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Organizations struggle to maintain good data quality, especially as duplicated, misspelled, inconsistent, irrelevant, overlapping and inaccurate data proliferate at all levels of an organization. Poor internal and external data quality severely affects businesses, but in many cases, these organizations do not have the right metrics in place to notice and correct the damage.

To measure data quality, it’s necessary to understand what it is, what data metrics are used, and what the best tools and practices are in the industry. This guide offers a closer look at how to measure data quality in a way that is actionable.

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What is data quality?

Data Ladder defines data quality management as the implementation of a framework that continuously profiles data sources, verifies the quality of information and executes several processes to eliminate data quality errors. The process is designed to make data more accurate, correct, valid, complete and reliable.

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The gold standard for data quality is data that is fit to use for all intended operations, decision-making and planning. When data quality strategies are implemented correctly, data becomes directly aligned with the company’s business goals, targets and values.

Data quality metrics

Data quality metrics determine how applicable, valuable, accurate, reliable, consistent and safe the data your organization uses is.

Gartner explains the importance of data quality metrics well, revealing that poor data quality costs organizations an average of $12.9 million every year. Beyond revenue losses, poor data quality complicates operations and data ecosystems and leads to poor decision-making, which further affects performance and your bottom line.

To revert these kinds of issues, organizations turn to data quality metrics and management. By 2022, Gartner predicts that 70% of organizations will rigorously track data quality levels, improving quality by 60% to reduce operational risks and costs significantly.

Key data quality metrics to consider

Depending on your industry and business goals, specific metrics may need to be in place to determine if your data is meeting quality requirements. However, most organizational data quality can and should be measured in at least these categories:


Accuracy is often considered the most critical metric for data quality. Accuracy should be measured through source documentation or independent confirmation techniques. This metric also refers to data status changes as they happen in real-time.


Different instances of the same data must be consistent across all systems where that data is stored and used. While consistency does not necessarily imply correctness, having a single source of truth for data is vital.


Incomplete information is data that fails to provide the insights necessary to draw needed business conclusions. Completeness can be measured by determining whether or not each data entry is a “full” data entry. In many cases, this is a subjective measurement that must be performed by a data professional rather than a data quality tool.


Known as data validation, data integrity ensures that data complies with business procedures and excels in structural data testing. Data transformation error rates — when data is taken from one format to another and successfully migrated — can be used to measure integrity.


Out-of-date data almost always leads to poor data quality scores. For example, leaving old client contact data without updates can significantly impact marketing campaigns and sales initiatives. Outdated data can also affect your supply chain or shipping. It’s important for all data to be updated so that it meets accessibility and availability standards.


Data may be of high quality in other ways but irrelevant to the purpose for which a company needs to use it. For example, customer data is relevant for sales but not for all top-level internal decisions. The most important way to ensure the relevancy of data is to confirm that the right people have access to the right datasets and systems.

What tools can you use to measure data quality?

There are many good data quality solutions and tools in the market today. Some take holistic approaches and others focus on certain platforms or specific data quality tools. But before we dive into some of the best in the industry, it’s essential to understand that data quality solutions only work when they’re partnered with a strong data quality culture.

Data quality actions you can take

Gartner reveals actions you can take to improve data quality in your business:

  • Understand how data quality impacts business: Make a list of your organization’s existing data quality issues and how they impact revenue and other business KPIs, then establish data quality improvement plans and select data stewards and analytic leaders so they can begin developing data quality processes.
  • Define your data quality standards: Data quality standards need to be aligned with your business goals and targets, so define what data is fit for use for your organization.
  • Build a data quality culture across your business: From internal to external operations, ensure data quality becomes part of your business culture and reaches all levels.
  • Profile data: Examine data constantly, identify errors and take corrective actions.
  • Use data quality dashboards: These technological tools provide visual insight into data quality for all stakeholders, and they reveal the full data quality picture as it happens in your organization.
  • Set clear responsibilities: Define who is responsible for each data quality process.

Top data quality tools and software

Datamation explains that data quality tools can help companies deal with the increasing data challenges they face. As cloud and edge computing operations grow, data quality tools can analyze, manage and scrub data from different sources, including databases, email, social media, logs and the Internet of Things. Leading data quality vendors include Cloudingo, Data Ladder and IBM.


Cloudingo is a data quality solution that is strictly designed for Salesforce. Despite its narrow focus, those using Salesforce can assess data integrity and data cleansing processes with the tool. It can spot human errors, inconsistencies, duplications and other common data quality issues through automated processes. The tool can also be used for data imports.

IBM InfoSphere QualityStage

IBM InfoSphere QualityStage offers data quality management for on-premises, cloud or hybrid cloud environments. It also provides data profiling, data cleansing and management solutions. Focusing on data consistency and accuracy, this tool is designed for big data, business intelligence, data warehousing and application migration.

Data Ladder

Data Ladder is one of the leading data quality management tools. Its flexible architecture provides a wide array of tools to clean, match, standardize and assure your data is fit for use. The solution can integrate into most systems and sources, and it’s easy to use as well as deploy despite being highly advanced.

Other top solutions for data quality include:

  • Informatica Master Data Management: Handles a wide array of data quality tasks, including role-based capabilities and artificial intelligence insights.
  • OpenRefine: Formerly known as Google Refine, this is a free, open-source tool for data and big data quality management. It is also available in several languages.
  • SAS Data Management: This graphical data quality environment tool manages, integrates and cleans data.
  • Precisely Trillium: A leader in the data integrity space, Precisely offers five versions of the plug-and-play application, each with different capabilities.
  • TIBCO Clarity: This tool focuses on analyzing and cleansing large volumes of data to produce rich and accurate data sets. It works with all major data sources and file types, including tools for profiling, validating, standardizing, transforming, deduplicating, cleansing and visualizing data.

Measuring data quality is key to every business today. Many excellent solutions out in the market can simplify data quality management. However, companies must first adopt best practices and embrace the culture of data quality, first learning what they want to measure and how they will ensure data quality standards are maintained for the long run at all levels.

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