Data Governance & Quality Management Flashcards

What is the primary goal of data governance?

Answer: To ensure data is managed properly and securely

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.

Which of the following best defines data quality?

Answer: The usability and accuracy of data

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.

What role does a data steward play?

Answer: Enforcing data governance policies

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.

Which metric is commonly used to evaluate data completeness?

Answer: Null value percentage

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.

How can organizations improve data quality?

Answer: By implementing data validation and cleansing

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.

Which policy area does data retention fall under?

Answer: Data governance policy

Data retention policies dictate how long specific types of data must be kept and when they should be securely disposed of. This critical aspect falls directly under data governance, as it ensures compliance with legal, regulatory, and business requirements. It also helps manage storage costs and mitigate risks associated with holding unnecessary data.

What is a data quality dashboard?

Answer: A visual interface displaying data quality indicators

A data quality dashboard provides a visual, real-time overview of an organization's data quality status. It displays key data quality indicators (DQIs) such as completeness, accuracy, consistency, and validity, often using charts and graphs. This tool helps stakeholders quickly identify data issues, monitor improvement efforts, and make informed decisions about data management.

Why is metadata important in data governance?

Answer: It gives essential information about data’s structure and source

Metadata, often described as "data about data," is crucial in data governance because it provides essential context and information about an organization's data assets. It details data's structure, definitions, source, lineage, usage, and ownership. This comprehensive understanding enables effective data management, quality control, and compliance by clarifying what data exists and how it should be used.

What is the benefit of implementing a data governance framework?

Answer: It enhances data consistency and compliance

Implementing a data governance framework provides numerous benefits, primarily by enhancing data consistency and ensuring compliance with regulations. It establishes clear rules, roles, and processes for data management, leading to more reliable and standardized data across the organization. This structured approach reduces errors, improves decision-making, and helps meet legal and industry requirements.