right tools and processes. Below are three key strategies that can help organizations sustain high data quality over time. For more, check out our post on the top 9 ways to maintain data quality.

Regular Data Quality Reviews

Ongoing data quality monitoring is essential for sustaining improvements. Regular reviews involve analyzing data over time to detect new issues and evaluate the effectiveness of remediation efforts.

Best practices for regular reviews include:

• Scheduled Audits: Monthly or quarterly evaluations of data sets.
• Key Performance Indicators (KPIs): Tracking metrics such as error rate, data completeness, and user complaints.
• Reporting Dashboards: Visual tools to monitor quality trends in real time.

These reviews allow organizations to proactively identify emerging problems and respond swiftly.

Promoting a Data-Driven Culture

Improving data quality is not just a technical challenge—it’s a cultural one. Organizations must instill the importance of data integrity throughout their workforce to ensure everyone values and contributes to data quality.

Ways to foster a data-driven culture:

• Executive Buy-In: Leadership must champion data initiatives.
• Employee Training: Offer training on data entry best practices and quality standards.
• Recognition and Accountability: Reward teams for maintaining clean data and hold them accountable for errors.
• Cross-Functional Collaboration: Encourage departments to work together on data-related initiatives.

A data-aware culture ensures that quality becomes an ongoing priority across all levels of the organization.

Utilizing Data Quality Tools

Modern data quality tools provide powerful features to automate, monitor, and enhance data quality. These platforms can significantly reduce the manual workload and ensure consistent standards.

Popular types of data quality tools include:

• Data Profiling Tools: Analyze datasets to identify errors, gaps, and anomalies.
• Data Cleansing Software: Automate the process of correcting or removing inaccurate data.
• Master Data Management (MDM): Create a unified view of key business entities by reconciling duplicates and discrepancies.
• Data Observability Platforms: Monitor, detect, and alert on data quality, reliability, and pipeline health issues across the data stack in real time
• ETL (Extract, Transform, Load) Tools: Integrate and transform data from multiple sources with built-in quality checks.

Selecting the right tools depends on your organization’s size, industry, and specific data challenges.

Actian Helps Businesses Improve and Maintain Data Quality

With the Actian Data Intelligence Platform, organizations can organize and govern their data, share it between departments, perform data discovery, and more. Its federated knowledge graph technology offers advanced metadata management and helps with everything from quick data retrieval to regulation compliance.

Ready to see the difference Actian makes? Schedule a personalized demo 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.