What Is Data Profiling? (Importance and Types)

By Indeed Editorial Team

Published May 31, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Companies can use data profiling to make their data operations more efficient, accurate, and effective. Different organizations can also keep a procedure in place to discover and rectify difficulties, making it easier to handle and transmit data. Understanding these processes can help you keep a company's data accurate and organized so you can develop better methods for managing it. In this article, we define data profiling, explore its importance, discuss reasons and techniques for using it, review its types, discuss its benefits, define data analysis, and list some essential skills for data analysts.

What is data profiling?

Data profiling is the process of examining and documenting statistics from data to assure its accuracy. This provides organizations with the information they require to enter and maintain data in a data warehouse (DW), which collects data from a variety of sources like websites, social media, and email. To replicate and transfer data to a new system, DWs use a process known as extract, transform, load (ETL). Professionals can use this profiling to detect data flaws in the ETL process, which they can then correct or improve.

Why is profiling data important?

Profiling of data is vital because it ensures the accuracy, completeness, and quality of data. Websites, blogs, social media, and other platforms are all common data sources for businesses. This profiling verifies the information for transfer so that businesses can access, use, and modify it as needed. It also enables businesses to ensure data quality prior to transferring data from an old system to a new one.

Related: Data Entry Skills and How to Improve Them

Reasons that companies profile data

Here are some reasons companies might profile data:

  • Organizing and understanding data

  • Ensuring data meets statistical and organizational standards

  • Discovering issues with data quality

  • Identifying specific data that needs correcting

  • Determining the sources of data quality issues

Missing values, duplication, and aberrant patterns are some of the faults commonly found in data by businesses. Once they've identified the problems, they can use tools like data cleansing software to fix them and prepare the data for storage or transfer.

Techniques for effectively profiling data

Here are four common techniques for effective profiling:

Column profiling

A program reviews tables and counts the number of times each value appears in each column during column profiling. Businesses use this method to find the frequency distribution and patterns of data properties, such as:

  • Range analysis

  • Format evaluation

  • Pattern distributions

  • Cardinality

  • Uniqueness analysis

  • Sparseness

  • Value absence

  • Abstract type recognition

  • Attribute overloading analysis

You can implement column profiling by using hash tables, which are data structures that map keys to values, meaning they link them together. Hash tables allow you to organize columns of data visually so you can access the data easily.

Related: How to Learn Data Science (A Complete Guide)

Cross-column profiling

You acquire information about how values and fields in a table relate to each other through cross-column profiling. There are two basic processes involved in this, key analysis and dependency analysis. In key analysis, you look for the main key or the column label that identifies the rest of the data in information fields. Comparatively, you look for links between fields in a data collection using dependency analysis.

Cross-table profiling

Cross-table profiling analyzes the relationship between certain variables. Its major goal is to look for foreign keys, which are connections between sets of attributes in one table and the primary key in another. This method is useful to businesses for finding similarities and differences in data. This helps identify data redundancy and determines which data values can transfer to other systems.

Data rule validation

Data rules determine the kinds of information a user can enter into a cell. Data rule validation enforces these restrictions by verifying that sets of data follow certain rules. For example, a data professional may decide that consumers can only enter values between 6 and 12 in a product pricing column. If a user types a number that is outside of the range, the program warns them that they may not input that value in that cell.

Related: How to Learn Data Entry and Available Career Options

Types of profiling of data

Here's a list of the main types of profiling for you to consider:

Structure discovery

Structure discovery, also known as structure analysis, ensures that data is consistent and formatted. It also looks at basic data statistics, including means, medians, modes, and standard deviations. One of the most prevalent structure discovery approaches is pattern matching. It enables data analysts to check sets of data for valid formats.

Content discovery

Data professionals can use content discovery to identify flaws in individual data records. It indicates specific rows inside a table that require attention, and also systemic flaws with the data. Content discovery also reveals areas that contain null or incorrect values.

Relationship discovery

Finding active data and determining relationships between data sets are both important aspects of relationship discovery. The procedure starts with a broad data analysis and progresses to the discovery of linkages between overlapping data sets. Relationship discovery makes it possible to reuse data and minimize problems within the data warehouse.

Benefits of profiling of data

While profiling has several advantages for businesses, it's particularly beneficial for large companies with a vast amount of data from different sources. These advantages include:

  • Improves the quality of the data: This method may expose data integrity concerns, allowing you to rectify them prior to storage or transfer. Following the first profiling procedure, data management may become easier and more successful.

  • Prevents and manages crises: This process provides insight into potential data issues, which can help you resolve them before they create problems in the system.

  • Shortens the implementation phase of projects: This process can shorten the time required to implement databases because you can confirm the quality of your data prior to testing it, installing it, and training personnel on how to use it.

  • Enables master data management: This process has an essential role in master data management because it allows business and information technology teams to work together to ensure the consistency, accuracy, and accountability of a company's data.

  • Improves decision-making: This process can show you the potential outcomes of new scenarios, which can guide you in making decisions.

  • Stays organized: This process can help you understand the relationships between each data value, and it can store and access data in an organized manner.

What is data analytics?

You can refer to the practice of analyzing raw data to make conclusions as data analytics. These conclusions can help you understand how a company works and what it can do. Data analysis entails obtaining, cleaning, and analyzing information to find common and consistent patterns. The following are some examples of data analytics:


Descriptive data analytics are useful for explaining the current state of a company's data. It analyzes the company's existing financial and performance data and determines if itis producing revenue or whether there are opportunities to enhance productivity and profitability. Descriptive data analytics can only tell you whether you might make adjustments.


Diagnostic data analytics examine the data in further detail to see where a business may improve. It uses descriptive-analytical data to determine the reason for revenue losses and gains. With diagnostic data, a business may determine where it can reduce expenses or reallocate resources from low-profit sectors to places that create more revenue.


Predictive data analytics uses both descriptive and diagnostic data, and historical data from the business, to identify patterns and correlations that enable them to forecast future trends. Predictive data may be useful for determining the result of an organization's existing business practices and also the possible effects of changing key factors. You may utilize the diagnostic data to identify areas for improvement and provide different forecasts to assist you in making decisions.

Essential skills for data analysts

Here are some important skills that may be useful to data analysts:

  • SQL: SQL is a spreadsheet and computer language that's capable of managing large amounts of data and processing information significantly more rapidly than other spreadsheet applications. SQL is a powerful tool for data analysts, so familiarity with its features is critical.

  • Spreadsheets: While SQL is often the chosen program for data analysts, familiarity with and comprehension of widely used spreadsheet applications is also essential. Certain businesses may want to display reports or data sets using conventional spreadsheet methods.

  • Critical thinking: Businesses often assign data analysts the responsibility of both data collection and data interpretation for a particular goal. Knowing what data to gather and how to analyze it to get the necessary information requires a level of critical thinking that data analysts can learn.

Please note that none of the companies, institutions, or organizations mentioned in this article are affiliated with Indeed.

Explore more articles