What is Data Profiling: Types, Benefits, Methods, Tools & Challenges

Imagine diving headfirst into a data pool without knowing its depth, clarity, or potential hazards. That’s often the reality when working with raw data. Data profiling is your lifeguard, meticulously analyzing your data to reveal hidden patterns, identify inconsistencies, and ensure data quality.

This blog will guide you through the fascinating world of data profiling, exploring its importance, techniques, and how it can empower your data-driven decisions.

What is Data Profiling in ETL?

Data profiling in ETL analyzes data quality and structure before it undergoes extraction, transformation, and loading. This practice identifies missing values, duplicates, and inconsistencies, ensuring that only high-quality data is processed. Businesses can improve data accuracy, streamline ETL workflows, and enhance decision-making. It aids in:

• discovering data quality issues,
• risks, and
• overall trends.

More specifically, it sifts through data to determine its quality and legitimacy. It is used for various reasons, but it is most commonly used to assess the quality of data, which is a larger project component. Usually, it is combined with an ETL process. It can be leveraged to cleanse, enrich, and load quality data into a target location if performed correctly.

It can eliminate costly errors that are common in databases. These errors include incorrect or missing values, values outside the range, unexpected patterns in data, etc. It involves the following processes:

• Collect descriptive statistics such as minimum and maximum values, count of values, etc., along with any other attributes that can describe the basic features of the data while going through the profiling process.
• Performing data quality assessment.
• Identifying data types, recurring patterns, etc.
• Tagging data with descriptions and keywords.
• Group data into categories.
• Identifying the metadata and its accuracy.
• Performing inter-table analysis.
• Identifying functional dependencies, embedded value dependencies, distributions, key candidates, foreign-key candidates, etc.

Example

For one report or analysis, data warehousing or business intelligence projects may necessitate gathering data from numerous distinct systems or databases. Before moving on with these projects, data profiling can assist detect potential flaws and corrections in extract, transform, and load (ETL) activities and other data integration procedures.

Dividend Growth Investment has a long history and has always been popular for generating passive income. As with any other investment, deciding which stocks to buy requires deliberation. The larger the risk, the higher the potential gain, just like any other investment. This dataset should aid in choosing a portfolio based on data.

What are the Types of Data Profiling?

• Structure Discovery: This type of profiling involves performing mathematical checks on the data, such as sum, minimum, maximum, etc., along with other descriptive statistics. Structure discovery aims to understand how well the data is structured and ensure data consistency. For example, consider a database with the contact numbers of all users. A Structure Discovery process would involve finding the percentage of phone numbers that do not have the correct number of digits. 
• Content Discovery: Content discovery profiling involves looking into individual data records to identify errors. Content discovery identifies which rows in a given dataset contain problems or any systemic issues occurring in the data. For example, consider a database with the contact numbers of all users. A content discovery process would involve determining the percentage of phone numbers with no area code.
• Relationship Discovery: Relationship discovery involves identifying how parts of the data are related to each other. For example, identifying key relationships between tables in a database, references between cells and tables in a spreadsheet, etc.

What are the Benefits of Data Profiling?

• Improved Data Quality and Credibility: It can be used to ensure that the data being leveraged is of the best possible quality. Good quality and credible data can be leveraged to determine helpful information that could affect business choices, identify problems within an organization’s system, and draw certain conclusions about the company’s future health.
• Proactive Crisis Management: It can help businesses efficiently identify and address problems before they arise.
• Predictive Decision-making: Profiled information can be used to prevent small mistakes from becoming significant problems. It can also help businesses understand possible outcomes for different scenarios. It helps create an accurate snapshot of the health of a business to improve the decision-making process.
• Organized Sorting: Most databases interact with a diverse set of data from various sources, such as social media, surveys, and other big data markets. It can be leveraged to trace data to its source and ensure proper encryption for data security. A data profiler can then be used to analyze the different databases and data sources and ensure that the data meets the standard statistical measures and business rules.

What are the Data Profiling Methods?

• Column Profiling: In this method, the number of times every value appears within each table column is counted. This method helps to uncover patterns within the data.
• Cross-column Profiling: In this method, users look across columns to perform key and dependency analysis. Key analysis is implemented to scan the collections of values in a table to identify a potential primary key. Dependency analysis determines the dependent relationships within data sets. These analyses can be leveraged to determine the relationships and dependencies across tables.
• Cross-table Profiling: In this method, users look across tables to identify all potential foreign keys. It also attempts to identify similarities and differences among data types and syntax between tables to determine which data can be mapped together and which might be redundant.

What are the 5 Best Tools To Use For Profiling Your Data?

1) IBM InfoSphere Information Analyzer

IBM InfoSphere Information Analyzer is a component of the IBM InfoSphere Information Server. It is used to evaluate the data quality and structure within and across heterogeneous systems. It leverages a reusable rule library that houses over 200 built-in data quality rules to control the ingestion of bad-quality data and supports multi-level evaluations by pattern and rule record. It can carry out numerous profiling functions such as primary key analysis, column analysis, cross-domain analysis, natural key analysis, etc. It is considered suitable for analyzing diverse data formats for big data, business intelligence, data warehousing, management, etc.. It also houses numerous machine-learning capabilities that can efficiently auto-tag data and identify potential issues.

IBM InfoSphere Information Analyzer Pricing

IBM does not follow a transparent pricing model for its InfoSphere Information Server. The final price depends on your business and data requirements and can be finalized by contacting the Sales team at IBM.

2) SAP Business Objects Data Services (BODS)

SAP Business Objects Data Services (BODS) is one of the most popular tools available. It can help businesses seamlessly carry out an in-depth analysis to identify inconsistencies in their data and other data problems. In one package, it houses features such as Data Quality Monitoring, Metadata Management, Data Profiler, etc.. It can easily perform redundancy checks, pattern distribution, analyze cross-system data dependencies, etc. The standard profiling using SAP Business Objects Data Services (BODS) allows businesses to understand the unique values present in the columns, whereas Relationship Profiling carries out detailed profiling.

SAP Business Objects Data Services (BODS) Pricing

SAP does not follow a transparent pricing model for Business Objects Data Services (BODS). The final price depends on your requirements and can be finalized by contacting the Sales team at SAP.

3) Informatica Data Explorer

Informatica Data Explorer houses numerous data quality and profiling solutions that give businesses the ability to carry out faster and more thorough analyses of their data. This tool can efficiently scan every single data record from all sources to identify anomalies and hidden relationships. It can work seamlessly on highly complex datasets and also find connections between multiple data sources. This tool houses various pre-built rules that can be applied to Structured or Unstructured data for profiling.

Informatica Data Explorer Pricing

Informatica offers a 30-day free trial for its products, following which the users have to start paying. However, Informatica does not follow a transparent pricing model for its product, and the final price depends on your unique business and data requirements.

4) Melissa Data Profiler

Melissa allows businesses to perform numerous functions such as data matching, profiling, enrichment, verification, etc. It is considered relatively easy to use and can analyze data of different forms efficiently while carrying out General Formatting, Content Analysis, etc. Its profiling capabilities help check data before it is loaded into the Data Warehouse and help ensure data consistency and quality. In addition, it can carry out tasks such as identifying and extracting data, monitoring data quality processes, enhancing data governance, creating a metadata repository, ensuring data standardization, etc.

Pricing

Melissa does not follow a transparent pricing model for its profiling tool. The final price depends on your requirements and can be finalized by contacting the Sales team at Melissa.

5) SAS DataFlux Data Management Server

SAS DataFlux Data Management Server combines data integration, data quality, and master data management. It houses high-performance environments that allow users to create and explore data profiles, design data standardization schemes, etc. Businesses can leverage it to extract, profile, standardize, monitor, and verify the data efficiently and securely. SAS DataFlux Data Management Server ensures that businesses have high-quality data flow across every process.

SAS DataFlux Data Management Server Pricing

SAS does not follow a transparent pricing model for DataFlux Data Management Server. The final price depends on your business and data requirements and can be finalized by contacting the Sales team at SAS.

Other Data Profiling Tools:

• Commercial tools: Informatica Data Quality, IBM InfoSphere, Ataccama
• Open-source tools: Talend Open Studio, OpenRefine

Apart from these, there are the best open-source data profiling tools available in the market.

What are the Related Challenges?

• Complicated Process: While the goals of data profiling are simple, the work is quite complicated, with various processes occurring from data acquisition to data warehousing.
• Creating a Profiling Program: One of the obstacles that businesses have when attempting to develop and operate a successful data profiling program is complexity.
• Large Data Volume: Another problem is the sheer volume of data generated by a typical firm and the variety of data sources, ranging from cloud-based systems to endpoint devices deployed as part of an internet-of-things ecosystem.
• Lack of Proper Tools: These data preparation issues are magnified in organizations that have not yet adopted current profiling tools and still rely on manual processes for most of their data preparation.
• Lack of Trained Employees: Similarly, organizations that lack necessary resources, such as Trained Data Experts, Tools, and Financing, will find it more challenging to overcome these obstacles.
• Data Privacy Concerns: Ensuring compliance with data protection regulations during profiling is critical.
• Complex Data Structures: Profiling unstructured or semi-structured data poses unique challenges.

These same factors, however, make it more critical than ever to guarantee that the company has the high-quality data it requires to Power Intelligent Systems, Customer Personalization, Productivity-Boosting Automation Projects, and other initiatives.

What is Data Profiling In A Cloud-Based Data Pipeline

Data Profiling is a time-consuming process carried out by data engineers before and during the admission of data into a data warehouse. Before entering the pipeline, data is thoroughly evaluated and processed (with some automation).

More companies are migrating their data infrastructure to the cloud, realizing that data ingestion can be done with a button. Hundreds of data sources are pre-integrated into cloud data warehouses, data management tools, and ETL services. You can now use an automated Data Pipeline like Hevo. Hevo helps you directly transfer data from a source of your choice to a data warehouse, or desired destination in a fully automated and secure manner without having to write the code.

With massive volumes of data traveling through the big data pipeline and the ubiquity of unstructured data, it is more critical than ever. You’ll need an automated data warehouse in a cloud-based data pipeline architecture. Instead of employing a profiling tool to analyze and treat your data, pour it into the automated data warehouse, where it will be cleaned, optimized, and prepared for analysis.

Conclusion

This article provided you with an in-depth understanding of what Data Profiling is, along with the methods for its implementation and various tools that can be used to automate the process.

Most businesses today use multiple platforms to carry out their day-to-day operations. As a result, all their data is spread across the databases of these platforms. If a business wishes to perform a common analysis of its data, it would first have to integrate the data from all these databases and store it in a centralized location. After performing the process, migrate your data to any warehouse using Hevo’s automated pipeline. Sign up for a free trial and learn more.

FAQs