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

Summary: Data profiling involves examining and analysing datasets to understand their structure, quality, and content. This process identifies anomalies, inconsistencies, and patterns within the data, enabling organisations to improve data quality and integrity. By leveraging data profiling techniques, businesses can enhance decision-making, optimise data management, and ensure compliance with regulations.

Introduction

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

Data profiling is the process of analysing a dataset’s content, structure, and quality to identify anomalies and enhance data integrity. Here are some real-world examples of data profiling in action:

Retail Store: Inventory Management

A retail store profiles its inventory data to identify inconsistencies, such as missing stock levels or incorrect product descriptions. By analysing data patterns and quality, the store can optimise inventory management, reduce stockouts, and improve customer satisfaction.

Healthcare: Patient Record Accuracy

In the healthcare sector, it ensures the accuracy and completeness of patient records. By identifying missing data, formatting issues, or duplicate entries, hospitals can maintain high-quality patient information for better care delivery and regulatory compliance.

Banking: Fraud Detection

Banks use it to detect fraudulent activities by analysing customer transaction patterns. By profiling data from various sources, such as credit card usage and account activity, banks can identify suspicious behaviour and prevent financial losses.

Real Estate: Property Valuation

A real estate company profiles property data, including location, size, and amenities, to provide accurate valuations. It helps identify relevant factors that influence property prices, enabling the company to make informed decisions and provide reliable estimates to clients.

Agriculture: Crop Yield Optimization

Agricultural businesses profile data from soil quality measurements, weather data, and historical crop yields to optimise crop production. By analysing correlations between these factors, farmers can adjust planting schedules, irrigation practices, and fertiliser application for higher yields and reduced resource consumption.

It is a crucial process that helps organisations understand their data, identify quality issues, and make informed decisions. By leveraging data profiling techniques and tools, businesses can improve data-driven decision-making, enhance operational efficiency, and gain a competitive advantage in their respective industries.

Types of Data Profiling

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

Structural Profiling

Structural profiling involves analysing 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 helps in gaining insights into how the data is organised and formatted. Key aspects of structural profiling include:

Data Types: Identifying the types of data stored in each column (e.g., integer, string, date, boolean).

Length and Format: Analysing the length and format constraints of string or character columns.

Pattern Analysis: Identifying consistent patterns or regular expressions within text data.

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 helps in assessing the reliability and accuracy of the data. Key aspects of content profiling include:

Value Distribution: Analysing the frequency and distribution of values within columns.

Uniqueness: Determining the uniqueness of values in columns to identify potential duplicates.

Missing Data: Detecting missing values and assessing the extent of missingness.

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:

Key Identification: Identifying primary keys and unique identifiers in tables.

Foreign Key Relationships: Discovering relationships between tables through foreign keys.

Referential Integrity: Ensuring that data relationships adhere to referential integrity constraints.

Cross-Column Analysis: Analysing correlations and associations between different columns.

These three main types 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

It offers numerous benefits, including improved data quality, enhanced decision-making, and increased operational efficiency. Some of the benefits are mentioned below:

Improved Data Quality and Credibility

It 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. 

Predictive Decision-Making

It helps businesses in preventing mistakes in becoming serious problems. Consequently, businesses can effectively understand the potential outcomes based on different scenarios. 

Organised sorting

Most of the databases tend to interact with a diverse set of data from multiple sources like social media, surveys, etc. Leveraging data profiling, it is possible to trace the source of data and enable encryption for security.

Eventually, you can analyse different databases and ensure that data meets the statistical standards and business rules. 

Best Data Profiling Tools

Data profiling tools are essential for analysing and assessing data quality, structure, and content. They help organisations identify anomalies and improve decision-making by ensuring data accuracy. Explore the best data profiling tools available to enhance your data management processes and drive better insights.

IBM InfoSphere Information Analyzer

IBM InfoSphere Information Analyzer is a comprehensive 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 organisations profile, cleanse, and standardise 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 organisations to gain a deep understanding of their data by analysing 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 organisations 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.

Conclusion

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 businesses 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 databases to a centralised location. Thus, by building an in-house data integration solution, businesses will be able to conduct complex tasks efficiently. 

Frequently Asked Questions

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. 

How to do data profiling in Excel?

Data Profiling in Excel involves analysing and summarising 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.