Data Masking: Data masking is the process of replacing sensitive information with fictitious data, while preserving the overall structure, usability, and functionality of the original data. This skill should be measured in the test to assess the candidate's ability to protect sensitive data and ensure compliance with data security regulations.
Data Subset: Data subset refers to the process of creating a smaller, representative sample of a larger dataset. This skill should be measured in the test to evaluate the candidate's proficiency in extracting relevant subsets of data for analysis or testing purposes, which can help optimize performance and reduce resource requirements.
Data Generation: Data generation involves creating synthetic data that mimics real-world data. This skill should be measured in the test to assess the candidate's capability to generate large volumes of test data for various scenarios, such as performance testing, without relying on production data.
Rule Simulator: A rule simulator is a tool used to test and validate data quality rules and their impact on datasets. This skill should be measured in the test to evaluate the candidate's proficiency in simulating and verifying data quality rules, ensuring the accuracy, completeness, and validity of data.
Sequence Data Generation: Sequence data generation involves generating sequential values, such as unique identifiers or timestamps, in a specific order. This skill should be measured in the test to assess the candidate's ability to generate ordered sequences of data, which can be useful for maintaining data integrity and consistency in various applications.
Dictionary Data Generation: Dictionary data generation is the process of creating data based on predefined lists or dictionaries containing specific values. This skill should be measured in the test to evaluate the candidate's proficiency in generating data that adheres to predefined standards or criteria, facilitating data analysis and comparisons.
Data Quality: Data quality refers to the level of accuracy, completeness, consistency, and reliability of data. This skill should be measured in the test to assess the candidate's understanding of data quality concepts and their ability to identify and address data quality issues, ensuring the reliability and usability of data within an organization.
Data Profiling: Data profiling involves analyzing and understanding the structure, content, and quality of data. This skill should be measured in the test to evaluate the candidate's expertise in accurately assessing data quality, identifying data anomalies, and providing insight into data patterns, which is crucial for making informed data-driven decisions.
Data Cleansing: Data cleansing is the process of identifying and correcting erroneous, incomplete, or irrelevant data. This skill should be measured in the test to assess the candidate's ability to identify and rectify data quality issues, ensuring data accuracy, consistency, and integrity prior to analysis or integration with other systems.
Data Standardization: Data standardization involves transforming data into a consistent and predefined format, typically conforming to specific rules or guidelines. This skill should be measured in the test to evaluate the candidate's proficiency in converting data into a uniform format, facilitating data integration, comparability, and efficient data management.
Duplicate Detection: Duplicate detection is the process of identifying and removing duplicate records within a dataset. This skill should be measured in the test to assess the candidate's ability to develop algorithms or techniques for detecting and eliminating duplicate data, enhancing data accuracy, and reducing redundancy.
Data Validation: Data validation is the process of ensuring that data adheres to predefined rules, constraints, or requirements. This skill should be measured in the test to evaluate the candidate's expertise in validating data against specified criteria, detecting errors or inconsistencies, and ensuring data reliability, integrity, and adherence to business rules.
Data Enrichment: Data enrichment involves enhancing existing data by adding additional information, such as demographics, geolocation, or external datasets. This skill should be measured in the test to assess the candidate's proficiency in augmenting data with valuable insights, improving data completeness, accuracy, and overall quality for better decision-making and analysis.
