In our data-driven world, organizations are obsessed with gathering all kinds of data to help them make strategic decisions and gain competitive insights. However, simply gathering more data doesn't guarantee better outcomes. Poor-quality data can lead to misleading insights, costly errors, and noncompliance with regulations, undermining the very purpose of data-driven decision-making. You need high-quality data to make accurate and effective decisions.

A data quality framework provides a structured, continuous approach to measuring and improving the quality of your organization's data. Instead of cleaning up data once, you implement processes and standards that consistently maintain data quality over time. A mature data quality framework ensures the way your organization collects, processes, stores, and uses data consistently results in accurate, reliable, and fit-for-purpose information.

This article explains how to track the quality of your data using Secoda's Data Quality Score (DQS) and how to use this information to mature your data quality framework and thus improve the quality of your data over time.

Measuring data quality

The outcome of a well-designed data quality framework that's been implemented effectively is high-quality data. If you want to assess the maturity of your data quality framework, you need to measure the quality of your data. Turning data quality into a measurable value helps you objectively assess the current state of your data quality, identify areas for improvement, justify investments in data quality initiatives, and track improvements over time.

One way to quantify data quality is with a data quality score. Secoda's DQS offers a framework for assigning a numerical value to your data's quality and comparing it with pre-established benchmarks that align with your organization's goals and industry standards. Secoda's DQS helps you do that without having to reinvent the wheel. It quantifies and evaluates data quality across four dimensions:

• Stewardship evaluates the clarity and implementation of data governance roles. Have you appointed data stewards for accountability, compliance with quality standards, and proactive issue resolution to foster a culture of data ownership and continuous improvement?
• Usability assesses how easily data can be accessed, understood, and used. Is data presented clearly and consistently with adequate documentation and support to enable effective decision-making and analysis?
• Reliability evaluates the consistency and dependability of your data. How complete is your data, and are there any null values? Also, how up-to-date (fresh) is your data—do you have timely updates and synchronization so your organization can make decisions and operate with relevant data?
• Accuracy evaluates the correctness and precision of data. Do you have adequate validation processes and checks to maintain data integrity and reliability?

A high score across all dimensions indicates that your organization has a mature data quality framework. Lower scores in specific dimensions highlight where your framework is lacking. Regularly measuring your DQS allows you to measure the impact of any changes you make to your data quality framework on data quality.

Keep in mind that stewardship and reliability scores reflect the effectiveness of your data management processes, while usability and accuracy scores measure the outcome of those processes and whether they are providing high-quality, actionable data. The impact of changes to your data quality framework will therefore show up later in your usability and accuracy scores.

Improving your data quality framework

Once you know your DQS, there are some best practices you can follow to systematically improve your data quality and the framework that supports it.

Stewardship: Fostering a culture of data quality

To enhance data stewardship, assign ownership and responsibility for different data assets to specific individuals or teams with relevant domain expertise. Someone who understands the context, significance, and potential quality issues of a specific data asset can identify subtle quality issues that those less familiar with the data's context might overlook. Data stewards' deep understanding of the data's origin, use, and implications also allows them to define DQS standards that align with your organization's and industry's needs.

Clear data governance policies support data stewards and enhance overall data quality. Policies should outline guidelines for data access, usage, and security to ensure compliance with regulatory requirements and promote accountability at all levels.

Also implement a feedback loop through regular surveys, dedicated channels for reporting data issues, and periodic data quality reviews. Continuous feedback helps identify potential issues early, informs data quality processes, and ensures that data management practices evolve to meet the changing needs of the organization. Leverage the domain expertise of data stewards to interpret and act on user feedback in their domains.

Usability: Ensuring data accessibility and comprehension

Making data easily accessible and understandable across the organization helps people use data efficiently and reduces the risk of misinterpretation.

To enhance usability, implement a comprehensive metadata management strategy. This involves creating and maintaining detailed descriptions for data assets, columns, and schemas:

• Clear, concise overviews of data assets allow users to quickly identify data sources relevant to their needs.
• Column descriptions with detailed explanations of individual data fields ensure accurate interpretation and use of data elements.
• Schema descriptions document the overall structure and relationships in data sets for a deeper understanding of data context and relationships.

Implementing standardized templates for these descriptions can significantly improve consistency and readability. For instance, a resource description template might include fields for the data source, update frequency, and primary use cases. Column descriptions could include the data type, valid values, and business context.

Also consider establishing a centralized metadata repository accessible to all stakeholders that serves as a single source of truth for data definitions, lineage, and usage guidelines. Automated documentation processes can help maintain accuracy and reduce the manual effort of keeping such a repository updated. Tools that automatically generate and update schema documentation, data dictionaries, and entity-relationship diagrams ensure consistency and save valuable time for data teams.

Reliability: Ensuring consistent and timely data updates

Maintaining consistent and up-to-date data across the organization builds trust in data assets and ensures that decision-makers have access to the most current information.

To enhance reliability, implement a comprehensive system of checks and monitoring processes that cover various aspects of data integrity. For instance, it would verify the absence of null values where they're not allowed, ensure data is in the correct format, and validate that values fall in expected ranges. While most database systems automatically enforce uniqueness for primary keys, it's still valuable to include checks for uniqueness in other fields where it's a business requirement.

Also consider establishing clear freshness metrics for different data types and sources. For instance, financial transaction data might require updates within minutes, while other types of data may have less stringent timeliness requirements. Aim for a high percentage of data freshness—typically 95 percent or higher—as a benchmark, adjusting based on specific business needs and industry standards.

Ensuring pipeline reliability is another key aspect of maintaining data quality. Develop robust data pipelines with built-in error handling and retry mechanisms to minimize disruptions. Implement monitoring and alerting systems to quickly identify and address pipeline failures or delays. A centralized dashboard for monitoring data freshness and pipeline performance across all data assets can provide valuable insights and help you manage data reliability issues proactively.

Lastly, establish a clear communication protocol to inform stakeholders about data update statuses and potential delays. This transparency helps manage expectations and maintain trust in the quality of your data.

Accuracy: Ensuring data correctness and precision

One way to improve accuracy is to implement a comprehensive system of checks and validation processes that ensure data values are correct, valid, and precise. Use format validation to ensure data conforms to predefined structures, such as validating email addresses, phone numbers, and date fields; use range checks to flag values outside expected boundaries, like unrealistic ages or percentages; and use consistency checks to verify data across related fields or tables to ensure totals match line items and dates follow logical sequences.

Historical trend analysis and cross-reference validation further bolster accuracy. Use algorithms to detect anomalies in numerical values based on historical trends, and validate data against authoritative sources or related data sets. For instance, compare product codes against official catalogs or verify addresses with postal service databases.

Lastly, develop a suite of data quality rules covering these aspects for various data types. Implement automated data profiling tools for continuous monitoring and reporting. Establish a regular process for data cleansing and correction, involving both automated fixes for simple issues and manual review for complex problems. Create dashboards to visualize accuracy metrics and trends over time and manage data quality proactively.

Refining your data practices

Measuring the stewardship, usability, reliability, and accuracy of your data quantitatively lets you identify areas in your data processes that can be improved to ensure better data quality.

‍Secoda's Data Quality Score helps you assess your data quality and refine your data practices so that your organization's data can be a strategic asset. To learn more, see the Secoda blog series about DQS or book a demo.