Intelligent Informatica Data Quality Test

When data is of excellent quality, it leads to insights that help the organization make better decisions. This is because high-quality data is accurate, reliable, and relevant, allowing organizations to gain a deeper understanding of their operations, customers, and market trends. With these insights, organizations can identify patterns, trends, and opportunities that may have otherwise been overlooked. By leveraging the power of high-quality data, organizations can make informed decisions that drive growth, efficiency, and competitive advantage.

Accuracy of data refers to the correctness, precision, and reliability of the information. When data is accurate, it is free from errors, inconsistencies, and discrepancies. Therefore, it will pass spell checks, as it will have correct spellings. It will also pass checks, meaning that it will meet the required standards and criteria. Additionally, accuracy ensures that data can pass spot checks, which involve randomly selecting and reviewing a sample of data to ensure its accuracy. Therefore, the correct answer is "All of the above" as it encompasses all these aspects of data accuracy.

For data to be of high quality, it must be unambiguous. This means that the data should be clear and have a single interpretation. Ambiguous data can lead to confusion and errors in analysis and decision-making. Therefore, to ensure accuracy and reliability, it is important for data to be unambiguous.

The given correct answer, "Is not repeated," suggests that when data is free of duplicate values, it means that there are no repeated or identical values present. This implies that each value in the data set is unique and does not occur more than once.

Data quality refers to the accuracy, completeness, and relevance of data. Redundancy, on the other hand, refers to the unnecessary duplication of data. It is not considered an aspect of data quality because redundant data can lead to confusion, increased storage requirements, and potential inconsistencies. Therefore, redundancy is the correct answer as it does not fall under the aspects of data quality.

Data Quality Assurance is the process of verifying the effectiveness of data. This involves ensuring that the data is accurate, complete, and reliable. It includes checking for errors, inconsistencies, and discrepancies in the data, as well as validating its integrity and relevance. By verifying the effectiveness of data, organizations can ensure that the information they are using is trustworthy and can make informed decisions based on it.

Skimming is not a data quality activity. Data quality activities involve processes such as rationalization, validation, and in-depth analysis to ensure that data is accurate, reliable, and consistent. Skimming, on the other hand, refers to quickly scanning or reading through information without detailed analysis or evaluation. Therefore, skimming is not a part of data quality activities.

Scrutinizing data is done practically. This means that it is typically carried out in a practical or realistic manner. It implies that data is carefully examined, analyzed, and evaluated to gain insights or make informed decisions. It suggests that scrutinizing data is not just a theoretical or abstract concept, but rather a hands-on process that involves practical applications and real-world considerations.

Prune is not a tool used when data is not clean inside. The other three options, Extract, Transform, and Load, are commonly used tools in data cleaning processes. Extract involves retrieving data from various sources, Transform involves modifying and structuring the data, and Load involves loading the cleaned data into a database or a data warehouse. However, Prune is not a tool specifically used for data cleaning; it refers to the act of removing unnecessary or unwanted parts of something.

The relevance of data quality means that the data should describe a specific time, period, and location that are relevant to the subject being studied. This ensures that the data accurately represents the context in which the subject exists and is affected by. By including this information, the data becomes more meaningful and useful for analysis and decision-making processes.