What is Data Profiling: Examples, Techniques, & Steps

A data-driven environment is a necessity that you can focus on to enhance decision-making and business performance. To achieve this, the primary focus should be on the quality of the data that is being incorporated into the analytical workflow of your organization. The practice of data profiling has become a pivot in this conversation.

This article highlights the critical process of data profiling: why it is important, the commonly used tools and techniques, and how they can influence the data integration process.

What Is Data Profiling?

Data profiling is the process of reviewing, analyzing, and summarizing a dataset to understand its structure and fix any issues that may exist. It goes beyond maintaining data consistency and delves into aspects like reliability, accuracy, and validity of data.

This process helps you understand your data better by discovering the relationship between different data sources (tables and databases) to understand data flow within your organization. Data profiling also involves employing different methods, like column and data pattern profiling, to analyze data.

By ensuring clean and accurate data, data profiling empowers your organization's strategies and ensures data aligns with your needs. This can assist you in identifying trends, planning a robust governance framework, or analyzing customer behavior more effectively.

Why Is Data Profiling Important?

Here are some of the most prominent benefits of employing data profiling:

Helps Assess Data Quality

Data profiling provides a concise map of the problems present in your data, including missing values, inconsistencies, and anomalies. This enables you to gain insights into your data, helping you enhance data quality by resolving issues before analysis.

Pinpoint Data Issues

Data profiling lets you check for incomplete records, violation of uniqueness in the primary key, inconsistent formats, and all other issues that may affect data quality. It allows you to address these issues before analyzing the data, as they might bias your results.

Supports Data Governance

By enhancing data quality, profiling strengthens the foundation of effective data governance. This empowers you to gain a better understanding of the data you use, ultimately impacting your organization’s data governance policies.

Ensures Data Compliance

Maintaining compliance with industry standards is an essential step in ensuring responsible use of data. Data profiling enables you to comply with governance policies by adhering to regulatory standards, security and privacy policies, and industry requirements. This leads to an enhanced security and privacy environment mitigating compliance risks.

Assists Data Integration Process

Understanding the dataset and its relationships is an important step before integrating data from multiple sources into a centralized location. This provides knowledge of how you should link the data and what considerations you must know before engaging in the data integration process.

What Are the Common Types of Data Profiling?

Here are the three main types of data profiling:

Structure Discovery

Structure discovery focuses on understanding data formats and the overall structure of your data to ensure consistency throughout the database. Common methods performed in structured discovery are data type identification, identifying primary values, etc.

For example, in a university administration database, structure discovery explains the presence of tables like “Students” and “Courses” with “StudentID” and “CourseID” fields.

Content Discovery

This process involves examining each database value closer to determine any missing or error values. The data must be error-free to process and integrate with the existing dataset. Content discovery helps to determine anomalies, inconsistencies, or other data quality issues that you must resolve before further analysis.

For example, the “Students” table contains missing values of the “GPA” column. This content discovery highlights incomplete academic records of students.

Relationship Discovery

Relationship discovery allows the identification of connections between different data elements.

For example, in an educational institute, relationship discovery analyzes the relationship between different fields and tables, like the association between “Students” and “Courses,” enabling an understanding of how different fields are interconnected with each other.

Data Profiling Techniques

This section highlights the techniques used to perform data profiling to determine the underlying meaning of your data. Here are the key data profiling techniques:

Column Profiling

Column profiling is the process of analyzing each column in a table. It involves tasks like checking for missing values, identifying data types, and counting the distinct values and their frequencies.

Cross-Column Profiling

Cross-column profiling includes describing the relationship between data in different columns within the same table. It has key and dependency analysis. The key analysis helps you determine columns with unique values in each row.

On the other hand, dependency analysis helps you explore the dependencies between columns in a single table. Dependency analysis also helps determine functional dependencies, a measure of which fields directly affect other columns.

Data Pattern Profiling

Data pattern profiling is a technique that specifically focuses on identifying the recurring underlying patterns within data. This technique includes finding frequency distribution, formatting trends, and relationships between data points in a regular pattern.

Data Distribution Profiling

Data distribution profiling allows you to analyze the spread of data points in a specific column. It can help you understand how the data is distributed.

How to Perform Data Profiling?

The most commonly followed data profiling steps include:

Step 1: Gather Data from Sources

The first step in performing data profiling is to gather data. This includes extracting data from multiple sources, such as databases or some other storage platform, into a centralized location for further tasks.

Step 2: Perform an Initial Exploration

After extracting the data and storing it in a destination, explore and validate its structure, content, and relationship depending on your use cases. This might involve understanding how the data is organized, particularly the tables and columns or the different data types associated with each column.

Step 3: Assess the Quality of the Data

Evaluate the data quality by checking for its completeness, consistency, and reliability. Analyzing the dataset to identify potential errors like missing values, inconsistencies in formatting, or outliers can help you establish data quality rules.

By analyzing these data quality issues, you can establish a set of rules that will define the expected format, content, and validity of your data. These rules can then be used in the next step for data validation.

Step 4: Validate the Data against Predefined Rules

Establish data validation rules and regulations based on your organization's standards and data usage. This might involve ensuring data adheres to specific guidelines, describing the required value range for particular fields, checking mandatory fields, etc.

Data validation helps you to identify any discrepancies with your predefined rules and allows for data correction.

Step 5: Document the Findings

A continuous step of data profiling involves documenting the whole process, from extracting the data to validating it. Documentation includes summarizing the key findings from all the steps performed, detailing all the data quality issues, outlining any data transformation step required, and reporting the results.

By following these steps, you can effectively perform data profiling and gain valuable insights into the quality and structure of your data before using it for analysis.

Data Profiling Vs. Data Cleansing Vs. Data Mining

Understanding the key differences between data profiling, cleansing, and mining is essential for getting the most value out of your data. Here are the critical differences to consider:

Data Profiling Tools You Should Know

There are popular data profiling tools available that you can choose from to streamline your data profiling process. Here are a few of the most popular ones:

Data Fabric by Talend

Talend Data Fabric is a robust data integration and management platform that helps you leverage your data assets more effectively. It enables you to extract data from various sources, such as databases and files, for profiling without complex coding.

The key features of Data Fabric are:

• Talend Data Fabric can automatically cleanse incoming data using machine learning-powered deduplication, validation, and standardization.
• The built-in Talend Trust Score helps you evaluate the reliability of your data. This lets you easily identify which information is safe to share and which datasets require additional cleansing.
• Talend Data Fabric can also enhance your data by integrating it with external sources such as postal validation codes or business identifiers. This helps you gain a more comprehensive understanding of your data, leading to better decision-making.

Astera Centerprise

Astera Centerprise is a powerful data integration that can greatly assist you in understanding and improving the quality of your data. You can design dataflows and use the built-in job scheduler for automated and repeated execution. Additionally, workflows in Astera simplify the visualization and implementation of complex task sequences, allowing you to streamline your data processes.

Here are some of the crucial features of Astera Centerprise:

• With Astera, you have the flexibility to define your own validation rules based on your specific data quality requirements. This helps you effectively identify and rectify duplicates, missing fields, and other errors in your datasets.
• Data Quality Mode in Astera allows you to perform in-depth data profiling and analysis to identify any potential issues or anomalies in your data. It confirms that the final cleansed and transformed data is complete and reliable.
• By using the Data Cleanse transformation, you can refine raw data and convert it into a standardized, enterprise-ready format. This process involves removing null values, redundant text, and unwanted characters to ensure the data is clean and accurate.

IBM InfoSphere Information Analyzer

IBM InfoSphere Information Analyzer is a robust data profiling and analysis platform that helps evaluate the content and structure of your data. With InfoSphere Information Analyzer, you can perform comprehensive data quality assessments by analyzing the consistency and accuracy of your data. It helps identify data anomalies, such as missing values, duplicates, and inconsistencies, enabling you to improve reliability of your data.

Here are the features provided by IBM InfoSphere Information Analyzer:

• InfoSphere Information Analyzer provides a reusable rules library that supports multilevel data quality evaluations. You can leverage these predefined rules or create custom ones to suit your specific data profiling requirements.
• The platform offers over 80 configurable reports to help visualize data analysis results and trends. This allows you to easily understand and act on the insights generated from the data profiling process.
• It enables you to verify the quality and reliability of external data sources before incorporating them into your data ecosystem. This helps mitigate the risk of incorrect information and downstream data quality issues.

How Data Profiling Works in ETL?

ETL (extract, transform, and load) process involves extracting data from multiple sources, transforming it into an analysis-ready format, and loading it into a destination system. However, many factors influence its effectiveness, including data quality, structure, and consistency.

These factors are essential parts that data profiling manages, positively affecting the ETL processes and further analysis. However, data profiling with ETL can become complex, as many considerations are involved while moving data. Modern ETL tools like Airbyte can help you streamline your ETL process and work with quality data produced by profiling.

Airbyte is a robust data integration platform that enables you to transfer data from multiple sources to a destination. Its highly interactive user interface provides 350+ pre-built connectors for various data sources. If the source of your choice is unavailable on the Airbyte interface, you can create a custom connector using the Connector Development Kit.

Additionally, Airbyte supports the Change Data Capture (CDC) feature, enabling you to effortlessly capture and synchronize the changes made to the source connector with the destination. This feature allows you to monitor and update any change to the destination platform.

Key Takeaways

Data profiling plays an essential role in data analytics workflow by ensuring your data is clean, consistent, and reliable before you use it for decision-making. By performing data profiling, you can address potential issues in your data that may lead to failures or misinterpretation. Data profiling empowers you to proactively address these challenges and ensure your data is ready for analysis.

FAQs

Q. Is Data Profiling an ETL Process?

Data profiling is a preliminary step of the ETL (Extract, Transform, and Load) process, as it allows you to prepare data for further analysis tasks.

Q. What Is the Best Data Profiling Tool/Library to Use?

There are multiple data profiling tools that you can choose from, including Talend Data Fabric, Astera Centerprise, or IBM InfoSphere Information Analyser.

Q. What Is the Difference between Data Analysis and Data Profiling?

Data profiling involves understanding the characteristics and quality of data to ensure its suitability for analysis. On the other hand, data analysis involves extracting insights from data to uncover patterns and trends.