DATA PROFILING: What It Is, Processes, Tools & Best Practices

Why Data Profiling Is Important tools what examples

Without data profiling, or analyzing source data for substance and quality, data processing and analysis are not possible. It is becoming more vital as data sizes grow and cloud computing becomes the norm for IT infrastructure. You need to profile massive amounts of data but are short on time or money. Read on to learn more about data profiling tools and how to make use of them. Some examples are also added to the article for better understanding of it all.

Enjoy the ride!

Basics of Data Profiling

Data profiling is the practice of investigating, evaluating, and synthesizing information. Data quality issues, risks, and general trends can thus be more easily identified thanks to this process’s output of a high-level overview. The results of data profiling also provide invaluable information that businesses can use to their advantage.

Data profiling, to be more precise, is the process of evaluating the reliability and accuracy of information. The analytical algorithms find the mean, lowest, maximum, percentile, and frequency of a dataset to analyze it thoroughly. Metadata such as frequency distributions, key relationships, foreign key candidates, and functional dependencies are then uncovered by the analysis. Finally, it takes all of this data into account to show you how well those elements correspond to your company’s expectations and aims.

Common yet expensive mistakes in consumer databases can also be remedied via data profiling. Null values (unknown or missing values), values that shouldn’t be there, values with an unusually high or low frequency, values that don’t fit expected patterns, and values outside the normal range are all examples of these errors.

Types of Data Profiling

Data profiling can be broken down into three categories:

#1. Structure Discovery

Checking the data for mathematical errors (such as a missing value or an incorrect decimal place) and making sure the data is consistent and presented correctly. Structure discovery aids in determining the quality of data structure, such as the proportion of incorrectly formatted phone numbers. 

#2. Content Discovery

The process of inspecting data record by record in search of mistakes. Data errors, such as missing area codes or invalid email addresses, can be discovered with the use of content discovery.

#3. Relationship Discovery

Learning the interconnections between data elements. Key associations in a database, or cell or table references in a spreadsheet, are two such examples. When importing or merging linked data sources, it is critical to keep the connections between the data sets intact.

Data Profiling Techniques

Data profiling includes not only types but also approaches that can be applied to validating data, keeping track of dependencies, and other similar tasks, regardless of the specific method being utilized. The following are some of the more popular ones:

#1. Profiling Columns

Column profiling is a technique that counts how often a specific value appears in each column by scanning the entire column. You can use this data to spot trends and common values.

#2. Cross-Column Profiling

Key analysis and dependency analysis are the two components of cross-column profiling. The purpose of key analysis is to locate potential primary keys in table columns. Finding patterns or connections in a dataset is the goal of dependency analysis. Collectively, these steps expose intercellular relationships within the same table.

#3. Cross-Table Profiling

To establish connections between fields in several databases, cross-table profiling uses foreign key analysis. As a result, you may see interdependencies more clearly and identify disparate data sets that can be mapped together to speed up research. In addition to revealing redundant information, cross-table profiling can reveal syntactic or semantic discrepancies between linked datasets.

#4. Data Rule Validation

Data rule validation is a process that ensures data values and tables follow predetermined rules for data storage and presentation. The tests give engineers insight into weak points in the data so they can strengthen them.

Data Profiling Examples

There are a variety of situations in which data profiling can help businesses better understand and manage their data. The following are some examples of data profiling:

#1. Consolidation through Purchasing

Your company merges with an industry rival in this example. You can now mine a whole new trove of information for previously unattainable insights and untapped markets. However, you must first combine this information with what you already have.

New data assets and their interdependencies can be better understood with the help of a data profile. The data team can then attempt to standardize the data’s format after data profiling reveals where it differs from the old data and where duplicates exist between the two systems. The time has come to combine and standardize the data for use.

#2. Data Warehousing

When a company builds a data warehouse, its goal is to centralize and standardize data from many sources so that it can be quickly accessed and analyzed. If the data is of low quality, though, simply collecting it in one place won’t help. Poor information results in subpar judgment.

The data quality in a data warehouse can be monitored with the help of data profiling. It can also be used before or during the data intake process to ensure the data’s integrity and compliance with data rules as information is gathered, generally in an ETL process. Now that you have gathered all of your organization’s data in one place, you can make decisions with confidence.

Data Profiling and Data Quality Analysis Best Practices

The following are basic data profiling techniques:

#1. Distinct Count and Percent

Finds the natural keys, which are the unique numbers in each column that can help with inserts and updates. Useful for tables that don’t have headings.

#2. Percent of Zero, Blank or Null Values

Identifies information that is missing or unknown. Default value assistance for ETL architects.

#3. Minimum, Maximum, and Average String Length

Facilitates the choice of a suitable data type and size for the intended database. It also makes it possible to optimize efficiency by setting column widths to the exact lengths required by the data.

Advanced data profiling techniques:

#1. Key Reliability

Use zero-based, blank-value, or null-analysis to guarantee that crucial values are consistently available. Also useful for locating ETL and analysis-related orphan keys.

#2. Cardinality

verification of connections between related datasets, including one-to-one, one-to-many, and many-to-many. This aids BI tools in making the appropriate inner or outer connections.

#3. Pattern and frequency distributions

Validates the correctness of the format of data fields, such as emails, to ensure they may be used. The fields required for outbound communication (email, phone, and physical address) are crucial.

Data Profiling Tools

Data profiling is a time-consuming and manual process, but it may be automated with the right software to facilitate large-scale data projects. You cannot have a data analytics stack without them. The following are some tools you can use for data profiling:

#1. Quadient DataCleaner


  • Information cleansing, data profiling, and information management
  • Find and combine similar items
  • Boolean analysis
  • Examination for Completion
  • Differing Character Sets
  • Comparison of Two Dates
  • Accurately matching references

#2. Aggregate Profiler


  • Data filtering, profiling, and management
  • Tests for Similarity
  • Enhancing data
  • Immediately notifying users of any data errors or updates
  • Validation of a basket analysis using a bubble chart
  • An Individualistic Perspective on the Customer
  • Dummy data creation
  • The Search for Metadata
  • Data anomaly detection and cleaning software
  • Hadoop integration

#3. Data Profiling in Informatica


  • Data management process simulation dashboard for data stewards
  • Business user interface for handling exceptions
  • Data Governance in the Enterprise
  • Create a single map of data quality standards for use across all deployments.
  • Consolidation, de-duplication, enrichment, and standardization of data
  • Administration of metadata

#4. Oracle Enterprise Data Quality


  • Tools for data auditing, profiling, and visualization
  • Standardization and parsing of data, including fields for notes and erroneous entries
  • Combining and matching can be done automatically
  • Human-operated case management
  • Checking the address
  • Data Validation for a Product
  • Compatibility with Oracle’s MDM System

#5. SAS DataFlux


  • Provides data extraction, cleaning, transformation, standardization, aggregation, loading, and management.
  • Facilitates Master Data Management in both batch and real-time settings
  • Services for real-time data integration are created and can be reused.
  • Layer of semantic references that are easy to use
  • Access to information about the source and history of the data being analyzed
  • Enhancement options that are optional

#6. Talend Open Profiler

When it comes to open-source data integration and data profiling tools, many developers turn to Talend Open Studio. It’s great for batch or real-time data integration and ETL processes.

This software has several useful functions, including data management and purification, text field analysis, fast data integration from any source, and more. The ability to improve matching with time-series data is one of the distinctive selling points of this product. Moreover, the Open Profiler has a user-friendly interface that displays the profiling results for each data element in the form of graphs and tables.

#7. Quadient DataCleaner

If you’re looking for an Open-Source, plug-and-play Data Profiling Tool, look no further than Quadient DataCleaner. One of the most common Data Profiling Tools is Quadient DataCleaner, which is used for data gap detection, completeness analysis, and data wrangling.

Users of Quadient DataCleaner have the option of performing Data Enrichment in addition to their routine cleansing procedures. In addition to ensuring quality, the technology also provides clear visualizations of findings in the form of reports and dashboards. 

The public version of this program is freely available to anyone. However, the cost of premium versions with additional features is given upon request and is based on the specifics of your use case and company needs.

#8. Open Source Data Quality and Profiling

Every data issue can be addressed with Open Source Data Quality and Profiling. Data Profiling, Data Preparation, Metadata Discovery, Anomaly Discovery, and other data management tasks may all be accomplished with this tool’s high-performance, comprehensive data management platform.

Data Governance, Data Enrichment modification, Real-time Alerting, etc. are only some of the features that have been added to the original Data Quality and Preparation application. This program now also works with Hadoop, allowing for the transfer of files within the Hadoop Grid, facilitating the efficient processing of large datasets.

#9. OpenRefine

OpenRefine is an Open Source program for cleaning up dirty data that was formerly known as Google Refine and Freebase Gridworks. It is a community-driven Data Profiling tool that first saw the light of day in 2010, and since then its developers have worked hard to stay up with evolving user needs.

It is also a Java-based utility that helps users load, clean, reconcile, and interpret data. The tool was also translated into more than 15 languages. Also, Data Profiling is made more accurate by adding web data to the mix. Users can also use General Refine Expression Language (GREL), Python, or Clojure to perform complex data transformations.

#10. DataMatch Enterprise

Code-free profiling, cleaning, matching, and deduplication are all made possible with DataMatch Enterprise. For fixing problems with the quality of customer and contact data, it delivers a highly visual data cleansing solution. The system also employs a number of in-house and industry-standard algorithms to detect typographical, phonetic, fuzzy, mistyped, shortened, and domain-specific variants.

While DataMatch Enterprise (DME) is available at no cost, more advanced versions, such as DataMatch Enterprise Server (DMES) have prices that are only made known upon reserving a demonstration.

#11. Ataccama

If you’re looking to create a data-driven, agile business, go no further than Ataccama, an enterprise Data Quality Fabric solution. Ataccama is one of the free and open-source data profiling tools available, with capabilities like advanced profiling metrics like foreign key analysis, the ability to profile data in the browser, the capacity to transform any data, etc.

The software also makes use of AI to spot outliers during data loads, alerting users to potential problems. With tools like Ataccama DQ Analyzer and others built within the platform, data profiling is a breeze. The community is also planning to release other modules for Data Profiling, such as Data Prep and Freemium Data Catalog.

Recognizing the Importance of Data Profiling Tools

Data profiling tools have several advantages, some of which are listed below:

  • Data Profiling Tools allow users to enhance data quality.
  • Using Data Profiling Tools, companies may pinpoint the causes of quality problems.
  • In order to effectively consolidate data, data profiling tools can identify patterns and data correlations.
  • Profiling tools for data offer an accurate image of the data’s organization, content, and rules.
  • Users can gain a deeper insight into the collected data with the help of data profiling tools.
Read Also: DATASTAGE: What It Is, How It Work & Uses

Why Is Data Profiling Important?

Companies can lose as much as 30 percent of their income due to inaccurate data. Millions of dollars lost, plans that need to be redone, and damaged reputations are the results for many businesses. Where do issues with data quality originate, then?

In many cases, carelessness is to blame. Companies can get so busy getting data and running their operations that the usefulness and quality of the data they collect suffer. This could lead to decreased output, lost revenue, and a weakened bottom line. A data profiling tool is useful for this purpose.

Once a data profiling program is activated, it will continuously analyze, clean, and update data in order to deliver vital insights that can be accessed from your desktop computer. Data profiling, in particular, offers:

#1. Better Data Quality and Credibility

The software can aid in the removal of duplicates or outliers once the data has been evaluated. It can be used to ascertain pertinent data that can influence business decisions, pinpoint quality issues within an organization’s infrastructure, and extrapolate the state of the business going forward.

#2. Predictive Decision Making

Profile data can be utilized to catch errors before they snowball. As an added bonus, it can show what might happen in hypothetical situations. Data profiling aids in painting a clear picture of a company’s health to guide decision-making.

#3. Proactive Crisis Management

Data profiling allows for the early detection and resolution of issues.

#4. Organized Sorting

For the most part, databases engage with a wide variety of data sources, such as blogs, social media, and big data marketplaces. If sufficient encryption is in place, profiling can be used to track down the data’s origin. A data profiler can examine all of the sources and destinations of your data to check for statistical consistency and compliance with your company’s regulations.

A company’s future strategy and long-term goals can be better mapped out with a firm grasp of the connection between the data it has, the data it needs, and the data it doesn’t have. A data profiling application’s availability can facilitate these procedures.

Data Profiling in a Cloud-Based Data Pipeline: The Need for Speed

The importance of efficient data profiling has only increased as more businesses have begun storing massive volumes of data in the cloud. Companies can already store petabytes of data in cloud-based data lakes, and the Internet of Things is increasing our data storage capacity by gathering massive amounts of data from a wide variety of sources, such as our homes, clothing, and technology.

To maintain a competitive edge in today’s market, which is being driven more and more by cloud-native big data capabilities, businesses must have the capability to effectively utilize this wealth of information. Data profiling determines the success or failure of data management initiatives, from meeting regulatory requirements to building a reputation for exceptional customer service. This post describes traditional data profiling, a sophisticated operation carried out by data engineers before and during data ingestion into a data warehouse. Before entering the pipeline, data undergoes careful examination and processing (with some help from automation).

Today, more and more businesses are realizing that data intake is as simple as clicking a button, thanks to the widespread adoption of cloud computing. Cloud-based data warehouses, data management tools, and ETL services are already wired to a wide variety of information resources. What about data profiling, though, if information can be transferred to the desired system with the click of a button?

Large amounts of data moving through the big data pipeline and the ubiquity of unstructured data have made data profiling more important than ever. An automated data warehouse that can handle data profiling and preparation on its own is essential in a cloud-based data pipeline design. Just dump the raw data into the automated data warehouse, and it will be cleaned, optimized, and ready for analysis without any human intervention or data profiling tool use.

How Is Data Profiling Done?

Here is how it is done:

  • Collecting descriptive statistics like min, max, count, and sum.
  • Collecting data types, lengths, and recurring patterns.
  • Tagging data with keywords, descriptions or categories.
  • Performing a data quality assessment, risk of performing joins on the data.
  • Discovering metadata and assessing its accuracy.

What Is the Goal of Data Profiling?

Examining, evaluating, and synthesizing data into useful summaries is what data profiling is all about. Data quality issues, risks, and general trends can thus be more easily identified thanks to this process’s output of a high-level overview. Companies can greatly benefit from the insights gained through data profiling.

What Is the Difference between Data Analysis and Data Profiling?

The analysis’s goals are different for each. By analyzing data, you can gain insight into the underlying operations and, thereby, your business. In data profiling, the data is examined so that conclusions can be drawn about its suitability, quality, extent, etc.

Final Thoughts

It’s not necessary to perform data profiling manually. In reality, automating the profiling process using a data management solution is the most effective approach to managing the process. Eliminating inconsistencies and bringing uniformity to the data profiling process are also two ways in which data profiling technologies boost data integrity.  We do hope this article was helpful?


Leave a Reply

Your email address will not be published. Required fields are marked *

You May Also Like