Data Governance and Profiling

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Questions and Answers

What is the main purpose of data profiling?

To examine simple and basic statistics in the data (correct)

To validate data with predefined rules

To design a new database schema

To discover relationships between parts of the data

What is an example of a data quality issue that can be identified through data profiling?

Phone numbers without the correct number of digits (correct)

Phone numbers with an area code

State fields with two-letter abbreviations

Dates in the format YYYY/DD/MM

What is the purpose of content discovery in data profiling?

To discover relationships between parts of the data

To look more closely into individual attributes and data values (correct)

To design a new database schema

To examine simple and basic statistics in the data

What is an example of a data dependency that can be identified through data profiling?

A key relationship between database tables Signup and view all the answers

What is the main purpose of structure discovery in data profiling?

To validate that data is consistent, formatted correctly, and well structured Signup and view all the answers

What is an example of a data quality issue that can be identified through content discovery?

Null values in a field Signup and view all the answers

What is the main purpose of relationship discovery in data profiling?

To discover relationships between parts of the data Signup and view all the answers

What is an example of a predefined rule that can be used for data validation?

A transaction amount should always be more than $0 Signup and view all the answers

What is the primary goal of data profiling?

To understand the central tendency, spread, and variability of the data Signup and view all the answers

Which data profiling technique involves identifying duplicate records?

Data uniqueness Signup and view all the answers

What is the main purpose of analyzing data patterns?

To understand the format, structure, and regularities in data values Signup and view all the answers

What can be indicated by outliers, unexpected distributions, and extreme values in the data?

Data quality issues or data entry errors Signup and view all the answers

What is the benefit of analyzing relationships and dependencies between variables?

To reveal correlations, associations, or dependencies between variables Signup and view all the answers

What is the purpose of calculating the percentage of missing values for each variable?

To determine the extent of missing data and its potential impact on analysis and decision-making Signup and view all the answers

What is the benefit of visualizing the distribution of variables?

To understand the shape, skewness, and presence of anomalies in the data Signup and view all the answers

What is the main purpose of data profiling in data governance?

To understand the characteristics of the data and identify data quality issues Signup and view all the answers

What is the primary goal of data quality management?

To ensure data meets the desired quality standards Signup and view all the answers

What is the main objective of data profiling?

To understand data structure, content, and interrelationships Signup and view all the answers

What is a consequence of poor data quality?

Flawed decision-making and operational inefficiencies Signup and view all the answers

What does data profiling involve, in terms of data quality assessment?

Assessing the risk of performing joins on the data Signup and view all the answers

What is an aspect of data quality?

Timeliness Signup and view all the answers

What is the purpose of data cleansing?

To correct errors and inconsistencies in the data Signup and view all the answers

What is an activity involved in data profiling?

Collecting descriptive statistics Signup and view all the answers

What is a benefit of data quality management?

Improved decision-making Signup and view all the answers

Study Notes

Data Governance

Data profiling involves identifying distributions, key candidates, foreign-key candidates, functional dependencies, embedded value dependencies, and performing inter-table analysis.

Data Profiling

Structure discovery: validating data consistency, format, and structure, and examining simple statistics (minimum, maximum, means, medians, and standard deviations).
Examples: identifying date patterns (YYYY-MM-DD or YYYY/DD/MM) and phone number formats (correct number of digits).

Content Discovery

Examining individual attributes and data values to identify data quality issues.
Helps find null values, empty fields, duplicates, incomplete values, outliers, and anomalies.
Example: 'State' field containing two-letter abbreviations or fully spelled-out city names, and validating databases with predefined rules.

Relationship Discovery

Discovering relationships between data parts, critical for designing database schemas, data warehouses, or ETL flows.
Examples: key relationships between database tables, references between cells or lookup cells in spreadsheets, and joining tables based on key relationships.

Data Profiling Techniques

Data Completeness

Involves identifying missing values and calculating the percentage of missing values for each variable.
Helps determine the extent of missing data and its potential impact on analysis and decision-making.

Data Uniqueness

Identifying duplicate records to maintain data integrity.
Highlights data quality issues, such as data entry errors or system glitches.

Data Patterns

Analyzing data patterns to assess format, structure, and regularities in data values.
Useful for understanding naming conventions, data formats, and potential data quality issues.

Data Anomalies

Detecting anomalies to identify unexpected or erroneous data values.
Outliers, unexpected distributions, and extreme values can indicate data quality issues or data entry errors.

Data Dependencies

Analyzing relationships and dependencies between variables to understand how different variables or attributes are related.
Reveals correlations, associations, or dependencies between variables, useful for data exploration and modeling.

Data Quality Management

Data quality: the fitness of data for its intended use, encompassing accuracy, completeness, consistency, timeliness, and relevance.
Data quality management: ensuring data meets desired quality standards.
Poor data quality can lead to incorrect insights, flawed decision-making, and operational inefficiencies.

Data Quality Management Process

Profiling: reviewing source data, understanding data structure, content, and interrelationships.
Cleansing and Remediation: correcting data quality issues.
Monitoring and Validation: ensuring data quality standards are met.
Maintenance and Verification: ongoing data quality management.

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Description

This quiz covers data governance, data profiling, and structure discovery, including identifying distributions and dependencies, validating data consistency, and examining basic statistics.