Data Profiling

What exactly is Data Profiling: It’s Examples & Types

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The presence of large volumes of data within organisations requires effective sorting and analysing ensuring that decision-making is highly credible. Almost all organisations nowadays make informed decisions by leveraging data and analysing the market effectively. This helps them drive business growth and earn higher profitability. 

However, analysis of data may involve partiality or incorrect insights in case the data quality is not adequate. Accordingly, the need for Data Profiling in ETL becomes important for ensuring higher data quality as per business requirements. 

The following blog will provide you with complete information and in-depth understanding on what is data profiling and its benefits and the various tools used in the method. 

What is Data Profiling in ETL?

Data Profiling refers to the process of analysing and examining data for creating valuable summaries of it. The process of data profiling helps in understanding the structure, content and interrelationships of data. The method makes use of business rules and analytical algorithms to minutely analyse data for discrepancies. 

Data Analysts tend to make use of these data to interpret the information and understand the factors that align with business growth and objectives. The importance of Data Profiling lies in the fact that it helps businesses determine the accuracy of data and its validity along with risks and overall trends. 

Data Profiling Example:

Here are some examples of data profiling:

  • Column Data Types and Value Distribution:
    1. Identify the data types of each column (e.g., integer, string, date).
    2. Examine the distribution of values within numeric columns (e.g., mean, median, standard deviation).
    3. Determine the range of values for categorical columns.

  • Missing Values Analysis:
    1. Identify columns with missing values and calculate the percentage of missing values in each column.
    2. Analyze patterns of missing data to understand if they are random or systematic.

  • Uniqueness and Cardinality:
    1. Count the number of unique values in a column to understand its cardinality.
    2. Detect columns with high cardinality that might represent unique identifiers.

  • Pattern Analysis:
    1. Identify common patterns in text or string columns (e.g., email addresses, phone numbers, addresses).
    2. Look for consistent formats in date or timestamp columns.

  • Outliers and Anomalies:
    1. Detect outliers in numerical columns using statistical methods (e.g., Z-score, interquartile range).
    2. Identify anomalies or unexpected values that could indicate data quality issues.

  • Cross-Column Analysis:
    1. Explore relationships between columns to uncover potential data dependencies or correlations.
    2. Identify potential foreign key relationships between tables in a relational database.

  • Data Distribution Analysis:
    1. Create histograms, box plots, or scatter plots to visualize data distributions and relationships.
    2. Analyze skewness and kurtosis to understand the shape of the data distribution.

  • Data Profiling Reports:
    1. Generate summary reports that provide an overview of key profiling metrics for each column.
    2. Present visualizations and statistics to help stakeholders understand the data characteristics.

  • Data Quality Assessment:
    1. Assess data quality issues, such as duplicate records or inconsistent data formatting.
    2. Evaluate the accuracy and completeness of the data.

  • Sampling and Size Estimation:
    1. Use data profiling to estimate the size of a dataset, especially when dealing with large volumes of data.
    2. Create representative samples for analysis and testing purposes.

Types of Data Profiling:

Data profiling can be broadly categorized into three main types, each focusing on different aspects of the data:

  • Structural Profiling: 

Structural profiling involves analyzing the structure and metadata of the data. It focuses on understanding the basic characteristics of the dataset, such as data types, lengths, formats, and patterns. This type of profiling helps in gaining insights into how the data is organized and formatted. Key aspects of structural profiling include:

  1. Data Types: Identifying the types of data stored in each column (e.g., integer, string, date, boolean).
  2. Length and Format: Analyzing the length and format constraints of string or character columns.
  3. Pattern Analysis: Identifying consistent patterns or regular expressions within text data.
  4. Domain Validation: Verifying that data values conform to predefined domain rules.

  • Content Profiling: 

Content profiling focuses on the actual values within the dataset. It aims to understand the distribution, uniqueness, and quality of data values. This type of profiling helps in assessing the reliability and accuracy of the data. Key aspects of content profiling include:

  1. Value Distribution: Analyzing the frequency and distribution of values within columns.
  2. Uniqueness: Determining the uniqueness of values in columns to identify potential duplicates.
  3. Missing Data: Detecting missing values and assessing the extent of missingness.
  4. Data Quality: Identifying data anomalies, outliers, and inconsistencies that may indicate data quality issues.

  • Relationship Profiling:

Relationship profiling focuses on understanding the relationships and connections between data elements within and across datasets. It helps in identifying dependencies and associations between columns or tables. Key aspects of relationship profiling include:

  1. Key Identification: Identifying primary keys and unique identifiers in tables.
  2. Foreign Key Relationships: Discovering relationships between tables through foreign keys.
  3. Referential Integrity: Ensuring that data relationships adhere to referential integrity constraints.
  4. Cross-Column Analysis: Analyzing correlations and associations between different columns.

These three main types of data profiling collectively provide a comprehensive understanding of the data’s structure, content, and relationships. They assist data professionals in assessing data quality, making informed decisions during data integration and transformation, and ultimately preparing the data for effective analysis and reporting.

Benefits of Data Profiling:

There are several benefits of Data Profiling in ETL which you should be aware of: 

  1. Improved Data Quality and Credibility: Data Profiling can be effectively used to ensure that data is being leveraged which is of best quality. As the quality of data is improved and becomes more credible, it helps businesses in making better choices. 
  2. Proactive crisis management: Data profiling helps businesses in identifying and addressing problems even before they arise. 
  3. Predictive Decision-Making: Data Profiling helps busin esses in preventing mistakes in becoming serious problems. Consequently, businesses can effectively understand the potential outcomes based on different scenarios. 
  4. Organised sorting: most of the databases tend to interact with diverse set of data from multiple sources lie social media, surveys, etc. Leveraging data profiling, it is possible to trace the source of data and enable encryption for security.
    Eventually, using a data profiler, you can analyse different databases and ensure that data meets the statistical standards and business rules. 

5 Best Data Profiling Tools:

Following are some of the Data profiling tool in ETL that you need to know: 

  • IBM InfoSphere Information Analyzer: 

IBM InfoSphere Information Analyzer is a comprehensive data profiling and data quality tool that provides robust capabilities for understanding data structures, relationships, and quality issues. It supports metadata analysis, data lineage, and data quality assessment. It is part of the IBM InfoSphere suite of data integration and governance tools.

  • Informatica Data Quality: 

Informatica Data Quality is a powerful data profiling and data quality tool that helps organizations profile, cleanse, and standardize data. It offers features for identifying data anomalies, creating data quality rules, and integrating data quality processes into data workflows. 

It can be used as a standalone tool or as part of the Informatica suite of data integration and management solutions.

  • Open Studio for Data Quality (Talend): 

Talend’s Open Studio for Data Quality is an open-source data profiling and data quality tool that offers a range of data cleansing, enrichment, and validation features. It provides a user-friendly interface for designing data quality rules and workflows. Talend also offers commercial versions of its data integration and quality products.

  • Oracle Enterprise Data Quality: 

Oracle Enterprise Data Quality (EDQ) serves as a robust and comprehensive data profiling tool within Oracle’s suite of data management and integration solutions. As a data profiling tool, Oracle EDQ empowers organizations to gain a deep understanding of their data by analyzing its structure, content, and quality. 

This process of data profiling is pivotal for ensuring accurate and reliable data, which is essential for making informed business decisions and maintaining operational efficiency.

  • SAP Business Objects Data Services (BODS)

SAP BusinessObjects Data Services (BODS) is a powerful and versatile data integration and ETL (Extract, Transform, Load) tool offered by SAP, a leading enterprise software company. BODS is designed to help organizations extract, transform, and load data from various sources into a unified and structured format. 

Accordingly, it makes it suitable for analysis, reporting, and decision-making. It offers a wide range of features and capabilities to manage data across the entire data lifecycle.


In conclusion, the blog provided you with detailed understanding on data profiling and how it is useful in maintaining higher data quality using various tools for automating the process. As business use multiple platforms for conducting business operations, data is spread across these platforms. 

With the help of data profiling, it is possible for businesses to sort the data and integrate the data from all database to a centralised location. Thus, by building an in-house data integration solution, businesses will be able to conduct complex tasks efficiently. 


What is the difference between data profiling and ETL?

Data Profiling in ETL is a process of analysing data for creating valuable summaries and finding interrelationships within datasets. ETL or Extract, Transform and Load is the process of combining multiple sources of data into a large and central repository called data warehouse

Is data profiling the same as data cleaning?

No, Data profiling and data cleaning are not the same. Data profiling helps in analysing the problems within the data while data cleaning allows you to correct the errors in a dataset. 

What is the difference between data profiling and data wrangling?

While data profiling is the method of summarising the data collected and analysed to find patterns and trends, data wrangling is the process of removing errors from complex datasets. Data wrangling helps in making data more accessible and easier to analyse. 

How to do data profiling in Excel?

Data Profiling in Excel involves analyzing and summarizing dataset characteristics, such as data types, patterns, and missing values. It helps uncover insights, identify anomalies, and ensure data quality, aiding informed decision-making. Using built-in functions, pivot tables, and charts, Excel allows users to gain a comprehensive understanding of their data’s attributes and structure.