A Complete Guide on How to Build Effective Data Quality Checks

Data quality checks are like safety nets for any ETL project. They act as gatekeepers to ensure that data plays by the rules and stays in top shape. When ETL developers deal with tons of data moving around, data quality checks in ETL projects become the unsung heroes, catching issues before they become full-blown problems. 

Build an ETL Pipeline for Financial Data Analytics on GCP-IaC

Downloadable solution code | Explanatory videos | Tech Support

Start Project

Building a real-world ETL project requires more than just moving data from one place to another—it demands a meticulous approach to ensuring data quality. Neglecting data quality in ETL projects can have far-reaching consequences, ranging from flawed analytics to inaccurate reporting and, in some cases, even financial losses for organizations. Explore this blog thoroughly to discover the essential data quality checks and their examples that form the backbone of ETL projects. These checks are not just formalities but are vital for fortifying your ETL pipeline, ensuring reliability in data-driven insights. 

Introduction to Data Quality Checks  

Data quality checks involve a series of processes and methodologies designed to assess and maintain data accuracy, completeness, reliability, and timeliness. 

What is Data Quality, and Why is it Important? 

Data Quality refers to the degree to which data is accurate, reliable, consistent, and relevant for its intended purpose. High-quality data is essential for organizations to derive meaningful insights, make informed decisions, and meet regulatory requirements. Poor data quality can lead to faulty analyses, misinformed decisions, and damaged reputations. 

Venkatesh Kumar, Assistant Vice President at Citi Corp, emphasizes the critical role of Data Quality in identifying failures or errors. He highlights the importance of assessing completeness, validity, and accuracy, with prioritization based on materiality impact and alignment with reporting models. Check out his LinkedIn post below for more details - 

Importance of Data Quality Control Process 

• Decision-making: High-quality data ensures that decisions are based on accurate information, leading to better outcomes.

• Operational Efficiency: Reliable data is crucial for an efficient manufacturing or business process. 

• Customer Satisfaction: Accurate customer data contributes to improved customer experiences and satisfaction.

• Compliance: Many industries are subject to regulations that require accurate and secure data handling. Ensuring data quality is vital for compliance.

• Trust and Credibility: Organizations prioritizing data quality build trust with stakeholders, customers, and partners, enhancing their credibility in the market.

The 8 Dimensions of Data Quality 

Assessing data quality involves considering various dimensions that collectively determine the overall health of the data. Here are the top 8 Data Quality dimensions that provide a comprehensive framework for evaluating and improving data quality:

• Accuracy: Refers to the correctness of data. Accurate data is free from errors and represents the real-world values it is supposed to capture.

• Completeness: Addresses whether all necessary data is present. Incomplete data may lack essential information, leading to gaps in analyses.It's about making sure no data ninja goes missing. ETL developers count the entries at every stage, ensuring no record does a disappearing act during the ETL hustle.

• Consistency: Ensures uniformity and coherence across data sets. Inconsistent data may have conflicting information that hinders reliable decision-making.

• Timeliness: Reflects the relevance of data about when it is needed. Timely data is up-to-date and aligns with the current context. 

• Relevance: Focuses on the significance of the data about the task at hand. Relevant data is directly applicable to the analysis or decision-making process.

• Validity: Examines whether data conforms to predefined rules and standards. Valid data meets the criteria set for its format and structure.

• Reliability: Indicates the trustworthiness and consistency of data over time. Reliable data remains accurate and consistent across various timeframes.

• Precision: Deals with the level of detail in the data. Precise data is specific and avoids unnecessary granularity or ambiguity.

Here's what valued users are saying about ProjectPro

I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge. This is when I was introduced to ProjectPro, and the fact that I am on my second subscription year...

Abhinav Agarwal

Graduate Student at Northwestern University

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills in Data Engineering/Science and hoping to find real-world projects fortunately, I came across...

Ed Godalle

Director Data Analytics at EY / EY Tech

Not sure what you are looking for?

View All Projects

Types of Data Quality Checks in ETL 

Let’s now explore the various types of data quality checks in the Extract, Transform, Load (ETL) process to ensure the accuracy and reliability of data. These checks include:

1. Source Data Profiling 

This involves scrutinizing statistical properties and applying predefined rules, allowing for the detection of irregularities and the preservation of data quality during the initial ETL stages. 

• Statistical Profiling: Statistical profiling involves analyzing the statistical properties of the source data. This includes metrics such as mean, median, standard deviation, and distribution. By understanding these statistical characteristics, data engineers can identify anomalies, outliers, or patterns that deviate from the expected values. 

• Rule-Based Profiling: Rule-based profiling applies predefined business rules to the source data, flagging discrepancies or violations. This ensures adherence to specific data quality standards and facilitates the identification of data anomalies requiring attention during ETL processing.

New Projects

Stock Price Prediction Project using LSTM and RNN

Mastering A/B Testing: A Practical Guide for Production

Stock Price Prediction Project using LSTM and RNN

Many-to-One LSTM for Sentiment Analysis and Text Generation

Build an ETL Pipeline with DBT, Snowflake and Airflow

Predictive Analytics Project for Working Capital Optimization

Llama2 Project for MetaData Generation using FAISS and RAGs

MLOps Project to Build Search Relevancy Algorithm with SBERT

Predictive Analytics Project for Working Capital Optimization

dbt Snowflake Project to Master dbt Fundamentals in Snowflake

2. Data Transformation Validation

This involves ensuring data accuracy by verifying adherence to predefined formats, acceptable ranges, and referential integrity throughout the transformation process.

• Format Validation: Format validation ensures that data conforms to predefined formats during the transformation process. This check helps maintain consistency in data structure and prevents issues arising from incompatible data formats.

• Range Validation: Range validation verifies that data values fall within acceptable ranges. This check is crucial for identifying outliers and potential errors during ETL transformations, promoting the accuracy and reliability of the transformed data.

• Referential Integrity Checks: Referential integrity checks validate relationships between tables, ensuring that foreign key relationships are maintained. This guarantees that transformed data retains its intended meaning and consistency across different tables.

3. Consistency Checks in Staging

This involves examining cross-field and cross-table relationships within the staging area, ensuring consistent data coherence and integrity throughout the ETL workflow.

• Cross-Field Consistency: Cross-field consistency checks examine relationships between different fields within the same record. Detecting inconsistencies across fields in the staging area helps maintain data coherence and integrity throughout the ETL process.

• Cross-Table Consistency: Cross-table consistency checks verify the consistency of data across multiple tables in the staging area. This ensures that data relationships are maintained correctly, preventing discrepancies that could compromise the overall quality of the transformed data.

4. Target Data Completeness

This involves validating the presence of expected records and the population of required fields in the target dataset, preventing data loss and supporting comprehensive analysis.

• Record Completeness: Record completeness checks assess whether all expected records are present in the target dataset. This validation ensures that no data is lost during the ETL process, preserving the entirety of the information for downstream analysis.

• Field Completeness: Field completeness checks verify that all required fields in the target dataset are populated with data. This ensures that the transformed data meets the defined criteria for completeness, supporting accurate and comprehensive data analysis. 

Open-Source Data Quality Tools for ETL Processes

Open-source data quality tools play a crucial role in the Extract, Transform, Load (ETL) process by ensuring that the data being transferred and transformed is accurate, consistent, and reliable. These tools help organizations maintain high-quality data, which is essential for making informed business decisions. Here are three top open-source data quality tools commonly used in ETL processes: 

• Talend Open Studio: Talend Open Studio is a popular open-source ETL tool that includes comprehensive data quality features. It offers a user-friendly interface with drag-and-drop functionality, making it accessible to both developers and business users. 

• Apache Nifi: Apache Nifi is an open-source data integration tool that focuses on automating the flow of data between systems. While it is known for its data ingestion capabilities, it also includes features for data quality management.

• DataCleaner: DataCleaner is an open-source data quality solution that focuses specifically on cleansing and profiling data. It is designed to be user-friendly and can be integrated with various ETL tools and platforms.

How to Check Data Quality for ETL Processes? 

Check out the following five-step guide that provides a straightforward and practical approach to assessing data quality in ETL processes. From defining criteria to establishing robust monitoring mechanisms, each step is designed to systematically address potential issues and maintain a high standard of data quality throughout the ETL workflow. So, let’s get started! 

Step 1: Define Data Quality Criteria

Begin the data quality check for ETL processes by clearly defining the criteria for acceptable data quality. This involves specifying the characteristics that data must possess, such as accuracy, completeness, consistency, and timeliness. Establish measurable benchmarks for each criterion to objectively assess the data's quality throughout the ETL pipeline.

Step 2: Data Profiling

Perform thorough data profiling to examine the structure, patterns, and values within the datasets. This step involves analyzing data statistics, identifying outliers, and understanding data distributions. By conducting data profiling, you gain insights into potential anomalies or irregularities that might compromise data quality during ETL processes.

Step 3: Implement Data Validation Checks 

Create and implement data validation checks at various stages of the ETL process to ensure data accuracy and integrity. These checks can include referential integrity checks, uniqueness constraints, and domain-specific validations. By enforcing these checks, you can identify and rectify data discrepancies early in the ETL workflow.

Step 4: Monitor Data Transformation Processes

Implement robust monitoring mechanisms during the transformation phase of ETL processes. This involves tracking changes in data values, identifying transformation errors, and ensuring that data conforms to the defined quality criteria. Real-time monitoring enables timely intervention to address issues and maintain high data quality standards. 

Step 5: Establish Data Quality Metrics and Reporting

Define key performance indicators (KPIs) and establish data quality metrics to quantitatively measure the success of your ETL processes. Develop comprehensive reports that highlight data quality trends, issues, and areas for improvement. Regularly review these reports to continuously optimize the ETL pipeline and maintain a consistent standard of data quality. 

Data Quality Checks Examples: How to Ensure Data Quality? 

1. NULL Values Test

This test assesses databases or datasets for the presence of null or missing values. It ensures data integrity by identifying any gaps or incomplete information, helping maintain the quality and reliability of the stored data.

Data Quality Problem: Missing values in critical fields may lead to inaccurate analysis and reporting.

How do you solve this problem? 

Implement a NULL values test to identify fields with missing data and decide on an appropriate strategy for handling them, such as imputation or removal. Use SQL queries or programming languages like Python to count NULL values in each column. Visualization tools such as histograms or heatmaps can help provide a clear overview of missing data distribution.

2. Volume Tests

Volume tests evaluate the scalability and performance of a system by subjecting it to varying levels of data load. These tests help determine how well a system can handle large volumes of data, ensuring it remains responsive and efficient under different usage scenarios.

Handling Missing Data 

Data Quality Problem: Incomplete datasets can affect the accuracy of analytical models and reports.

How do you solve this problem? 

Establish volume tests to manage missing data, setting Service Level Indicators (SLIs) for acceptable levels of completeness. Volume tests are designed to ensure that datasets are complete and meet predefined thresholds. For instance, if you're dealing with customer data, a volume test might specify that at least 95% of customer profiles should be complete.

Too Much Data 

Data Quality Problem: Overwhelming amounts of data can strain processing resources and lead to inefficiencies.

How do you solve this problem? 

Define volume SLIs to ensure that the data processing infrastructure can handle the volume effectively, preventing performance degradation. This aspect of volume tests focuses on preventing systems from being overloaded with excessive data. Define SLIs for the maximum acceptable data volume and regularly monitor to ensure that the data processing infrastructure can handle the load efficiently. 

3. Freshness Check Test 

Freshness checks assess the timeliness of data, particularly in real-time or near-real-time systems. These tests verify whether data is updated and reflect the most recent information, ensuring that users or applications are working with current and accurate data for informed decision-making.

Data Quality Problem: Outdated data can misguide decision-makers, especially in rapidly changing environments.

How do you solve this problem? 

Employ freshness checks to ensure that the data is up-to-date, establishing thresholds for acceptable data staleness. Timestamps or date fields can be used to track the last update time for each record. Freshness checks can then compare this timestamp to the current time to determine if the data is within an acceptable time frame.

4. Numeric Distribution Tests

Numeric distribution tests assess the spread and consistency of numerical data. For instance, in a dataset of product prices, a numeric distribution test might identify prices that fall outside a reasonable range.

Inaccurate Data 

Data Quality Problem: Inconsistencies in numerical data can introduce errors in calculations and analyses.

How do you solve this problem? 

Implement numeric distribution tests to identify outliers and ensure the accuracy and consistency of numeric data. Use statistical measures like mean, median, and standard deviation to identify outliers. Visualization tools such as box plots or histograms can help in identifying the distribution patterns.

Data Variety 

Data Quality Problem: Diverse data types may lead to misinterpretation or mishandling during analysis.

How do you solve this problem? 

Utilize numeric distribution tests to validate the variety of numeric data, ensuring compatibility with analysis tools and models. Data variety is crucial, especially in datasets with multiple data types. Numeric distribution tests can reveal if there are unexpected variations in the numeric data, ensuring that the data aligns with the assumptions made in the analysis. 

5. Uniqueness Tests 

Uniqueness tests involve checking for duplicate entries within a dataset. For example, in a customer database, identifying and removing duplicate entries for the same customer is essential for accurate customer analysis.

Duplicate Data Detection  

Data Quality Problem: Duplicate records can skew analysis results and misrepresent the true state of the data.

How do you solve this problem? 

Perform uniqueness tests to identify and eliminate duplicate data, maintaining a clean and accurate dataset.  For example, in a customer database, identifying and removing duplicate entries for the same customer is essential for accurate customer analysis. Use SQL queries or programming scripts to identify duplicate records based on key fields such as customer ID or transaction ID. 

6. Referential Integrity Test 

Referential integrity tests ensure that relationships between tables are consistent. In a relational database, this might involve checking that foreign keys in one table correspond to primary keys in another.

Data Quality Problem: Inconsistencies in relationships between tables can compromise data integrity.

How do you solve this problem? 

Use SQL queries to check for consistency in relationships between tables. For instance, in a database with orders and customers, ensure that every order references a valid customer ID.

7. String Patterns Test 

String pattern tests focus on ensuring that text data follows expected formats. For example, in a dataset of email addresses, a string pattern test might identify entries that do not conform to the typical email address format.

Data Quality Problem: Irregularities in text data can lead to misinterpretation and errors in natural language processing.

How do you solve this problem? 

Apply string pattern tests to ensure that text data adheres to predefined patterns, facilitating accurate analysis and interpretation. Use regular expressions or specific string-matching algorithms to identify and correct deviations from expected patterns.

Best Practices for ETL Data Quality Checks

Adopting best practices to improve data quality for ETL is indispensable for organizations seeking to maintain data integrity and enhance the overall efficiency of their data pipelines. Check the top best practices below for maintaining accurate and reliable data throughout the extraction, transformation, and loading processes.

• Define Clear Data Quality Metrics: Clearly define and document the data quality metrics relevant to your ETL process. Establish criteria for accuracy, completeness, consistency, and timeliness. This ensures that data quality expectations are transparent and deviations can be easily identified during the ETL process.

• Implement Automated Data Profiling: Leverage automated data profiling tools to analyze and understand the characteristics of your data. This includes identifying data patterns, distributions, and anomalies. Automated profiling helps in the early detection of data quality issues, enabling timely interventions and improving the overall efficiency of the ETL process.

• Establish Robust Error Handling Mechanisms: Implement comprehensive error handling mechanisms throughout the ETL workflow. This includes capturing, logging, and alerting for data quality issues. Clearly define escalation procedures for addressing critical errors. Robust error handling ensures that data discrepancies are promptly addressed, preventing the propagation of inaccuracies through downstream systems.

• Execute Data Quality Checks at Key Transition Points: Integrate data quality checks at crucial transition points within the ETL pipeline, such as after data extraction, transformation, and before loading into the target system. This approach allows for identifying issues at each stage, reducing the chances of erroneous data being processed further downstream. It also facilitates a more granular understanding of the origin of data quality problems.

• Regular Monitoring and Continuous Improvement: Establish a systematic and regular monitoring process for data quality. This involves setting up recurring audits, analyzing historical data quality reports, and continuously refining the ETL process based on insights gained. Regular monitoring ensures that the data quality standards are maintained over time and allows for proactive adjustments to accommodate changing data patterns or sources.

Master Your ETL Skills with ProjectPro! 

As organizations rely heavily on accurate and reliable data, the demand for professionals proficient in implementing robust Data Quality checks within ETL projects is on the rise. These checks play a vital role in guaranteeing the integrity of the data being processed, rectifying errors, and enhancing the overall reliability of insights derived from the data. With a rich repository of over 270+ real-world projects in ETL, data science and big data, ProjectPro offers a hands-on platform for professionals to immerse themselves in the complexities of ETL processes. These projects provide a unique opportunity to apply theoretical knowledge to practical scenarios, ensuring that individuals not only grasp the principles of data quality checks but also cultivate the expertise needed to implement them effectively in diverse real-world situations. 

So, what are you waiting for? Check out ProjectPro Repository today to elevate your ETL skills, prioritize data quality assurance, and navigate the intricacies of enterprise-grade ETL projects with confidence. 

FAQs on Data Quality Checks 

How to do data quality checks?

You can conduct data quality checks by assessing completeness, accuracy, consistency, timeliness, and reliability. Employ validation rules, outlier detection, and cross-validation techniques. You can also use data profiling tools to analyze data patterns and identify anomalies.

Why do we perform data quality checks?

Data quality checks are essential to ensure reliable, accurate, and consistent data. They help identify and rectify errors, anomalies, or missing information, preventing downstream issues in decision-making processes. 

How to do quality checks for data in Hadoop?

You can use tools like Apache NiFi or Apache Griffin to assess data quality in Hadoop through profiling, validation, and monitoring processes. 

What are data quality checks? 

Data quality checks involve validating source and target data, identifying anomalies, and implementing cleansing and transformation processes to maintain high-quality data throughout the ETL pipeline. 

About the Author

Nishtha

Nishtha is a professional Technical Content Analyst at ProjectPro with over three years of experience in creating high-quality content for various industries. She holds a bachelor's degree in Electronics and Communication Engineering and is an expert in creating SEO-friendly blogs, website copies,

Meet The Author