Our Knowledge Hub is a resource center that includes commonly asked questions, a glossary of data quality terms, and helpful comparisons of popular data quality tools. It’s designed to answer your questions and deepen your understanding of data quality.
Frequently Asked Question (FAQ)
What is data quality, and why is it important?
Data quality refers to the accuracy, consistency, and reliability of data. High-quality data is essential for informed decision-making and operational efficiency.
How can data quality be measured?
Data quality is often measured by metrics like accuracy, completeness, consistency, and timeliness. These metrics vary depending on the organization and industry.
What is Master Data Management (MDM)?
MDM is the practice of creating a single, authoritative source for critical business data, ensuring that all departments access accurate and consistent information.
Glossary of Terms
A comprehensive glossary of data quality terms is essential for understanding the complexities of data management. Below is an expanded list of key terms and their definitions:
Accuracy
The degree to which data correctly describes the real-world object or event it represents. Accurate data is free from errors and reflects true values.
Completeness
The extent to which all required data is present. Incomplete data lacks necessary information, which can impede decision-making processes.
Consistency
The absence of differences when comparing two or more representations of a thing against a definition. Consistent data maintains uniformity across different datasets and systems.
Data Cleansing
The process of detecting and correcting (or removing) corrupt or inaccurate records from a dataset. This ensures that data is accurate and reliable.
Data Governance
The overall management of data availability, usability, integrity, and security in an organization. It involves establishing policies and procedures to ensure data quality and compliance.
Data Profiling
The process of examining data from an existing information source to collect statistics or informative summaries about that data. It helps in understanding data characteristics and identifying potential data quality issues.
Data Quality
The measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability, and how up-to-date it is. High-quality data is fit for its intended use in operations, decision-making, and planning.
Data Stewardship
The management and oversight of an organization's data assets to help provide business users with high-quality data that is easily accessible in a consistent manner. Data stewards are responsible for ensuring data policies and standards are implemented and adhered to.
Data Validation
The process of ensuring that data is correct and useful. It involves checking data for accuracy and completeness to ensure it is suitable for its intended purpose.
Master Data Management
A comprehensive method of enabling an enterprise to link all of its critical data to a common point of reference. MDM streamlines data sharing among personnel and departments.
Metadata
Data that provides information about other data. It helps users understand the context, structure, and meaning of data, facilitating its management and use.
Reference Data
A type of data that defines permissible values to be used by other data fields. It is used to classify or categorize other data and is typically static or changes infrequently.
Timeliness
The degree to which data is up-to-date and available within a useful time frame. Timely data ensures that information is current and can be relied upon for decision-making.