MDM Data Governance & Quality Management

Question 1

What is the primary goal of data governance?

Accepted Answer

To ensure data is managed properly and securely

Answer

Data governance establishes policies, processes, and responsibilities to ensure that an organization's data is managed effectively and securely throughout its lifecycle. Its primary goal is to maintain data quality, compliance, and availability, enabling reliable decision-making and mitigating risks. This structured approach ensures data assets are trustworthy and used appropriately.

Question 2

Which of the following best defines data quality?

Accepted Answer

The usability and accuracy of data

Answer

Data quality refers to the overall fitness of data for its intended purpose. It encompasses characteristics like accuracy, completeness, consistency, timeliness, and validity. High-quality data is reliable, usable, and essential for effective business operations and informed decision-making.

Question 3

What role does a data steward play?

Accepted Answer

Enforcing data governance policies

Answer

A data steward is a key role within a data governance framework, responsible for the practical implementation and enforcement of data governance policies. They ensure data quality, manage data definitions, resolve data issues, and act as a liaison between data users and IT. Their work is crucial for maintaining the integrity and usability of data assets.

Question 4

Which metric is commonly used to evaluate data completeness?

Accepted Answer

Null value percentage

Answer

Data completeness refers to the degree to which all required data is present. The null value percentage is a direct metric for evaluating completeness, as it quantifies the proportion of missing values in a dataset or specific fields. A high null value percentage indicates poor data completeness, suggesting that critical information is absent.

Question 5

How can organizations improve data quality?

Accepted Answer

By implementing data validation and cleansing

Answer

Organizations can significantly improve data quality by implementing robust data validation and cleansing processes. Data validation involves checking data against predefined rules to ensure accuracy and consistency at the point of entry or ingestion. Data cleansing then identifies and corrects or removes inaccurate, incomplete, or inconsistent data from existing datasets, ensuring data reliability.

(MDM) Informatica Master Data Management Certification Practice Test

(MDM) Informatica Master Data Management Certification Practice Test

MDM Data Governance & Quality Management