Features of data quality tools mainly consider the dimensions or the metrics that define quality. These solutions can support some or all of the functions as mentioned below to deliver useful end results:
Data cleansing: It is the process of removing redundant, incorrect, and corrupt data. It is sometimes referred to as data cleaning or data scrubbing. Being one of the critical stages in data processing, most data quality tools have this feature. A few of the common data inaccuracies include incorrect entries and missing values.
Data standardization: It is a major step in organizing data. It involves converting data into a common format which makes it easier for users to access and analyze the data. This stage fulfills one of the parameters of data quality—consistency. Bringing the data into a single common format makes sure that data is consistent. Data standardization plays a key role in achieving accuracy which is another factor in data quality. It helps by giving users access to the latest cleansed and updated data.
Data profiling: Data profiling is the process of analyzing data, understanding the structure of data, and identifying the potential projects for the specified data. Data is minutely analyzed using analytical tools to detect characteristics like mean, minimum, maximum, and frequency.
Data deduplication: It is a process to eliminate excessive copies of data and reduce storage requirements. It is also called intelligent compression or single-instance storage or data dedupe.
Data validation: This feature ensures that data quality and accuracy are in place. In automated systems, there is minimal or almost no human supervision when the data is entered. This makes it essential to check that the data entered is correct. Common types of data validation include data check, code check, range check, format check, and consistency check. There also are certain data quality rules defined for data management platforms.
Extract, transform, and load (ETL): When organizations advance in the technology strategy, data from existing systems are transferred to the new systems. ETL forms a vital task of the data migration process. The end goal is to maintain data quality for the data that is being migrated. ETL stands third in the phases of the data quality lifecycle. Other phases are quality assessment, quality design, and monitoring. It involves extracting data from the data sources, transforming it by deduplicating it, and loading it into the target database.
Master data management (MDM): This feature manages quality data by organizing, centralizing, and enriching data. It includes non-transactional data like customer data and product data. MDM is important for enterprise data management.
Data enrichment: This feature is the process of enhancing the value and accuracy of data by integrating internal and external data with the existing information.
Data catalog: Data catalog hosts data and metadata to help users with their data discovery. Data quality monitoring tools have this feature to increase transparency in workflows.
Data warehousing: Data warehousing focuses on unifying data from various data sources. It ensures enterprise data quality by improving the accuracy of data.
Data parsing: Data usually is conformed to specific formats. For example address, telephone number, and email address all have data patterns. Parsing helps with such address verifications and also if the telephone numbers are conforming to the patterns.
Other features of data quality software: ERP Capabilities and File Capabilities.
