What is data quality monitoring?

Data quality monitoring refers to the assessment, measurement and management of an organization’s data in terms of accuracy, consistency and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making. 

The importance of data quality cannot be overstated, as poor-quality data can result in incorrect conclusions, inefficient operations and a lack of trust in the information provided by a company’s systems. Monitoring can ensure that data quality issues are detected early, before they can impact an organization’s business operations and customers.

In this article, you will learn about the key dimensions of data quality, specific metrics and techniques for monitoring data quality:

Data quality dimensions

The following are the key dimensions of data quality that are typically addressed by data quality monitoring:

  1. Accuracy: This measures the degree of correctness when comparing values with their true representation.
  2. Completeness: It evaluates the extent to which all required data is present and available.
  3. Consistency: This pertains to the uniformity of data across different sources or systems.
  4. Timeliness: It assesses how up-to-date the information is in relation to its intended use.
  5. Validity: This refers to the adherence to predefined formats, rules, or standards for each attribute within a dataset.
  6. Uniqueness: This ensures that no duplicate records exist within a dataset.
  7. Integrity: This helps maintain referential relationships between datasets without any broken links.

Key metrics to monitor

Beyond the dimensions of data quality, there are specific metrics that can indicate quality problems with your data. Tracking these key metrics enables early identification and resolution of issues before they impact business decisions or customer experience.

Error ratio

The error ratio measures the proportion of records with errors in a dataset. A high error ratio indicates poor data quality and could lead to incorrect insights or faulty decision-making. Divide the number of records with errors by the total number of entries to calculate the error ratio.

Duplicate record rate

Duplicate records can occur when multiple entries are created for a single entity due to system glitches or human error. These duplicates not only waste storage space but also distort analysis results and hinder effective decision-making. The duplicate record rate calculates the percentage of duplicate entries within a given dataset compared to all records.

Address validity percentage

An accurate address is crucial for businesses relying on location-based services, such as delivery or customer support. The address validity percentage measures the proportion of valid addresses in a dataset compared to all records with an address field. To maintain high data quality, it is essential to cleanse and validate your address data regularly.

Data time-to-value

Data time-to-value describes the rate of obtaining value from data after it has been collected. A shorter time-to-value indicates that your organization is efficient at processing and analyzing data for decision-making purposes. Monitoring this metric helps identify bottlenecks in the data pipeline and ensures timely insights are available for business users.

8 data quality monitoring techniques

Here are a few common data quality monitoring techniques you can use to monitor the quality of your data:

Data profiling

Data profiling is the process of examining, analyzing and understanding the content, structure and relationships within your data. This technique involves reviewing data at the column and row level, identifying patterns, anomalies and inconsistencies. Data profiling helps you gain insights into the quality of your data by providing valuable information such as data types, lengths, patterns and unique values.

There are three main types of data profiling: Column profiling, which examines individual attributes in a dataset; dependency profiling, which identifies relationships between attributes; and redundancy profiling, which detects duplicate data. By using data profiling tools, you can gain a comprehensive understanding of your data and identify potential quality issues that need to be addressed.

Data auditing

Data auditing is the process of assessing the accuracy and completeness of data by comparing it against predefined rules or standards. This technique helps organizations identify and track data quality issues, such as missing, incorrect, or inconsistent data. Data auditing can be performed manually by reviewing records and checking for errors or using automated tools that scan and flag data discrepancies.

To perform an effective data audit, you should first establish a set of data quality rules and standards that your data must adhere to. Next, you can use data auditing tools to compare your data against these rules and standards, identifying any discrepancies and issues. Finally, you should analyze the results of the audit and implement corrective actions to address any identified data quality problems.

Data quality rules

Data quality rules are predefined criteria that your data must meet to ensure its accuracy, completeness, consistency and reliability. These rules are essential for maintaining high-quality data and can be enforced using data validation, transformation, or cleansing processes. Some examples of data quality rules include checking for duplicate records, validating data against reference data and ensuring that data conforms to specific formats or patterns.

To implement effective data quality rules, you should first define the rules based on your organization’s data quality requirements and standards. Next, you can use data quality tools or custom scripts to enforce these rules on your data, flagging any discrepancies or issues. Finally, you should continuously monitor and update your data quality rules to ensure they remain relevant and effective in maintaining data quality.

Data cleansing

Data cleansing, also known as data scrubbing or data cleaning, is the process of identifying and correcting errors, inconsistencies and inaccuracies in your data. Data cleansing techniques involve various methods, such as data validation, data transformation and data deduplication, to ensure that your data is accurate, complete and reliable.

The process of data cleansing typically involves the following steps: Identifying data quality issues, determining the root causes of these issues, selecting appropriate cleansing techniques, applying the cleansing techniques to your data and validating the results to ensure that the issues have been resolved. By implementing a robust data cleansing process, you can maintain high-quality data that supports effective decision-making and business operations.

Real-time data monitoring

Real-time data monitoring is the process of continuously tracking and analyzing data as it is generated, processed and stored within your organization. This technique enables you to identify and address data quality issues as they occur, rather than waiting for periodic data audits or reviews. Real-time data monitoring helps organizations maintain high-quality data and ensure that their decision-making processes are based on accurate, up-to-date information.

Tracking data quality metrics

Data quality metrics are quantitative measures that help organizations assess the quality of their data. These metrics can be used to track and monitor data quality over time, identify trends and patterns and determine the effectiveness of your data quality monitoring techniques. Some common data quality metrics include completeness, accuracy, consistency, timeliness and uniqueness.

To track data quality metrics, you should first define the metrics that are most relevant to your organization’s data quality requirements and standards. Next, you can use data quality tools or custom scripts to calculate these metrics for your data, providing a quantitative assessment of your data quality. Finally, you should regularly review and analyze your data quality metrics to identify areas for improvement and ensure that your data quality monitoring techniques are effective.

Data performance testing

Data performance testing is the process of evaluating the efficiency, effectiveness and scalability of your data processing systems and infrastructure. This technique helps organizations ensure that their data processing systems can handle increasing data volumes, complexity and velocity without compromising data quality.

To perform data performance testing, you should first establish performance benchmarks and targets for your data processing systems. Next, you can use data performance testing tools to simulate various data processing scenarios, such as high data volumes or complex data transformations and measure the performance of your systems against the established benchmarks and targets. Finally, you should analyze the results of your data performance tests and implement any necessary improvements to your data processing systems and infrastructure.

Learn more about data reliability

Metadata management

Metadata management is the process of organizing, maintaining and using metadata to improve the quality, consistency and usability of your data. Metadata is data about data, such as data definitions, data lineage and data quality rules, that helps organizations understand and manage their data more effectively. By implementing robust metadata management practices, you can improve the overall quality of your data and ensure that it is easily accessible, understandable and usable by your organization.

To implement effective metadata management, you should first establish a metadata repository that stores and organizes your metadata in a consistent and structured manner. Next, you can use metadata management tools to capture, maintain and update your metadata as your data and data processing systems evolve. Finally, you should implement processes and best practices for using metadata to support data quality monitoring, data integration and data governance initiatives.

Explore how IBM® Databand® delivers better data quality monitoring by detecting unexpected column changes and null records to help you meet data SLAs. If you’re ready to take a deeper look, book a demo today.

Was this article helpful?

More from Databand

IBM Databand achieves Snowflake Ready Technology Validation 

< 1 min read - Today we’re excited to announce that IBM Databand® has been approved by Snowflake (link resides outside ibm.com), the Data Cloud company, as a Snowflake Ready Technology Validation partner. This recognition confirms that the company’s Snowflake integrations adhere to the platform’s best practices around performance, reliability and security.  “This is a huge step forward in our Snowflake partnership,” said David Blanch, Head of Product for IBM Databand. “Our customers constantly ask for data observability across their data architecture, from data orchestration…

Introducing Data Observability for Azure Data Factory (ADF)

< 1 min read - In this IBM Databand product update, we’re excited to announce our new support data observability for Azure Data Factory (ADF). Customers using ADF as their data pipeline orchestration and data transformation tool can now leverage Databand’s observability and incident management capabilities to ensure the reliability and quality of their data. Why use Databand with ADF? End-to-end pipeline monitoring: collect metadata, metrics, and logs from all dependent systems. Trend analysis: build historical trends to proactively detect anomalies and alert on potential…

DataOps Tools: Key Capabilities & 5 Tools You Must Know About

4 min read - What are DataOps tools? DataOps, short for data operations, is an emerging discipline that focuses on improving the collaboration, integration and automation of data processes across an organization. DataOps tools are software solutions designed to simplify and streamline the various aspects of data management and analytics, such as data ingestion, data transformation, data quality management, data cataloging and data orchestration. These tools help organizations implement DataOps practices by providing a unified platform for data teams to collaborate, share and manage…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters