What Is Data Profiling?

Data profiling is the process of cleansing and analyzing data to identify its core features for better understanding. Profiling breaks down data elements like its format or structure, precision, consistency and overall quality. The data profiles or summaries that this process yields can give data engineers and analysts an overview of what that data contains, its condition or quality, and how applicable it is to organizational operations.

Understanding Data Profiling

Data profiling aims to yield usable, quality data that organizations can apply to business intelligence. Its results give a snapshot of different areas you could work on to improve data quality, including faulty sources or specific departments.

Types of Data Profiling

Three standard profiling types include:

• Structural: Data analysts use this profiling approach to evaluate data formatting throughout your storage. The aim is to certify that data meets the same formatting standards for consistency. Automated data systems can aid structure review through pattern identification and validation rule compliance.
• Content: These evaluations look at individual and minor details in data input, checking for errors, anomalies and other issues. Validation and verification tools can streamline this type of profiling.
• Relationship: This data profiling procedure reviews metadata to see data use and connections between sets. It can help identify links in data use, structures and other areas, giving you an excellent overview of the configuration and movement of your data within your organization.

Profiling Application Techniques and Tools

The three common tool types and framework techniques you can employ for these profiling types are:

• Column profiling: Column profiling studies table columns to identify repeating values and how often they occur, reviewing for value frequency, duplication and patterns.
• Cross-column profiling: This approach involves two steps — key and dependency analysis. Key analysis searches for a potential primary key within a range of value properties. Dependency analysis highlights patterns or connections between inputs in a set.
• Cross-table profiling: This profiling technique also uses key analysis. It highlights differences or outlier data by reviewing connections columns in different datasets or tables.

You can employ solutions that automate these techniques with AI and machine learning technologies that identify data patterns. Other profiling techniques include key integrity, cardinality and rule validation checks.

Profiling Metrics and Statistical Results

Data professionals also refer to data profiling as data archaeology, as it investigates the culture of data. Through statistics and metrics like mean, mode, median, minimums and maximums, ranges and standard deviation, you uncover the characteristics of your institutional data. Values yielded from the process can point to specific patterns, trends and relationships. Gaps and errors in data, like duplicates, incomplete values, inconsistencies and more, can guide you in improving data processes and governance.

Practical Applications and Challenges of Data Profiling

Data profiling plays a role in data governance and quality management by preparing for and supporting data transformation, cleansing, integration, validation and integrity control. It is a key step in many governance processes.

Challenges in Data Profiling

Common challenges to efficient profiling include large data columns, complex data environments and inadequate systems leading to latent results. To address these challenges, you can use automated solutions that can:

• Manage big data quantities
• Integrate and collaborate with various data sources
• Handle variant data types
• Automate profiling steps like anomaly and error detection
• Utilize automated profiling results visualization tools
• Incorporate metadata management solutions
• Standardize data entry formats