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Data profiling is a critical process in data management that helps organizations ensure the quality of their data before it is used for analysis, reporting, or decision-making. Here's a summary of the key aspects of data profiling:

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Definition and Importance

• Data Profiling: The process of examining, analyzing, reviewing, and summarizing data sets to gain insights into their quality.

• Data Quality: Measured based on accuracy, completeness, consistency, timeliness, and accessibility.

• Benefits: Provides a high-level view of data quality, helps identify potential data projects, and is a crucial precursor to data processing and analytics.

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Functions of Data Profiling

1. Review of Source Data:

   • Understanding data structure, content, and interrelationships.

   • Identifying quality issues and potential projects.

2. Improvement of Data Quality:

   • Continuous improvement and measurement of data quality.

   • Also known as data archaeology, assessment, discovery, or quality analysis.

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Types of Data Profiling

1. Structure Discovery:

   • Focuses on data formatting, ensuring uniformity and consistency.

   • Uses statistical analysis to validate data.

2. Content Discovery:

   • Assesses quality of individual data pieces.

   • Identifies ambiguous, incomplete, or null values.

3. Relationship Discovery:

   • Detects connections, similarities, differences, and associations among data sources.

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Data Profiling Process Steps

1. Gathering Data Sources and Metadata:

   • Collecting data from multiple sources along with associated metadata.

2. Data Cleaning:

   • Unifying structure, eliminating duplications, identifying interrelationships, and finding anomalies.

3. Statistical Analysis:

   • Using profiling tools to return statistics (mean, min/max values, frequency, patterns, dependencies, quality risks).

   • Analysing frequency distribution, cross-column relationships, and inter-table connections.

 

Example Analysis

• Frequency Distribution: Examines the occurrence of values in each column to understand their type and usage.

• Cross-Column Analysis: Reveals embedded value dependencies.

• Inter-Table Analysis: Discovers overlapping value sets indicating foreign key relationships.

Data profiling, through these steps, ensures that data professionals can work with clean, consistent, and high-quality data, enabling better decision-making and analytics outcomes.

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