Unhealthy, poorly managed data results in significant losses, whether in time, finances, or reputation. Healthy data, however, offers significant value to your organization’s team members who leverage it—facilitated by its discoverability, accessibility, and trustworthiness.
The health of your organization's data is connected to the strength of its profiling. Data profiling—or data archaeology—refers to the process of reviewing, analyzing, and cleansing data. This process is conducted to understand your organization’s data structure, characteristics, integrity, and quality.
In this article, we’ll unpack data profiling, covering its functions, benefits, tools, and best practices.
Data profiling: Functions and benefits
Data profiling functions to analyze and understand the characteristics and quality of data in a dataset. This process involves the generation of reports that examine the data at both column and table levels, offering a detailed overview of its attributes and identifying potential issues.
At the column level, reports focus on statistical measures and metadata to provide insights into the distribution and quality of the data. These reports include information on:
- Minimum and maximum values, which indicate the range of the data
- Mean and mode, which reveal the average and most common values
- Percentiles, which show the data distribution across intervals
- Standard deviation, which indicates data variability
- Frequency, which highlights the repetition of specific values
- Variation, which shows data diversity and the aggregated sum of values within a column
The reports also detail metadata, such as data types (such as integer, string, or date), indicating the nature of the data stored in each column, the length of data entries, uniqueness or the presence of duplicates, the occurrence of null values, and typical string patterns, among others.
At the table level, the focus shifts to:
- Structural aspects, including primary key analysis to ensure each row is unique
- Foreign key analysis to maintain proper relationships between tables
- Referential integrity analysis to confirm that links across tables are consistent and valid
4 data profiling benefits
By profiling your data, your organization can benefit from increased data quality, optimized crisis management, centralized information, and enhanced decision-making.
- Increased data quality. Data profiling identifies data inaccuracies, inconsistencies, and anomalies, leading to cleaner, more reliable datasets. This improvement in data quality ensures your business operates on accurate and up-to-date information—reducing avoidable errors.
- Optimized crisis management. By understanding your data landscape, your organization can quickly identify and respond to data-related issues, minimizing the impact of potential crises—often before they arise. Moreover, data profiling enables proactive detection of vulnerabilities and risks, facilitating prompt resolution and maintaining business continuity.
- Centralized information. Data profiling helps consolidate data from diverse sources into a coherent framework, making relevant information more accessible and manageable. This centralization supports enhanced data governance, simplifies the process of data analysis and reporting, and ultimately enables your team members to derive maximum value out of the data.
- Enhanced decision-making. A clear understanding of data quality and structure yields informed decisions. High-quality, well-profiled data is a foundation for analytics and business intelligence (BI) efforts, offering insights that drive strategic initiatives and competitive advantage.
Data profiling tools
The tools and methodologies you adopt will depend on the type of data profiling you opt for, its intended purpose, and the nature of the data itself. There are three forms of data profiling: content discovery, structure discovery, and relationship discovery.
- Content discovery refers to the examination of the actual content within the data, focusing on identifying inaccuracies, inconsistencies, anomalies, or patterns that could indicate underlying data quality issues.
- Structure discovery refers to analyzing the organizational format and schema of the data, including data types, formats, and adherence to data standards. It ensures the data is logically and consistently organized and can be efficiently processed and analyzed.
- Relationship discovery focuses on understanding the connections and associations between different data elements or datasets, aiding data integration and leveraging relational insights for subsequent analyses.
Data profiling tools encompass a range of functionalities, from basic analysis to advanced statistical examinations. Here are specific capabilities and how they contribute to effective data profiling.
Values analysis
This function enables users to perform a detailed examination of data values across columns, providing insights into the presence of null values, unique values, and the distribution of positive, negative, and zero values.
Statistical analysis
This feature offers a deep dive into the variability and distribution of data by calculating minimum, maximum, and mean values; standard deviation; skewness; and kurtosis.
Frequency analysis
This tool calculates the frequency of each value within a column, aiding in the identification of common or repetitive data points. This analysis is particularly useful for spotting patterns or outliers that may not be immediately apparent.
Histograms
Histograms provide a visual representation of data distribution, making it easier to spot irregularities in data spread and concentration. This can highlight potential issues in data quality or areas that need further investigation.
Text field analyzer
This functionality is designed to analyze raw text data, determining the actual data types and identifying inconsistencies in data entry. Its value is in ensuring data consistency and accuracy—especially in free-text fields.
Scatter plot
Scatter plots allow for the visualization of relationships between two or three variables, helping to uncover correlations or trends that may not be evident through traditional analysis methods.
Overlap analysis
This feature identifies overlapping values across different tables or datasets, foundational for ensuring data integrity and consistency across an organization’s data ecosystem.
Data explorer
Automating the exploration of tables or views within a database, this tool simplifies the process of data profiling by quickly identifying key characteristics and potential issues across a broad dataset.
Data profiling process steps and best practices
Consider the following four data profiling stages:
- Initiate data profiling early to determine the data's fitness for analysis, guiding the project's continuation or halt decision
- Detect and rectify quality problems within the original data before it migrates to the intended database
- Uncover quality issues amendable through extract, transform, and load (ETL) during the data's transition from source to target, identifying the need for further manual intervention
- Discover unexpected business rules, hierarchies, and key relationships to refine the ETL strategy accordingly
On the best-practices front, analyzing the percentage of zero, blank, or null values enables the identification of missing data, guiding the setup of default values. Additionally, assessing the minimum, maximum, and average string lengths informs the selection of appropriate data types and sizes for the target database, optimizing performance.
While more advanced, key integrity checks help ensure the completeness of keys and identify orphan keys, thereby preventing complications in ETL processes and subsequent analyses. Cardinality assessments help you to accurately understand relationships between datasets, supporting the correct execution of joins by BI tools. Furthermore, evaluating pattern and frequency distributions aids in verifying the correct formatting of data fields, particularly those used in outbound communications—ensuring the reliability and effectiveness of the data.