Data Quality Testing: Why to Test, What to Test, and 5 Useful Tools

Data quality testing refers to the evaluation and validation of a dataset’s accuracy, consistency, completeness, and reliability. It employs various techniques to detect errors, inconsistencies, or inaccuracies that could impact the overall quality of data used for analysis or decision-making. The primary goal of data quality testing is to ensure that an organization’s datasets are accurate and reliable enough to support well-informed decisions.

Data engineers often utilize specialized tools to perform comprehensive tests on their datasets while tracking metrics related to these aspects throughout the entire lifecycle. Data quality testing is a critical component of data engineering, as it guarantees that the data used for analysis and decision-making is accurate.

In this article:

• The Significance of Data Quality Testing
• Essential Data Quality Tests
• When to Test Your Data
• Data Quality Testing Frameworks

Data quality testing is essential for ensuring the accuracy, consistency, and reliability of your organization’s data. It enables:

• Enhanced decision-making: Accurate and reliable data allows businesses to make well-informed decisions, leading to increased revenue and improved operational efficiency.
• Increased customer satisfaction: Ensuring high-quality data helps you better understand your customers’ needs and preferences, enabling you to deliver personalized experiences that improve satisfaction.
• Risk mitigation: Data errors can result in expensive mistakes or even legal issues. Regularly testing your datasets reduces the likelihood of such occurrences by detecting inconsistencies early on.
• Improved productivity: Poor-quality data often leads to time wasted on correcting errors or reconciling discrepancies. Implementing robust quality checks streamlines workflows by minimizing the need for manual intervention.

Learn more in our detailed guide to data monitoring

Here are some of the important tests typically included in a data quality program:

• Completeness: Confirm that all required fields in your dataset have values and no critical information is missing.
• Uniqueness: Check for duplicate records or entries within your dataset to maintain data integrity.
• Validity: Verify that the data adheres to specified formats or rules (e.g., email addresses follow a specific pattern).
• Trend analysis: Examine historical trends in your datasets to identify any anomalies or unexpected changes over time.
• Data profiling: Generate summary statistics about each field in your dataset (e.g., min/max values, average length) to help detect potential issues with new incoming data.

In addition to these core tests, it’s essential to run domain-specific checks tailored for your industry or use case. For example, if you’re working with financial transactions, validating transaction amounts against predefined thresholds might be necessary.

Testing data is a fundamental aspect of data engineering and should be conducted at multiple points throughout the information’s lifespan. Regular testing ensures that your organization maintains high-quality data and can make informed decisions based on accurate insights.

• During ingestion: Test your data as it enters your system to identify any issues with the source or format early in the process. This allows you to address problems before they propagate through downstream systems.
• After transformation: After processing or transforming raw data into a more usable format, test again to ensure that these processes have not introduced errors or inconsistencies. For example, if you’re using ETL (Extract, Transform, Load) pipelines to prepare your data for analysis, validating its quality after each step is essential.
• Before reporting and analysis: Prior to generating reports or conducting analyses on your dataset, perform another round of testing to confirm that the final output meets all necessary requirements and standards. 
• Scheduled checks: Regularly scheduled tests are crucial for maintaining ongoing confidence in your datasets’ integrity over time. By continuously monitoring key metrics such as completeness, accuracy, consistency, timeliness, and uniqueness, potential issues can be detected early enough for corrective action.

IBM Databand

IBM Databand is a robust observability platform tailored for contemporary data teams handling big data applications. It delivers comprehensive visibility into your entire data stack, from origin to destination, allowing for swift identification and resolution of issues related to duplicated or inaccurate data.

Databand provides automated anomaly detection which simplifies alert configuration and management. Instead of setting specific conditions on metrics that may be not known in advance or change as a data change, an anomaly alert will trace the historical value of the metric and would be triggered when the metric has an abnormal value compared to the past.

A typical example is setting anomaly on a run duration, when run duration gets an extremely high value, an alert would be triggered. This ensures that all incoming and outgoing datasets adhere to predefined quality standards before utilization in downstream processes.

Deequ

Deequ, an open-source library created by Amazon Web Services (AWS), enables testing the completeness, consistency, and accuracy of large-scale datasets using declarative constraints expressed in SQL-like syntax.

The framework supports both batch processing on static datasets and streaming processing on real-time event streams, such as clickstreams or IoT sensor readings. Deequ also features built-in support for common use cases like schema validation, outlier detection, and cross-field validation checks, among others.

dbt

Teams use dbt Core and dbt Cloud to quickly deploy and test their analytics code. 

dbt supports schema tests for uniqueness, null or accepted values, or referential integrity between tables.

Databand provides dbt observability across your jobs, tests, and models you can know when a dbt process breaks, and how to fix it fast.

OwlDQ

OwlDQ is an open-source platform engineered specifically for enhancing data quality in modern data-driven organizations. It boasts advanced features such as automated anomaly detection using machine learning algorithms and real-time monitoring of your datasets with configurable alerts for detected anomalies.

With OwlDQ, you can effortlessly establish custom rule sets to validate incoming datasets against specific business requirements or industry standards. The platform integrates with popular ETL tools like Apache NiFi and Talend Open Studio, enabling quick incorporation into existing workflows without necessitating significant changes.

Great Expectations

Great Expectations is a tool for validating, documenting, and profiling your data to maintain quality and improve communication between teams.

Great Expectations gives developers testing capabilities such as assertions about your data, automated data profiling, data validation, and Data Docs.

Learn more in our detailed guide to data quality framework