Data Quality Metrics & Measures
What You Need to Know
What Is Data Quality?
Data quality refers to the utility of a dataset and how easily it can be processed and analyzed for other uses. It ensures that your organization’s data is fit for purpose. Data quality is essential to making informed decisions, driving innovation and achieving business goals.
What Are Data Quality Metrics?
Data quality metrics are the tools that organizations use to measure and improve the quality of their data. By choosing the right metrics and using them to evaluate data, organizations can ensure that they have the high-quality data they need. Establishing a baseline for data quality by tracking metrics can also help ensure data integrity, identify failure points and determine improvement targets. You may also hear the terms “data accuracy metrics” or “data integrity metrics” when referring to data quality metrics.
Common Data Quality Metrics
Common data quality metrics include accuracy, completeness, consistency, timeliness, validity, duplication and uniqueness. Here’s a breakdown of each:
Accuracy: Does your data reflect the real-world object and / or events it is intended to model? Using accuracy as a metric helps ensure this is the case. Data accuracy measures include how well the values agree with an information source that is known to be correct. This helps you maintain correct customer, financial and product / supply chain information.
Completeness: Does your data contain all required records and values? Determining the completeness of your data ensures that you have all the information you need to run quality analytics and AI. It can help you create comprehensive customer profiles and capture complete historical information. It can also help inform analysis and forecasting.
Consistency: Do your data values draw from many sources that conflict with each other? Checking for consistency determines if your data is in the same data format or structure for effective data integration. It’s important to note that consistency does not need data to be accurate or complete but instead is a separate metric. It helps ensure consistent product categorization and prevent duplicate records. It also helps maintain consistent definitions and codes for analysis.
Timeliness: Is your data updated frequently or in real time to support the needs of your users? The timeliness metric keeps your data accurate, accessible and available for your analytics and AI initiatives. This provides up-to-date information for customer programs, operational data and supply chain management.
Validity: Does your data conform to defined business rules and meet allowable parameters? Validity helps verify that your data is usable for the purpose you need. This could include credit card transactions, shipping addresses, legal purposes, contracts, etc.
Duplication: Does your data have multiple representations of the same data objects within a data set? The duplication metric helps avoid multiple representations of an entity across your systems, which poses vulnerabilities and risks to your data. Preventing duplications can save you from having multiple records for your customers or products.
Uniqueness: Can every record be uniquely identified and accessed within your data set and across applications? Looking at uniqueness can help make sure no record exists more than once within a data set, even if it exists in multiple locations. This helps maintain unique identifiers for products, customers and employees within your organization.
Lineage: Where did the data come from? The lineage metric clarifies how data is processed, modified or combined as it moves through different systems, applications or processes. It enables organizations to identify and rectify data quality issues early. This prevents the propagation of errors to downstream processes.
How to Choose Data Quality Metrics
First, you need to consider your specific organizational goals. Identify the key business processes related to those goals that rely on data. Then, you should determine the impact of data quality on those processes and their outcome. Select metrics that will evaluate the data that is most critical to that process. For example, if your goal is to run a successful marketing campaign, accuracy, uniqueness and timeliness of your data might be the most relevant to focus on having an up-to-date customer profile. But if your goal is to improve efficiency in your supply chain, you might want to evaluate data lineage, duplication and consistency to get accurate product data.
How to Collect Data Quality Metrics
Begin by collaboratively crafting a data quality plan that involves key stakeholders, including business owners. Define the objectives of the data quality assessment, considering your specific business needs.
Once you have established your plan and goals, you can select the right metrics to track. This requires engagement with business owners to identify the critical data sources that need to be measured for quality. Their insights will help ensure that the selected sources align with the organization's operational needs and strategic priorities.
Then, you need to develop data quality tests based on your chosen metrics. This is where business owners can provide valuable context about the significance of certain data attributes, guiding the creation of relevant tests. Their domain expertise can help identify anomalies that might not be clear from a purely technical standpoint. This will also inform how data quality metrics will be presented to business owners in an easily understandable way.
Finally, set up a continuous process to monitor data quality metrics. This will not only identify data quality issues, but it will also provide insights into recurring patterns and trends. This information is essential for iterative improvement initiatives. By combining automated data quality testing with ongoing monitoring, you can create a robust framework that protects your data integrity. This will help your organization make informed decisions and stay ahead of the competition.
How to Improve Data Quality
Once you have data quality measures and processes in place, you can start to refine the areas that aren't strong. You can do this by identifying and fixing obvious data errors by manually inspecting your data or using automated tools. Once you have identified the errors, you can fix them by deleting the bad data, correcting the errors or filling in the missing values.
Another way to improve data quality is to put data quality procedures in place. This includes data integrity measures like creating data quality standards, developing data quality checklists and implementing data quality controls. Data quality procedures will help you to prevent data errors from happening in the first place.
Finally, you may want to consider investing in data quality tools. These tools can help you improve data quality more efficiently and effectively by automating tasks and using artificial intelligence (AI).
Data Quality Resources
Free Trial: Informatica Cloud Data Quality
Webpage: Data Quality and Observability
Analyst Report: 2022 Gartner® Magic Quadrant™ for Data Quality Solutions1
1 Gartner® Magic Quadrant™ for Data Quality Solutions, Ankush Jain, Melody Chien, November 1, 2022 GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.