How to Measure Data Quality

Organizations struggle to maintain good data quality, especially as duplicate, misspelled, inconsistent, irrelevant, overlapping, and inaccurate data proliferates at all levels of an organization. Poor internal and external data quality impacts organizations greatly, but in many cases, these organizations don’t have the right metrics to spot and fix the damage.

In order to measure data quality, it is necessary to understand what it is, what data metrics are used, and what are the best tools and practices in the industry. This guide provides a closer look at how to measure data quality in an actionable way.

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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, checks the quality of information, and runs multiple processes to eliminate data quality errors. The process aims to make data more accurate, correct, valid, complete and reliable.

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The gold standard for data quality is data that is appropriate for all intended operations, decision making, and planning. When data quality strategies are properly implemented, the data is directly aligned with the business goals, objectives and values ​​of the organization.

Data quality metrics

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

Gartner does a good job of explaining the importance of data quality metrics, showing that poor data quality costs organizations an average of $12.9 million each year. Aside from lost revenue, poor data quality complicates operations and data ecosystems and leads to poor decision making, further impacting performance and your bottom line.

To troubleshoot these types of issues, organizations turn to data quality metrics and management. Gartner predicts that by 2022, 70% of organizations will rigorously track data quality levels and improve quality by 60% to significantly reduce operational risk and costs.

Important data quality metrics to consider

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

accuracy

Accuracy is often considered the most important metric for data quality. Accuracy should be measured through source documentation or independent verification techniques. This metric also relates to data state changes that occur in real-time.

consistency

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 critical.

completeness

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

integrity

Known as data validation, data integrity ensures data conforms to business procedures and is characterized by structural data testing. Data transformation error rates—when data is transferred from one format to another and migrated successfully—can be used to measure integrity.

timeliness

Stale data almost always results in poor data quality scores. For example, if old customer contact details are not updated, it can have a significant impact on marketing campaigns and sales initiatives. Stale data can also impact your supply chain or shipping. It is important that all data is updated to meet accessibility and availability standards.

relevance

Data can be of high quality in other ways, but irrelevant to the purpose for which an organization 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 data is relevant is to confirm that the right people have access to the right data sets and systems.

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

Data quality actions you can take

Gartner highlights actions you can take to improve data quality in your organization:

• Understand how data quality impacts business: Make a list of your company’s existing data quality issues and how they affect revenue and other business KPIs, then create data quality improvement plans and select data stewards and analytics leaders so they can begin developing data quality processes.
• Define your data quality standards: Data quality standards must align with your business goals and objectives. So define which data is suitable for use in your company.
• Build a data quality culture in your company: From internal to external operations, ensure that data quality becomes part of your company culture and reaches all levels.
• Profile data: Constantly examine data, identify errors and take corrective action.
• Use data quality dashboards: These technological tools provide everyone involved with a visual insight into data quality and show the full picture of data quality in your organization.
• Set clear responsibilities: Define who is responsible for each data quality process.

Best-in-class data quality tools and software

Datamation explains that data quality tools can help organizations address the increasing data challenges they face. As cloud and edge computing operations increase, data quality tools can analyze, manage, and cleanse data from multiple sources, including databases, email, social media, logs, and the Internet of Things. Leading data quality providers include Cloudingo, Data Ladder and IBM.

cloudingo

Cloudingo is a data quality solution built exclusively for Salesforce. Despite its narrow focus, Salesforce users can use the tool to assess data integrity and data cleansing processes. It can detect human errors, inconsistencies, duplicates and other common data quality issues through automated processes. The tool can also be used for data imports.

IBM InfoSphere QualityStage

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

data conductor

Data Ladder is one of the leading data quality management tools. The flexible architecture offers a wide range of tools to cleanse, reconcile, standardize and ensure that your data is ready for use. The solution integrates with most systems and sources and is easy to use and deploy despite its high level of advancement.

Other top data quality solutions include:

• Informatica master data management: Handles a wide range 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 multiple languages.
• SAS data management: This graphical data quality environment tool manages, integrates and cleanses data.
• Exactly Trillion: As a leader in data integrity, Precisely offers five versions of the plug-and-play application, each with different features.
• TIBCO Clarity: This tool focuses on analyzing and cleaning large amounts of data to create rich and accurate datasets. It works with all major data sources and file types, including tools to profile, validate, standardize, transform, deduplicate, cleanse, and visualize data.

Measuring data quality is crucial for every business today. Many excellent solutions on the market can simplify data quality management. However, organizations must first adopt best practices and embrace the culture of data quality by first learning what they want to measure and how to ensure data quality standards are maintained at all levels over the long term.