Accurate and reliable data is critical to informed decision-making, yet companies often struggle to achieve high standards of quality. Research shows that 75% of business executives do not fully trust their business data. This lack of confidence can lead to ill-advised decisions, inefficient processes, and missed opportunities.
However, you can use data quality metrics to help address the challenges of improving the reliability and precision of your data. Read on to learn how to measure data quality and regain confidence in your data.
What Are Data Quality Metrics?
Data quality metrics are quantifiable measures that you can use to gauge whether a given data set is reliable and trustworthy enough to use for your business decisions. Common data quality metrics to measure include the following:
Accuracy Metrics
- Error rate
- Percentage of correct values
- Number of data points outside acceptable ranges
Completeness Metrics
- Percentage of missing values
- Fill rate (percentage of fields that contain data)
- Record completeness ratio
Consistency Metrics
- Number of conflicting records
- Percentage of data adhering to defined business rules
- Cross-field or cross-system consistency rate
Timeliness Metrics
- Average time lag between data creation and availability
- Percentage of real-time data updates
- Data freshness score
Validity Metrics
- Percentage of data conforming to specified formats
- Number of records failing validation rules
- Data type consistency rate
The Importance of Data Quality Metrics
Data metrics are crucial for verifying the integrity of your organization’s data assets. By defining and measuring the most appropriate metrics, you can gain valuable and trustworthy insights.
Let’s take a closer look at some of the most important benefits of establishing data metrics for quality control:
- Enables Reliable Decision-Making: With consistently reliable data, you can trust your insights and make important decisions with confidence.
- Improves Operational Efficiency: Tracking and monitoring data quality metrics lets you identify and resolve data issues before they can propagate across your operations. This helps optimize your workflow and ensure your team’s time and skills are being applied in the most profitable ways.
- Enhances Data Governance and Compliance: Measuring data metrics allows you to demonstrate that your data practices comply with legal regulations and industry standards, helping you avoid potential penalties and reduce legal risks.
- Ensures Data Integration and Interoperability: Applying consistent data quality checks across all your sources and systems enables data to be integrated and shared with confidence, promoting better collaboration and decision-making across departments.
- Identifies and Resolves Data Quality Issues: Constantly tracking data quality metrics allows you to proactively identify and fix issues well before they turn into significant problems, helping you keep business operations running smoothly.
- Allows Continuous Improvement: By monitoring and analyzing data quality over time, you can identify patterns, trends, and the root causes of data quality issues.
- Increases Trust and Confidence in Data Assets: When high data quality can be verified by relevant data metrics, it strengthens trust and confidence in your organization’s data assets.
- Maximizes Returns on Data Investments: Investing in data metrics provides maximum value by supplying credible and actionable information for business intelligence, analytics, and decision-making processes.
Data Quality Dimensions vs. Data Quality Metrics vs. KPIs
Data quality metrics are frequently conflated with data quality dimensions and key performance indicators. Understanding the subtle differences between these concepts helps you to more accurately evaluate, manage, and assess data quality. Let’s explore these three concepts in more detail:
Data Quality Dimensions
Data quality dimensions are general classifications or aspects of data quality that you can focus on to ensure your data is fit for purpose. They serve as a framework for assessing the more specific aspects of data quality metrics. Some common data quality dimensions include validity, accuracy, and completeness.
Data Quality Metrics
Data quality metrics quantify and assess the progress of various data quality dimensions over time. Examples include the percentage of complete records, the number of duplicates, and the count of missing values.
Key Performance Indicators (KPIs)
KPIs are high-level metrics that tie in to your organization’s strategic goals. They are used to quantify the performance of particular plans or processes. In the context of data quality, KPIs include overall data quality score, cost savings from improved data quality, and customer complaints caused by data errors.
The 8 Essential Data Quality Metrics
Understanding how to measure data quality is essential to ensuring you have full visibility into the state of your data and that you can easily identify areas for improvement. Let’s take a look at eight key metrics that cover various important dimensions of data quality:
1. Data to Errors Ratio
This represents the ratio of erroneous data records (incomplete, missing, redundant) compared to the overall data volume. It is calculated by dividing the number of known data errors by the total size of the data set.
2. Number of Empty Values
The number of empty values metric shows the degree of incompleteness in the data set across important fields. You can measure it by counting the number of fields or records with null or missing values.
3. Amount of Dark Data
This quality metric measures the volume of collected data that is not being actively analyzed or used. Work this figure out by measuring the total size of data stored on your servers that is not currently informing decision-making.
4. Data Storage Costs
Data storage costs capture the total charges from data storage providers. This metric is significant because, if data usage remains constant but costs rise, it may indicate unnecessary or low-quality data is being stored, driving expenses higher.
5. Data Time-to-Value
Data time-to-value refers to the length of time elapsed from when data is initially collected until it provides tangible business benefits through insights. A long time-to-value can hint at quality problems where data needs cleaning before analysis.
6. Data Transformation Errors
Data transformation errors represent issues when converting data between formats, often indicating underlying data quality flaws. This metric is determined by adding up the number of failed data transformation attempts. A larger number points to systematic data quality problems like invalid or inconsistent data types that require investigation and corrective action to prevent faulty transformations.
7. Data Operation Failure Rate
This rate represents the percentage of data-related operations or functions that fail due to data quality issues. To calculate it, divide the number of failed data operations by the total number of attempted data operations and multiply the result by 100. This metric can apply to various data processes such as data transfers, API calls, database queries, or any other data-centric operations. A high failure rate may indicate underlying data quality issues such as invalid formats, inconsistent data types, or outdated information.
8. Cost of Quality
This quantifies the investment needed to ensure your data remains high-quality. It represents the total costs associated with all your data quality management activities, including data collection, validation, cleaning, and monitoring processes.
Challenges in Implementing Data Quality Metrics
Organizations often face obstacles in ensuring the highest data quality. Here are some common challenges you might come across:
- Data Complexity and Variety: Modern organizations work with data from multiple sources, in different formats, and with varying levels of complexity. Establishing consistent metrics across such a diverse landscape requires careful planning and standardization.
- Legacy Systems and Technical Debt: Many companies rely on outdated systems not developed with data quality in mind. Integrating metrics into these can be technically complex and resource-heavy.
- Organizational Silos and Lack of Collaboration: Ensuring data quality is an important concern across your company, but data silos can hamper collaboration and make it difficult to implement the same data quality metrics throughout your systems.
- Limited Resources and Constraints: Establishing and maintaining quality metrics can be resource-intensive, requiring specialized tools, skilled teams, and ongoing tracking and improvement efforts.
- Continuous Data Evolution: Data is a dynamic asset in a process of continuous development as your business needs, processes, and systems evolve.
Elevate Your Data Quality with Boomi’s Powerful Capabilities
Access to reliable, high-quality data is vital for making sound decisions. However, maintaining data quality across an array of disparate platforms can be challenging. The solution is to deploy an intelligent integration platform that breaks down these barriers.
Boomi’s DataHub service helps organizations like yours improve data quality metrics.
Boomi’s solution:
- Eliminates data silos: Boomi’s platform connects systems within and outside of organizations, breaking down silos to streamline operations.
- Ensures integrations are adaptable: Boomi’s self-managing capabilities ensure integrations are adaptable, allowing them to evolve without requiring extra support.
- Accelerates innovation: Boomi’s low-code interface automates routine integration tasks, empowering you to focus on innovating faster.
- Secures sensitive information: Boomi implements industry-leading security measures to protect valuable customer and business data.
- Grows with your needs: Boomi’s self-scaling architecture supports increasing data volumes and expanding use cases, eliminating barriers to growth.
- Uses collective knowledge: With over 100,000 collaborators, Boomi offers a vast ecosystem for guidance and technical support.
Discover how to improve your data quality metrics, identify and correct errors, gain real-time insights throughout your enterprise with Boomi Integration Flex.