The amount of data we deal with has increased rapidly (close to 50TB, even for a small company), whereas 75% of leaders don’t trust their data for business decision-making. Though these are two different stats, the common denominator playing a role could be data quality. With new data flowing from almost every direction, there must be a yardstick or a reference point to measure data quality – enter data quality metrics.

Data quality metrics are quantitative values that reveal the consumability percentage of data in all dimensions. They are like a building inspection report with all the nitty-gritty details of cracks, crevices, and other structural/internal defects, only with deeper insights.

Any data team that wants to ensure data reliability and build trust in data should make quality metrics measurement a periodic activity.

Key Data Quality Metrics

Any numerical metric must fall within a particular range to meet predefined conditions. Here are some data quality metrics and measures an organization should track and why they are important. 

Accuracy: How much of the data in each dataset carries original values? Example: Number of correct email addresses or contact numbers in a customer database. Accuracy is crucial for high reliability within customer communications, targeted campaigns, etc.

Consistency: How much is the data consistent across systems and databases? With multiple tools in place, the chance of contradictions in values increases, especially with use cases such as inventory management, financial reporting, etc. Example: Product price recorded as $100 in one system and $120 in another system.

Timeliness: This is the measure of having up-to-date and reliable data. The timeliness metric is essential for decision-makers to have the latest or near-real-time data available. This metric matters a lot for use cases like order fulfillment, the stock market, and other similar dynamic environments.  

Completeness: The percentage of fields with available data. The completeness metric will help you evaluate and avoid data gaps and ensure that the available information is holistic.

While accuracy, consistency, and timeliness are key data quality metrics, the acceptable thresholds for these metrics to achieve passable data quality can vary from one organization to another, depending on their specific needs and use cases.

There are a few other quality metrics, including integrity, relevance, validity, and usability. Depending on the data landscape and use cases, data teams can select the most appropriate quality dimensions to measure.

Why Are Data Quality Metrics Important?

Data quality metrics help you ensure that data will lead to accurate decisions. They can alert you to potential compliance risks of not meeting regulatory standards. 

To understand the importance of data quality metrics, we need to look more into the negative outcomes of bad data:

High operational costs: Poor quality data leads to wasted resources and additional costs. This can be taken in two ways. 

• It takes resources, labor, and tools to fix bad data. Also, there will be processing and storage costs, which could have been avoided.
• Bad data leads to bad decisions, which lead to lost opportunities, dreadful mistakes, and cost and resource wastage.

Operational inefficiencies: Time goes by fixing data errors, duplication, and incomplete entries. Thus, business users don’t get reports on time and the effects snowball and spread beyond the data team. 

Reputation takes a hit: Customers feel averse toward companies that make data security compromises. Not only does this cause a poor experience for them, but it also erodes their trust and drives them away from the brand, which is difficult to recover from. It doesn’t even take a cyber attack – a simple misspelling or a duplicate email is enough to leave a negative impression.

Data Quality Metrics vs. Data Quality Dimensions

Data quality metrics and data quality dimensions are closely related, but aren’t the same. The purpose, usage, and scope of both concepts vary too. Data quality dimensions are attributes or characteristics that define data quality. On the other hand, data quality metrics are values, percentages, or quantitative measurements of how well the data meets the above characteristics. 

A good analogy to explain the differences between data quality metrics and dimensions would be the following: Consider data quality dimensions as talking about a product’s attributes – it’s durable, long-lasting, or has a simple design. Then, data quality metrics would be how much it weighs, how long it lasts, and the like.

Factors	Data Quality Dimensions	Data Quality Metrics
What is it?	Data quality dimensions are composed of accuracy, completeness, consistency, timeliness, validity, and uniqueness.	Data quality metrics are accuracy metric, completeness metric, consistency metric, timeliness metric, etc.
Purpose	Define what quality means to your organization.	Quantify how much and how well your data will meet these quality dimensions.
Examples	My organization’s data would be accurate, complete, and consistent.	90% accuracy 50% consistency, etc.
Scope	Highly generic	Actionable

Best Practices to Maintain Data Quality

Can data quality metrics alone help you enhance data quality? No. Tracking shows only the progress and shortcomings; you need to take the following steps to improve data quality and sustain them. 

Identify Your Challenges

Every solution starts with a problem. Identify the pressing concerns – missing records, data inconsistencies, format errors, or old records. What is it that you are trying to solve? Turn this challenge into your goal statement and that into a use case. Let’s assume your bottleneck is inaccurate or inadequate inventory reports, which leads to stocking issues. Then, the goal statement should be: making reports reliable by addressing the accuracy errors. Based on this, set up goals for data quality metrics to achieve clean, accurate, and consistent data.

Use Data Quality Tools

There are many tools available to fix or improve data quality, be it open source or cloud-based. They fulfill a variety of requirements, from profiling to cleaning to validation. You could leverage them to automate data quality monitoring, profiling, and quality management. Data quality tools help achieve de-duplication, advanced cleansing, and validation against a set of defined rules.

Maintaining Data Integrity

Data integrity is a part of data quality management – keeping data accurate, complete, and consistent from creation to discard. This stage is critical to safeguard your data from security and compliance risks and requires more than just tools. Go back to your data governance framework and enforce validation rules to prevent unauthorized access. Ask these questions to strengthen it further: Are there any encryption measures in place to protect sensitive data? Is the sensitive data anonymized enough to help business use cases without revealing private info? Is the metadata being updated and managed well? All these questions are essential to ensure that data is used to its fullest extent in a secure manner.

Make It Accessible to Everyone

Where there’s easy data accessibility, there’s collaboration and consistent usage. Breaking silos and making data accessible beyond teams will bring more stakeholders into the picture, offering everyone a holistic view. When every user is made accountable for data they access, fewer errors and inefficiencies take place. And having such a centralized place reduces the chances of fragmentation, consistency errors, and duplication.  

Monitor and Refine It

Data quality management is more of a marathon than a sprint. Measure your data quality metrics using trackers and data quality dashboards. Set up automated alerting when there is an anomaly, notifying relevant teams. Occasionally, review the progress, make changes, and ensure they are aligned with current business goals. 

Final Thoughts

Having good-quality data isn’t just a nice-to-have – it’s indispensable. Data quality metrics are one way to look back and assess the data health, thus avoiding errors, cost wastage, delays, and bad decisions. Adding data quality metrics best practices like metadata management, data audits, and automated quality checks can strengthen your data management further, creating a stronger foundation for every data initiative.