What are Data Quality Checks?

What are Data Quality Checks?

Data quality checks are evaluations that measure metrics related to data quality and integrity. These checks involve identifying duplicate data, checking for mandatory fields, null values, and missing values, applying formatting checks for consistency, and verifying the recency of data. They also include validating row, column, conformity, and value checks for integrity. The goal of these checks is to ensure the accuracy, completeness, reliability, and relevance of data.

• Data Profiling: This method helps find defects by identifying potential incorrect values, which are then restricted from being used while also flagging them for review with other programmers or specialists.
• Auditing: A method to measure data quality based on the review of label accuracy by a domain expert.
• Data Cleansing: The process of cleaning out the database to ensure that the highest quality of data remains.
• Freshness Checks: A metric that looks at the age of data.

What is Data Quality Testing?

Data quality testing is the process of evaluating data for accuracy, consistency, and reliability. It involves running pre-defined tests on datasets to identify any inconsistencies, errors, or discrepancies that could impact the data's usability and credibility. The steps for data quality testing include assessing accuracy, building a baseline, checking consistency, determining data-entry configuration, and evaluating effectiveness.

• Accuracy: How well the data reflects reality.
• Completeness: Whether the data meets expectations of comprehensiveness.
• Consistency: Whether data assets stored in one place match relevant data stored elsewhere.
• Uniqueness: Whether different data sets can be joined correctly to reflect a larger picture.
• Validity: Whether the information is in a specific format, type, or size, and follows business rules and best practices.

What are the Common Data Quality Dimensions?

Data quality dimensions are the standards and rules used to measure and evaluate the data against expectations and requirements. These criteria are based on the purpose and scope of the analysis. Some common data quality dimensions include accuracy, completeness, consistency, uniqueness, and validity.

• Accuracy: The degree to which data accurately represents the real-world situation it is supposed to represent.
• Completeness: The extent to which all required data is present in the dataset.
• Consistency: The degree to which data is consistent, within the same data set or across multiple data sets.
• Uniqueness: The requirement that an entity is represented only once in the data.
• Validity: The degree to which data conforms to defined business rules or constraints.

What are the Techniques Involved in Data Quality Tests?

Data quality tests can involve various techniques, such as data validation, data profiling, and data cleansing. These techniques help organizations ensure that their data meets predefined quality standards. Data validation is the process of checking if the data meets certain criteria. Data profiling involves analyzing the data to understand its quality, structure, and content. Data cleansing is the process of detecting and correcting errors and inconsistencies in data.

• Data Validation: The process of checking if the data meets certain criteria.
• Data Profiling: The process of analyzing the data to understand its quality, structure, and content.
• Data Cleansing: The process of detecting and correcting errors and inconsistencies in data.

How to Ensure Data Accuracy?

Ensuring data accuracy involves implementing data quality frameworks, conducting regular data audits, using automated validation checks, providing training and education, implementing feedback mechanisms, verifying data sources, using data cleansing tools, and maintaining documentation. These methods help identify and fix data errors, anomalies, and inconsistencies early in the ETL process.

What are the Traits of Data Quality?

Data quality has five traits: accuracy, completeness, reliability, relevance, and timeliness.

• Accuracy: How well the data reflects reality.
• Completeness: Whether the data meets expectations of comprehensiveness.
• Reliability: The consistency of the data over time.
• Relevance: Whether the data is applicable and helpful for the matter at hand.
• Timeliness: Whether the data is up-to-date and available when needed.

How Does Secoda Prioritize Data Quality?

Secoda prioritizes data quality by defining it as the degree to which a dataset meets expectations for accuracy, completeness, validity, and consistency, using various measures to prevent data issues, inconsistencies, errors, and anomalies.

A key component for ensuring data quality in Secoda is the use of Secoda Monitoring, which allows users to configure monitors and receive alerts about changes. Secoda's AI-powered platform also use various metrics for measuring data quality, such as the ratio of data to errors, number of empty values, data transformation error rates, amounts of dark data, email bounce rates, data storage costs, and data time-to-value.

The DQ Score is not only useful for quickly assessing the overall quality of a dataset, but dimensional scores can also be used to identify areas of deficiency. For example, an asset might score perfectly on accuracy but low on reliability. This approach encourages data producers and consumers to work together to improve the quality of the data they provide.

• Secoda Monitoring: This feature allows users to configure monitors and receive alerts about changes, helping to maintain data quality.
• Data Quality Metrics: Secoda uses various metrics to measure data quality, such as the ratio of data to errors, number of empty values, data transformation error rates, amounts of dark data, email bounce rates, data storage costs, and data time-to-value.
• Data Quality Score (DQ Score): Secoda uses Airbnb's DQ Score, a single, high-level score from 0–100 that assesses the quality of data assets. The score uses categorical thresholds to indicate the quality of the data as "Poor", "Okay", "Good", or "Great".
• Dimensional Scores: These scores are used to identify areas of deficiency in a dataset. For example, an asset might score perfectly on accuracy but low on reliability.
• Collaboration: The use of the DQ Score encourages data producers and consumers to work together to improve the quality of the data they provide.
• Automated Documentation: Secoda maintains automated documentation to ensure that all data processes and changes are properly recorded and can be reviewed when necessary.