Data Quality Monitoring: Key Metrics, Benefits & Techniques

The quality of data significantly impacts decision-making in businesses, which, in turn, affects operations. Poor-quality data can result in inaccurate insights, incorrect strategies, and considerable financial losses. For effective data-driven decisions, data quality monitoring is essential.

With proper data quality monitoring, you can identify and address issues like duplicate data, missing values, or outdated information. This helps ensure your data is accurate, consistent, complete, and reliable.

Let’s look into the details of data quality monitoring, including why it’s needed and the different metrics worth monitoring.

What is Data Quality Monitoring?

Data quality monitoring is assessing your organization’s data quality to confirm it meets the required standards and is suitable for its intended use. It comprises tasks such as the examination, measurement, and management of the data regarding reliability, accuracy, and consistency.

Monitoring facilitates the early detection of issues before they impact your organization’s business operations or customers. The process uses various techniques to detect and resolve data quality issues. This guarantees the use of high-quality data for business operations and decision-making.

An example of data quality monitoring in a real-time analytics system may involve real-time accuracy and consistency checks. These checks help verify that the incoming data streams are up-to-date, correct, and synchronized across different platforms.

Data Quality Dimensions

Measuring data quality involves monitoring some key dimensions. With these dimensions, you can gain insights into several data quality aspects to identify and address any issues effectively.

Here are the crucial dimensions of data quality typically addressed by data quality monitoring:

• Accuracy: The accuracy of data measures how closely data values align with the true values, which are real-world entities or events. It is critical for reliable decision-making and analytics.‍
• Completeness: This involves evaluating the extent to which all the necessary data is present. It is essential to monitor completeness since missing data can result in incorrect analyses and decisions.‍
• Consistency: Data consistency pertains to assessing the uniformity of data across different systems and databases over time. Inconsistencies can cause confusion and errors in data usage and interpretation.‍
• Integrity: With data integrity, you can ensure the completeness of the data and that no critical parts are missing or misrepresented. It helps prevent any broken links and maintain referential relationships between datasets.‍
• Validity (or Relevance): The validity of data refers to its adherence to predefined formats, standards, or rules. Valid data is essential for maintaining compliance and accuracy in data processing while also being suitable for its intended business purpose.‍
• Timeliness: This involves evaluating whether the data is up-to-date and available when required to reflect current information. Timely data contributes to making relevant decisions.‍
• Uniqueness: Data uniqueness confirms that each data element is unique within a dataset without duplication. Unique data results in enhanced clarity and reduced redundancy.

Data Quality Metrics Worth Monitoring

Apart from the different data quality dimensions, there are certain metrics you can monitor to identify quality issues with your data. With these metrics, you can attain insights into several aspects of data quality and resolve any issues before they impact your business operations.

Error Ratio

The error ratio is the measure of the proportion of erroneous records in a dataset. Error ratio calculation involves dividing the number of records with errors by the total number of entries. A high error ratio signifies poor data quality, which may result in inaccurate insights or faulty decision-making.

Address Validity Percentage

If your business relies on location-based services, such as customer support or delivery, it is critical to have an exact address. The address validity percentage is a measure of the proportion of valid addresses in a dataset and the total number of records with an address field. It is essential to clean and validate your address data regularly to maintain high data quality.

Duplicate Record Rate

System glitches or human error can result in multiple entries or duplicate records for a single entity. The duplicate record rate is the percentage of duplicate entries within a given dataset when compared to all records. Such duplicates consume unnecessary storage space and also distort analytical results, affecting the decision-making process.

Data Time-to-Value

This metric describes the rate of extracting value from the data after its collection. A shorter time-to-value indicates the efficiency of your organization in processing and analyzing data for decision-making. By monitoring the time-to-value metric, you can easily identify data pipeline bottlenecks and ensure the availability of timely insights for business users.

Data Transformation Errors

The data transformation error rate is a measure of the error frequency resulting from transformations in the data pipeline. High error rates are indicative of issues in data processing rules or logic. To monitor the data transformation error rate, you can use auditing, logging, alerting, or dashboarding techniques.

Dark Data Volume

Organizational silos often result in dark data, which is the data that is collected but not used, often due to quality issues. It involves data generation by one team that might benefit another; however, the other team is unaware of this. Data discovery and profiling can help you quantify how much of your data is dark, specifying areas where data quality improvements can unlock value.

Why Should You Monitor Data Quality?

The need to monitor data quality is mainly due to the concerns associated with the data lifecycle; you may encounter different types of issues at each stage. Poor data quality often results in inaccurate analyses, misguided decisions, and financial collapse. These lead to a negative impact on organizational reputation.

Let’s look into the details of the areas in the data lifecycle where data quality may degrade:

Data Ingestion

Data ingestion is the intake of data into a system. The source data can be from various internal and external sources. Common sources include databases, data lakes, CRM and ERP systems, IoT devices, and apps. You can ingest data in real time or in batches from the source.

Apart from importing raw data, a proper data intake system can facilitate the conversion of data in diverse formats from multiple sources into a centralized, standard format. As a result, data ingestion can also involve converting unformatted data into a pre-existing data format.

Common issues that may arise during data ingestion include:

• Data duplication and delayed events
• Missing data
• Ingestion of stale or inaccurate data into the data system
• Incorrect data type or format
• Undetected outliers in a crucial field that show up in the data reporting layer
• Incorrect data syntax or semantics
• Data distribution drifts

Streamline Your Data Ingestion With Airbyte

To avoid the common issues related to data ingestion, you can use Airbyte, an efficient and dependable data integration solution.

Among the many impressive features of Airbyte is its extensive catalog of 550+ pre-built connectors. The supported platforms include diverse data warehouses, data lakes, databases, and analytical platforms for marketing, finance, ops, and products, among others.

Apart from the ready-to-use connectors, if you’re looking for one that’s not available, Airbyte offers multiple options to help you build custom connectors. You can use the Low-Code Connector Development Kit (CDK), Python CDK, and Java CDK. An AI assistant available within the no-code Connector Builder can automatically pre-fill several connection fields to configure the connectors. This speeds up the connector development process.

Here are some other noteworthy features of Airbyte that can help streamline your data ingestion process:

• Automated Schema Management: You can mention how Airbyte must handle schema changes in the source to ensure efficient and accurate data syncs. However, you can also manually refresh the schema at any time. Airbyte automatically checks for source schema changes every 15 minutes for cloud users and every 24 hours for self-hosted setups.‍
• Multiple Sync Modes: Airbyte supports incremental and full-refresh sync to read data from the source. To write data to the destination, it supports overwrite, append, append deduped, and overwrite deduped options.‍
• Pipeline Orchestration: You can integrate Airbyte with data orchestrators like Apache Airflow, Prefect, Dagster, and Kestra to ensure effective data flow through pipelines.‍
• Change Data Capture (CDC): With Airbyte’s support for CDC, you can identify incremental changes at the source and replicate them in the target system. Keeping track of updates helps maintain data consistency.

Data Systems or Pipelines

An effective data pipeline will provide healthy, dependable data. Whether you’re working with ETL, ELT, or rETL-based pipelines, it’s likely you may come across faulty data transformations resulting in data quality issues.

An example is if you write incorrect transformation steps, causing the wrong execution of your pipeline stages. Modifications within the data pipeline can lead to further problems, such as data corruption, data downtime, and issues for downstream customers.

Downstream Systems

While less common, data quality issues can occur when data is flowing to downstream customers, like analytics software or an ML training pipeline. Some examples of this include:

• Your BI analysis tool no longer receives data source updates. This may result in stale reports due to dependency changes or software upgrades.
• Code modification in your ML pipeline may prevent an API from gathering data for an offline or live model.

With constant monitoring and evaluation of data quality, you can ensure:

• Detection of most, if not all, data quality issues.
• Troubleshooting of such issues before they generate errors.
• Constant reporting of data quality to improve it and help solve business problems.

Data Quality Monitoring Techniques

There are different data quality monitoring techniques that you can employ to ensure data integrity and reliability. Let’s look into the details of these techniques:

Data Auditing

Data auditing involves assessing the completeness and accuracy of data in comparison to predefined standards or rules. This facilitates the identification and tracking of data quality issues, such as incorrect, missing, or inconsistent data.

For an effective data audit, you must first establish the data quality rules and standards that the data must adhere to.

You can perform data auditing using automated tools that can scan and flag data discrepancies or manually review records. This allows you to compare your data against the set rules or standards for identifying any issues or discrepancies. The final step is the analysis of the audit results and the implementation of remedial measures to address any identified data quality issues.

Data Profiling

Data profiling comprises tasks such as examining, analyzing, and comprehending the data structure and relationships. It involves data inspection at the row and column level to identify patterns, inconsistencies, and anomalies. With data profiling, you can gain information about data types, patterns, lengths, and unique values for insights into data quality.

Data profiling is classified into three main types:

• Column Profiling: This involves examining a dataset’s individual attributes.‍
• Dependency Profiling: It facilitates discovering relationships between attributes.‍
• Redundancy Profiling: This helps detect duplicate data.

You can use data profiling tools to gain a comprehensive overview of your data and discover any quality issues you must address.

Data Cleaning

Also known as data cleansing or data scrubbing, data cleaning is the process of identifying and fixing inconsistencies, errors, and inaccuracies in data. The different methods involved in data cleaning include data validation, transformation, and deduplication. These methods ensure your data is complete, accurate, and reliable.

A data cleaning process typically includes the following steps:

• Identify and determine the root cause of data quality issues.
• Select and apply appropriate cleansing techniques to your data.
• Validate the results to ensure the issues are resolved.

An effective data-cleaning process helps maintain high-quality data for decision-making and business operations.

Data Quality Rules

Your data must follow some predefined criteria or data quality rules to ensure reliability, completeness, consistency, and accuracy. With these rules, you can maintain high-quality data. Data validation, cleansing, or transformation processes can help enforce data quality rules.

Examples of data quality rules include validating data against reference data, checking for duplicate entries, and ensuring data conformance to specific patterns or formats.

For proper implementation of data quality rules:

• First, establish the rules based on your organizational data quality standards and requirements.
• Next, use data quality tools or custom scripts to enforce the established rules on your data. This will flag any issues or discrepancies.
• Finally, regularly monitor and update the data quality rules. As a result, you can ensure the relevancy of rules that will effectively enable data quality maintenance.

Real-time Data Monitoring

This process involves constant tracking and analysis of data as it is created, processed, and stored within your organization. Real-time data monitoring allows you to identify and fix data quality issues promptly instead of waiting for periodic data reviews or audits.

With real-time data monitoring, you can maintain high-quality data, ensuring decision-making processes utilize accurate and up-to-date information.

Data Performance Testing

Data performance testing is the evaluation of the effectiveness, efficiency, and scalability of your related infrastructure and data processing systems. Testing helps ensure that your data processing systems can handle increasing data complexity, volumes, and velocity without any quality compromise.

Here are the steps involved in data performance testing:

• Create performance standards and targets for your data processing systems.
• Use data performance testing tools to simulate varied data processing scenarios, such as large data volumes or complex transformations.
• Measure your systems’ performance against the established targets and standards.
• Review the data performance test results and implement necessary changes to improve your data processing systems and infrastructure.

Tracking Data Quality Metrics

To assess the quality of your organization’s data, you can monitor the data quality metrics. You can use these quantitative metrics to track data quality, identify patterns and trends, and assess the effectiveness of your data quality monitoring techniques.

Here are the steps involved in tracking data quality metrics:

• Determine the metrics that are relevant to your organization’s data quality standards and requirements.
• Use data quality tools or custom scripts to evaluate these metrics for your data. This will provide a quantitative assessment of your data quality.
• Regularly analyze and review your data quality metrics to identify scope for improvement. This helps ensure the effectiveness of your data quality monitoring techniques.

Metadata Management 

Metadata management comprises organizing, preserving, and utilizing metadata for the improvement of data quality, consistency, and usefulness. Data quality rules, data lineage, and data definitions are some types of metadata that allow you to understand and manage your data better.

With robust metadata management techniques, you can improve overall data quality. It also ensures the data is easily understandable, accessible, and usable by your organization.

For effective metadata management:

• Establish a metadata repository to store and organize your metadata in a structured and consistent way.
• As your data and data processing systems evolve, maintain and update your metadata using metadata management tools.
• Implements best practices and processes for using metadata to support data integration, data quality monitoring, and data governance initiatives.

Summing It Up

Data quality monitoring can help assess your organizational data to ensure it meets the expected standards of reliability, accuracy, and consistency. The different data quality dimensions include completeness, integrity, timeliness, and uniqueness, among others.

Some data quality metrics worth monitoring are error ratio, duplicate record rate, dark data volume, and data time-to-value. To measure data quality, you can use techniques such as data auditing, real-time data monitoring, metadata management, and data cleaning.

Good-quality, accurate data can assist with decision-making, leading to effective strategies that will benefit your organization.