Data profiling is the process of analyzing data to get a basic understanding of what it contains. It examines the data in tables or columns to determine values, patterns, and anomalies distribution. It’s essential to Data Quality Management because it helps business users understand their data and decide whether it meets their needs.
You can perform data profiling on structured or unstructured data, as it focuses on identifying key characteristics of the data, such as its source, quality, consistency, and completeness. It also helps identify potential issues within the dataset, such as missing values or duplicate records.
Algorithms identify dataset characteristics such as mean value, minimum value, maximum value, percentiles, and frequency to examine datasets in minute detail. They then analyze metadata, such as frequency distributions, key relationships, and foreign keys. Finally, they use this information to reveal how those factors align with business standards and goals.
The purpose of data profiling is to:
- Identify and correct issues with the quality of the data
- Explore and understand how the data was generated (i.e., from which source)
- Identify missing values in the data
- Identify duplicate records in the dataset
- Identify how frequently each attribute occurs
- Identify how unique the values on each attribute
- Identify outliers in the dataset
Other Definitions of Data Profiling
- “The process of examining, analyzing, and creating useful summaries of data. The process yields a high-level overview which aids in the discovery of data quality issues, risks, and overall trends. Data profiling produces critical insights into data that companies can then leverage to their advantage.” (Talend)
- “An essential part of how your organization handles its data for several reasons. First, data profiling helps cover the basics with your data, verifying that the information in your tables matches the descriptions. Then it can help you better understand your data by revealing the relationships that span different databases, source applications or tables.” (SAS)
- “A technology for discovering and investigating data quality issues, such as duplication, lack of consistency, and lack of accuracy and completeness. This is accomplished by analyzing one or multiple data sources and collecting metadata that shows the condition of the data and enables the data steward to investigate the origin of data errors.” (Gartner)
Types of Data Profiling
Several types of data profiling techniques help you organize your data the way you want it. They include:
- Structure discovery: Focused on the viability of your data. You can review how the data is stored and formatted and what it contains. Also includes reviewing the tables, fields, and number of datasets and looking at each dataset closely.
- Content discovery: Focused on the quality and content of the dataset. After validating the dataset’s structure, you can review the content, if it’s relevant to the business, and whether it’s stored in the correct database.
- Relationship discovery: The final step – identifying how different datasets connect. Here, you’ll analyze the dataset at a granular level and observe interlinking patterns between them. This helps link relevant datasets between cross-functional teams and produces valuable results.
Benefits of Data Profiling
- Helps identify missing or redundant attributes in existing datasets, which can be removed or combined without affecting the results of your analysis.
- Allows you to find inconsistencies between different records in a dataset, which could indicate errors in how it was entered or modified over time (this could also indicate problems with your database schema).
- Provides a baseline for measuring improvements in future data quality assessments. It allows you to track progress over time and compare results across different organizational systems or processes.
- Allows you to understand your data better, enabling you to create more effective queries and reports.
- Helps leaders make better data-driven decisions by identifying potential issues before they become significant problems. It can also help predict future trends and outcomes.
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