Top Data Quality Checks Every Business Should Perform

Data quality refers to the accuracy, completeness, consistency, and reliability of data. Organizations today rely heavily on data to make informed decisions, drive business strategies, and gain a competitive edge. However, poor data quality can have severe consequences, leading to flawed analyses, incorrect decisions, and ultimately, financial losses.

Inaccurate or incomplete data can result in costly mistakes, such as incorrect inventory management, ineffective marketing campaigns, or even regulatory non-compliance (leading to onerous fines). Poor data quality can also undermine customer trust, as incorrect information can lead to frustrating experiences and dissatisfaction.

On the other hand, maintaining high data quality offers numerous benefits. It ensures that decisions are based on reliable and accurate information, enabling organizations to make well-informed choices that drive growth and profitability. Good data quality also enhances operational efficiency, as processes can run smoothly without interruptions caused by data errors or inconsistencies.

Furthermore, high-quality data is essential for effective data analysis and reporting, providing valuable insights that can inform strategic decision-making and drive innovation. By ensuring data accuracy and completeness, organizations can gain a comprehensive understanding of their customers, markets, and operations, enabling them to identify opportunities and mitigate risks more effectively.

What are data quality checks?

Data quality checks encompass a set of processes and techniques used to assess and maintain the integrity, accuracy, and completeness of data. The purpose of these checks is to identify and address any issues or inconsistencies within the data, ensuring it is reliable, trustworthy, and fit for its intended use.

Data quality checks are crucial because poor data quality can lead to inaccurate analysis, flawed decision-making, and potentially costly mistakes. By implementing robust data quality checks, organizations can ensure that their data is clean, consistent, and meets the necessary standards for their specific use cases.

Organizations can perform various types of data quality checks, depending on their data requirements and the nature of their business operations. 

Common types of data quality checks

1. Null values check: This check identifies and addresses any missing or null values within the data, which can be problematic for certain analyses or processes.

2. Freshness checks: These checks ensure that the data being used is up-to-date and reflects the most recent information available, preventing the use of outdated or stale data.

3. Volume checks: These checks monitor the volume of data being processed and ensure that it falls within expected ranges, helping to identify potential issues with data ingestion or processing pipelines.

4. Numeric distribution checks: These checks analyze the distribution of numeric values within the data, looking for outliers, skewed distributions, or other anomalies that may indicate data quality issues.

5. Uniqueness checks: These checks verify that certain fields or combinations of fields within the data are unique, preventing duplicate or redundant entries.

6. Referential integrity checks: These checks ensure that relationships between different data entities are maintained, ensuring that foreign keys and other references are valid and consistent.

7. String pattern checks: These checks analyze string data for adherence to specific patterns or formats, such as email addresses, phone numbers, or postal codes, helping to identify and correct any formatting issues.

By implementing these and other data quality checks, organizations can proactively identify and address data quality issues, ensuring that their data is reliable, trustworthy, and suitable for its intended use cases.

Null values check

Null values, also known as missing or blank values, are placeholders in a dataset that represent the absence of data. These values can occur for various reasons, such as data entry errors, system failures, or intentional omissions. Identifying and handling null values is crucial for maintaining data integrity and ensuring accurate analysis and decision-making.

Null values can significantly impact data quality, leading to incomplete or inaccurate results. For instance, if a dataset contains null values in a column used for calculations or aggregations, the results may be skewed or incorrect. Additionally, null values can cause issues with data processing pipelines, leading to errors or unexpected behavior.

Null values: Industry use cases

In healthcare, null values in patient records, such as missing medical history, can result in inaccurate diagnoses or treatment plans, posing risks to patient safety. 

In finance, null values in transaction records or customer information can cause reporting errors and compliance issues. 

In retail, null values in inventory or sales data can lead to inaccurate stock levels and inefficient supply chain management. Handling null values is vital for optimizing inventory management, forecasting, and profitability analysis.

Proper handling of null values is essential across industries for accuracy, compliance, and operational efficiency.

How to run a null values check

To identify null values, data professionals can use various techniques, such as SQL queries, programming languages like Python or R, or data analysis tools. These tools allow for filtering, counting, and visualizing null values within a dataset. Once identified, null values can be handled in several ways:

Deletion: In some cases, it may be appropriate to remove rows or columns containing null values, especially if the missing data is not critical or if the dataset is large enough to maintain statistical significance.

Imputation: This involves replacing null values with estimated or calculated values based on the available data. Common imputation methods include mean/median substitution, regression-based imputation, or using machine learning algorithms.

Flagging: Another approach is to flag or mark null values with a specific value or indicator, allowing for transparency and enabling downstream processes to handle them appropriately.

By implementing robust null value checks and appropriate handling strategies, organizations can enhance data quality, ensuring reliable and accurate data-driven decision-making processes.

Freshness checks

Data freshness refers to the timeliness and relevance of data within an organization's systems. It measures how up-to-date the data is compared to the real-world events or entities it represents. Ensuring data freshness is crucial because stale or outdated data can lead to inaccurate analyses, flawed decision-making, and missed opportunities.

In today's fast-paced business environment, data quickly becomes obsolete, making freshness checks a critical component of data quality assurance. Freshness checks help organizations identify and address data that has become stale or outdated, enabling them to maintain accurate and reliable information for their operations and decision-making processes.

Performing freshness checks typically involves comparing the timestamp or date associated with the data against a predefined threshold or expected update frequency. This threshold can be set based on the specific requirements of the data source, the business process it supports, or industry best practices.

Freshness checks: Common techniques

Here are some common approaches to performing freshness checks:

1. Timestamp comparison: Compare the timestamp of the data record against the current time or a predefined threshold. For example, if customer data should be updated daily, any records older than 24 hours could be flagged as stale.

2. Last updated tracking: Maintain a separate record or metadata field that tracks the last time a data record was updated. This information can then be used to identify records that haven't been updated within the expected timeframe.

3. Change data capture (CDC): Implement a CDC mechanism that captures and logs changes to data sources. This log can be used to identify records that haven't been updated or have remained unchanged beyond the expected timeframe.

4. External data source monitoring: For data sourced from external systems or APIs, monitor the source for updates and compare the timestamp of the latest update against the data in your systems.

Freshness checks: Industry use cases

Freshness checks are particularly important in industries where data rapidly becomes obsolete or where timely information is critical for decision-making. For example:

In the financial sector, stock prices, market data, and trading information must be up-to-date to facilitate accurate trading decisions and risk management.

Retailers rely on fresh inventory data to ensure proper stock levels, optimize pricing strategies, and meet customer demand.

In healthcare, patient medical records, treatment plans, and prescription data must be current to provide accurate and timely care.

In logistics, shipping companies and logistics providers require fresh data on shipment locations, delivery statuses, and transportation routes to ensure efficient operations and customer satisfaction.

By implementing regular freshness checks, organizations can identify and address stale or outdated data, ensuring that their systems and decision-making processes are based on accurate and timely information.

Volume checks

Volume checks are a type of data quality check that monitors the amount or quantity of data flowing through a system or process. These checks are crucial for ensuring that the expected volume of data is being processed and that there are no significant deviations from the norm.

The importance of monitoring data volume cannot be overstated. Sudden changes in data volume can indicate underlying issues within the data pipeline or the source systems. For example, a significant drop in data volume could signal a system failure or data loss, while an unexpected surge in volume could indicate data duplication or incorrect data ingestion.

To perform volume checks, organizations typically establish baseline expectations for data volume based on historical trends and business requirements. These baselines can be set at different levels, such as daily, weekly, or monthly volumes. Once the baselines are established, data engineers or analysts can monitor the incoming data streams and compare the actual volumes against the expected baselines.

Volume checks: Common techniques

The most frequently used types of volume checks include:

1. Batch volume checks: For batch-based data pipelines, the total number of records or files processed in a batch can be compared against the expected volume.

2. Streaming volume checks: In real-time streaming environments, the volume of data can be monitored continuously by measuring the number of records or events received over a specific time window.

3. Incremental volume checks: For incremental data loads, the volume of new or updated records can be compared against the expected delta or change.

4. Threshold-based alerting: Alert mechanisms can be set up to trigger notifications when the data volume deviates from the expected range, either above or below predefined thresholds.

Volume checks: Industry use cases

Industry examples of volume checks include:

In retail, leaders must monitor the volume of sales transactions or inventory updates to detect potential issues with point-of-sale systems or supply chain processes.

In the world of finance, leaders may leverage volume checks for tracking the volume of financial transactions, trade orders, or market data feeds to ensure complete and timely processing.

Use cases for volume checks in telecommunications abound, including monitoring the volume of call detail records or network traffic to identify potential outages or capacity issues.

Healthcare leaders may use volume checks for monitoring the volume of electronic medical records or patient data to ensure completeness and identify potential data breaches or system failures.

By implementing robust volume checks, organizations can proactively identify and address data quality issues, ensuring the reliability and integrity of their data assets.

Numeric distribution checks

Numeric distribution checks analyze the statistical properties of numeric data fields to detect anomalies or deviations from expected patterns. These checks are crucial for maintaining data integrity, as they can identify data quality issues that might go unnoticed by other types of checks.

The importance of monitoring numeric distributions lies in the fact that many business processes and analytical models rely on the assumption that numeric data follows specific statistical distributions. Deviations from these expected distributions can indicate data quality issues, such as data entry errors, system malfunctions, or changes in underlying business processes.

To perform numeric distribution checks, data professionals typically calculate summary statistics, such as mean, median, standard deviation, and quantiles, for the numeric data fields of interest. These statistics are then compared against predefined thresholds or historical baselines to identify any significant deviations.

By implementing robust numeric distribution checks, organizations can proactively identify data quality issues, enabling timely corrective actions and ensuring the reliability of data-driven decisions.

Numeric distribution checks: Industry use cases

For example, in a financial institution, numeric distribution checks can be used to monitor the distribution of transaction amounts. If the distribution deviates significantly from the expected pattern, it could indicate potential fraud or errors in the transaction processing system.

One common tool for identifying fraud detection is Benford's Law, which states that in naturally occurring datasets, the leading digits are distributed in a predictable pattern. Deviations from this pattern can serve as a red flag for potential fraud. In finance, applying Benford’s Law to transaction amounts can help uncover suspicious activities, such as fabricated numbers in expense reports or accounting records. Similarly, businesses across industries can leverage this method to validate the authenticity of their numerical data and identify anomalies that warrant further investigation.

In the healthcare industry, numeric distribution checks can be applied to vital signs data, such as blood pressure or body temperature readings. Unexpected shifts in the distribution of these measurements could signal underlying health issues or data quality problems in the medical record system.

Retail businesses can leverage numeric distribution checks to monitor the distribution of product prices or sales figures. Anomalies in these distributions may indicate pricing errors, inventory issues, or changes in customer behavior.

Uniqueness checks

Uniqueness checks ensure that each record or row in a dataset is distinct and does not contain any duplicate entries. These checks are crucial for maintaining data integrity and preventing data redundancy, which can lead to inaccurate analysis and decision-making.

In many scenarios, data uniqueness is a fundamental requirement. For instance, in customer databases, each customer should have a unique identifier, such as an email address or a customer ID, to avoid duplicates. Similarly, in financial transactions, each transaction should have a unique reference number to prevent double-counting or incorrect reconciliation.

Performing uniqueness checks involves identifying the columns or combination of columns that should be unique within a dataset. This can be done by analyzing the data schema, understanding the business rules, and consulting with subject matter experts. Once the unique columns or column combinations are identified, various techniques can be employed to detect and handle duplicates.

One common approach is to use SQL queries or programming languages like Python or R to check for distinct values in the relevant columns. This can be done by grouping the data and counting the occurrences of each unique combination. Any combination with a count greater than one indicates the presence of duplicates.

Another technique involves sorting the data and comparing adjacent rows for duplicates. This can be particularly useful when dealing with large datasets or when the unique columns are not easily identifiable.

Uniqueness checks: Industry use cases

Uniqueness checks are essential in various industries, including:

In the financial sector, uniqueness checks are critical for ensuring the accuracy of transactions, preventing fraud, and maintaining regulatory compliance. For example, each bank account number or credit card number should be unique to avoid unauthorized access or mishandling of funds.

In the healthcare industry, uniqueness checks are vital for patient data management. Each patient should have a unique identifier, such as a medical record number or a combination of personal information, to ensure accurate record-keeping and prevent potential medical errors.

In retail, uniqueness checks are important for inventory management and customer data. Each product should have a unique identifier, such as a Stock Keeping Unit (SKU), to track stock levels accurately. Similarly, customer data should be unique to avoid duplicate records and ensure personalized marketing efforts.

In the telecommunications sector, uniqueness checks are valuable for managing customer accounts and billing. Each customer should have a unique account number or a combination of identifiers to prevent billing errors and ensure accurate record-keeping.

Uniqueness checks are a critical component of data quality management, ensuring data integrity, preventing data redundancy, and enabling accurate analysis and decision-making across various industries.

Referential integrity checks

Referential integrity checks are a type of data quality check that ensure the consistency and accuracy of relationships between tables or datasets. Referential integrity is a key concept in relational database management systems (RDBMS) and is essential for maintaining data integrity and preventing data corruption.

Referential integrity checks are important because they help to ensure that data relationships are valid and consistent across different tables or datasets. This is particularly crucial in scenarios where data is being shared, combined, or integrated from multiple sources. By enforcing referential integrity, organizations can avoid data inconsistencies, duplicates, and orphaned records, which can lead to inaccurate reporting, analysis, and decision-making.

To perform referential integrity checks, organizations typically implement foreign key constraints in their databases. A foreign key is a column or set of columns in one table that references the primary key of another table. When data is inserted, updated, or deleted, the RDBMS checks the foreign key values against the corresponding primary key values in the referenced table to ensure that the relationships are valid.

Referential integrity checks: Industry use cases

Referential integrity checks are particularly important in industries where data consistency and accuracy are critical, such as:

In the financial sector, referential integrity checks are essential for ensuring the accuracy of transactions, account balances, and customer information across different systems and databases.

In healthcare, referential integrity checks help to maintain the integrity of patient records, ensuring that medical data is correctly associated with the right patients and that relationships between different medical entities (e.g., diagnoses, treatments, and medications) are consistent.

In e-commerce platforms, referential integrity checks ensure that order data, customer information, and product details are accurately linked and consistent across different databases and systems.

In supply chain management systems, referential integrity checks help to maintain the accuracy of relationships between suppliers, products, orders, and shipments, ensuring that data is consistent across different stages of the supply chain.

By implementing robust referential integrity checks, organizations can enhance the reliability and trustworthiness of their data, enabling better decision-making, improved operational efficiency, and increased customer satisfaction.

String pattern checks

String pattern checks are a type of data quality check that validate whether text data conforms to predefined patterns or formats. These checks are crucial for ensuring data consistency and accuracy, especially in scenarios where data entry errors or formatting issues can lead to downstream problems.

The importance of string pattern checks lies in their ability to detect and prevent issues related to text data. For example, they can identify invalid email addresses, incorrectly formatted phone numbers, or improperly structured product codes. By catching these errors early in the data pipeline, organizations can avoid potential issues such as failed communication attempts, inaccurate customer information, or inventory management problems.

To perform string pattern checks, organizations typically define regular expressions (regex) that represent the expected format or pattern of the text data. These regular expressions are then applied to the data, and any values that do not match the specified pattern are flagged as potential errors.

String pattern checks: Industry use cases

Industry examples of string pattern checks include:

E-commerce: Validating product codes, SKUs, and other inventory identifiers to ensure accurate tracking and management.

Healthcare: Checking patient identifiers, such as medical record numbers or Social Security numbers, to maintain data privacy and integrity.

Finance: Verifying account numbers, credit card numbers, and other financial data to prevent errors in transactions and reporting.

Telecommunications: Ensuring phone numbers and other contact information are correctly formatted for accurate billing and customer communication.

By implementing string pattern checks, organizations can improve the overall quality of their data, reduce errors, and enhance downstream processes that rely on accurate and consistent text data.

The role of data catalogs in data quality

Data catalogs are centralized repositories that store metadata about an organization's data assets, including their location, structure, and lineage. They act as a single source of truth for data, enabling users to discover, understand, and access data more efficiently.

Data catalogs play a crucial role in facilitating data quality checks by providing a comprehensive view of an organization's data landscape. They allow data professionals to:

• Understand Data Lineage: By tracking data lineage, data catalogs help users understand the origin of data, the transformations it has undergone, and its relationships with other data sources. This information is valuable for performing data quality checks and identifying potential issues.

Data catalogs that offer data quality flags on data lineage help data users quickly grasp how data is transformed and where potential issues may dwell.

• Manage Data Definitions: Data catalogs store metadata, including data definitions, business rules, and data quality rules. This metadata can be leveraged to automate data quality checks and ensure consistency across the organization.

What makes data unhealthy? A data catalog that stores the data quality rule that determines data health answers this question for newcomers.

• Collaborate on Data Quality: Data catalogs facilitate collaboration among data professionals by providing a centralized platform for sharing data quality insights, documenting issues, and coordinating remediation efforts.

• Automate Data Quality Checks: Many data catalogs offer built-in data quality capabilities or integrate with third-party data quality tools. This enables organizations to automate data quality checks and receive alerts when issues are detected.

By leveraging data catalogs, organizations can streamline their data quality management processes and realize several benefits, including:

1. Improved Data Governance: Data catalogs support data governance initiatives by providing visibility into data assets, enforcing data quality standards, and enabling better data stewardship.

2. Enhanced Data Reliability: By facilitating data quality checks, data catalogs help organizations maintain high-quality data, reducing the risk of errors and ensuring reliable decision-making.

3. Increased Efficiency: Automating data quality checks and centralizing data quality information in a data catalog can significantly improve the efficiency of data quality management processes.

4. Better Collaboration: Data catalogs foster collaboration among data professionals, enabling them to share data quality insights, best practices, and coordinate remediation efforts more effectively.

5. Regulatory Compliance: In industries with strict data quality regulations, such as finance and healthcare, data catalogs can assist in demonstrating compliance by documenting data quality processes and providing audit trails.

Overall, data catalogs are invaluable tools for organizations seeking to maintain high data quality standards. By providing a centralized repository for data assets, metadata, and data quality rules, data catalogs enable more efficient and effective data quality management practices.

Conclusion

Incorporating essential data quality checks—such as string pattern validation, referential integrity, uniqueness, and freshness—ensures that businesses can trust their data for decision-making. However, manual oversight of these checks can be time-consuming and prone to error. By leveraging a data catalog, businesses can automate and centralize their data quality efforts, providing transparency, traceability, and actionable insights to maintain high standards of data integrity across the enterprise. A strong data catalog not only streamlines quality checks but also helps unlock the full potential of your data.

Keen to learn more about how a data catalog can help you take charge of data quality? Book a demo with us to learn more.