Businesses today are increasingly dependent on an ever-growing flood of information. Whether it is sales records, financial and accounting data, or sensitive customer information, the accuracy and adequacy of a company’s ability to measure data quality is critical. If portions of that information are inaccurate or incomplete, the effect on the organization can range from embarrassing to catastrophic.
Data quality is crucial for any organization that wants to leverage data to drive business value. However, many companies struggle to define, measure, and improve their data quality. This article will explain what data quality metrics are, why they are important, and provide examples of key metrics to track.
What is Data Quality?
Data quality refers to how accurate complete consistent, and timely data is. High-quality data correctly represents the real-world entities and events it describes. On the other hand, poor quality data contains errors, duplicates, inconsistencies, and gaps that create distrust in the data.
Some common data quality issues include:
- Inaccurate or outdated data values
- Missing or incomplete data
- Duplicate or overlapping records
- Inconsistencies across systems and sources
- Invalid or incorrect formats
These types of problems lead to operational inefficiencies, compliance risks, and unreliable data analysis. Therefore, maintaining high data quality is crucial for organizations that want to extract value from their data
Why Measure Data Quality?
Without measuring data quality, organizations lack visibility into the reliability of their data. Some key reasons to track data quality metrics include
- Identify data quality issues – Metrics help uncover problems to prioritize fixing.
- Monitor improvements – Trends show if efforts are improving data over time.
- Benchmark performance – Compare across systems, teams, or companies.
- Inform data consumers – Metrics indicate if data can be trusted.
- Enable data governance – Standards and policies can enhance quality.
In essence, data quality measurement provides the visibility required to manage and improve data effectively.
What Are Data Quality Metrics?
Data quality metrics quantify attributes that determine the fitness of data for business purposes. Each metric focuses on a specific aspect of quality.
Common data quality metrics include:
Accuracy
Accuracy measures how closely data matches the real-world entity or event it represents. It is often verified against authoritative sources.
Example metric: Percentage of accurate customer contact details
Completeness
Completeness refers to the degree to which data contains the expected attributes for an entity or event.
Example metric: Percentage of patient records with complete diagnosis details
Timeliness
Timeliness indicates how current or up-to-date data is. This could be measured in latency from event occurrence.
Example metric: Average latency between transaction time and appearance in reporting database
Consistency
Consistency metrics quantify if data matches across systems and sources. This includes uniformity of formats, definitions, and business rules.
Example metric: Percentage of records with consistent customer name spellings
Conformity
Conformity measures how well data complies with required formats, datatypes, standards, and rules.
Example metric: Percentage of address fields conforming to international postal standards
Uniqueness
Uniqueness metrics identify the level of duplicate or overlapping records within or across datasets.
Example metric: Percentage of unique customer email addresses
Integrity
Data integrity metrics determine if expected relationships between data elements are maintained consistently.
Example metric: Percentage of customer orders referencing valid products
Coverage
Coverage quantifies gaps or missing data elements within a dataset.
Example metric: Percentage of product records with price information
Validity
Validity metrics evaluate if data values fall within expected ranges and meet referential integrity constraints.
Example metric: Percentage of valid product category codes
How to Measure Data Quality Metrics
Once key data quality metrics are defined, the next step is determining how to measure them. Here are some common measurement approaches:
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Automated validation rules: Configurable tests that run on periodic schedules or on-demand to assess data quality metrics. For example, completeness checks for null values.
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Statistical profiling: Analyzing datasets to understand value patterns, ranges, distributions, and relationships between attributes. This can uncover data quality issues.
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Data quality monitoring: Ongoing processes that calculate metric scores on live databases to detect issues as they emerge.
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Data quality benchmarking: Comparing metric values over time or across data sources to quantify improvements and uncover gaps.
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Data quality dashboards: Data visualizations that track key metric values to provide visibility into data health.
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Data quality reports: Regular reports that analyze metric trends and performance compared to benchmarks and targets.
Sample Data Quality Metrics and KPIs
Here are some examples of data quality metrics that companies may want to measure, along with sample KPIs:
| Metric | Description | Sample KPI |
|---|---|---|
| Accuracy Rate | Percentage of records with accurate values | 95% accurate customer email addresses |
| Completeness Rate | Percentage of complete records | 85% of product records contain pricing data |
| Freshness | Average time since data was last updated | Customer address data updated within last 3 months |
| Consistency Rate | Percentage of matched values across sources | 90% consistent customer names across CRM and ERP systems |
| Conformity Rate | Percentage of values adhering to standards and rules | 99% valid product ID codes |
| Uniqueness Rate | Percentage of distinct values for specific fields | 100% unique customer email addresses |
| Integrity Rate | Percentage of referenced values matching source records | 95% of order line items reference a valid product |
| Coverage Rate | Percentage of expected fields containing values | 80% coverage of optional middle name field in customer records |
| Validity Rate | Percentage of values within expected ranges | 99% of order totals fall within expected limits |
Best Practices for Data Quality Measurement
Here are some tips for an effective data quality measurement program:
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Focus on metrics aligned to business needs and data use cases. Avoid measuring everything.
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Automate data quality checks where possible for scalability.
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Establish data quality KPIs with target values to drive performance.
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Monitor metric trends over time to quantify improvements.
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Prioritize fixing metrics with the biggest business impact.
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Use dashboards and reports to share metrics across teams.
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Include data quality metrics in data SLAs for accountability.
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Assess metrics periodically to ensure they provide value.
Why Data Quality Matters
7 Metrics to Assess Data Quality
To measure data quality – and track the effectiveness of data quality improvement efforts – you need, well, data. What does data quality assessment look like in practice? Following are seven examples.
| Metric | Definition | How to calculate |
|---|---|---|
| Ratio of Data to Errors | How many errors do you have relative to the size of your data set? | Divide the total number of errors by the total number of items. |
| Number of Empty Values | Empty values indicate information is missing from a data set. | Count the number of fields that are empty within a data set. |
| Data Transformation Error Rates | How many errors arise as you convert information into a different format? | How often does data fail to convert successfully? |
| Amounts of Dark Data | How much information is unusable due to data quality problems? | Look at how much of your data has data quality problems. |
| Email Bounce Rates | What percentage of recipients didn’t receive your email because it went to the wrong address? | Divide the total number of emails that bounced by the total number of emails sent, then multiply by 100. |
| Data Storage Costs | How much does it cost to store your data? | What is your data storage provider charging you to store information? |
| Data Time-to-Value | How long does it take for your firm to get value from its information? | Decide what “value” means to your firm, then measure how long it takes to achieve that value. |
Let’s look at each of these metrics in a bit more detail:
What Is Data Quality?
Data quality refers to the ability of a set of data to serve an intended purpose. Today’s businesses are using data to generate value in a myriad of different ways, but they simply can’t accomplish their objectives using low-quality data. We often describe data quality in terms of the following four dimensions:
- Completeness refers to the presence of all required information within a dataset. For example, if the customer information in a database is required to include both first and last names, any record in which the first name or last name field is not populated is considered incomplete.
- Validity describes the conformance of data to business rules such as the format (e.g. number of digits), allowable data types (integer, floating-point, string, etc.), and range (minimum and maximum values). For example, a telephone number field that contains the string ‘1809 Oak Street’ is not valid.
- Timeliness refers to whether the information is sufficiently up-to-date for its intended use. Is the correct information available when needed? If a customer has notified your company of an address change, but that information is not available when billing statements are processed, that indicates a problem with the timeliness of the data.
- Consistency is present when all representations of a particular item across multiple data stores match. If customer information is stored in both the ERP system and a separate CRM system, for example, it’s important that the address, order history, and other important details match.
What is Data Quality and Why is it Important?
What are data quality metrics?
Data quality is defined by metrics that include accuracy, consistency, completeness, and reliability of data. These metrics help ensure that data is suitable for its intended use and meets the needs of data consumers. Discover how data leaders use automation and data observability to enhance data quality.
What is the difference between data quality dimensions and metrics?
Description of the difference between data quality dimensions and metrics. One valuable way to reason about data quality dimensions is to identify whether a dimension is tied to a task or use case. If a dimension is independent of use case, that’s called an intrinsic data quality dimension (or a task-independent dimension).
What makes a good data quality metric?
“A good data quality metrics inspires the team and motivates the team, and it allows us to have a more cross-functional conversation with our stakeholders about what we’re doing and why.” For SurveyMonkey, accuracy, consistency, and completeness are also key data quality metrics measured by the team. “We also think about data variety.
What is a good metric for data integrity?
An appropriate metric for data integrity would be the number of orphan records present in a database. Your organization must have some kind of data quality assessment plan in place. The seven metrics we’ve discussed here offer a good starting point. For a deeper dive into data quality measurement, read our free eBook: 4 Ways to Meaure Data Quality
