Data Profiling is a method of cleansing, analyzing, monitoring, and reviewing data from existing databases and other sources for various data-related projects.
Data is now considered to be one of the most valuable assets of any organization. With organizations relying on evidence-based decision-making more than ever before, data also acts as a key decision-making tool. Almost all companies today, irrespective of their market position or size, leverage the data collected to analyze their business & customers, and thereby make smarter and informed business decisions that help drive business growth and profitability.
However, this analysis would lead to incorrect or partial insights if the data being analyzed is not of good quality or cannot be accessed by the necessary tools or analysts. Hence, there is a need for a robust Data Profiling mechanism that can ensure that the best quality data can easily be accessed as per business requirements.
This article will provide you with an in-depth understanding of what Data Profiling is along with a list of the best profiling tools available in the market.
Table of Contents
- What is Data Profiling?
- What are the Types of Data Profiling?
- What are the Benefits of Data Profiling?
- What are the Data Profiling Methods?
- What are the 5 Best Data Profiling Tools?
- What are the Data Profiling Challenges?
- What is Data Profiling In A Cloud-Based Data Pipeline?
- How to Improve Data-Driven Decisions With Data Profiling?
What is Data Profiling?
Data Profiling can be defined as the process of examining and analyzing data to create valuable summaries of it. The process yields a high-level overview that aids in
- discovering data quality issues,
- risks, and
- overall trends.
More specifically, Data Profiling sifts through data to determine its quality and legitimacy. Data Profiling is used for a wide variety of reasons, but it is most commonly used to determine the quality of data that is a component of a larger project. Usually, it is combined with an ETL process. If performed correctly, Data Profiling and ETL can together be leveraged to cleanse, enrich, and load quality data into a target location.
Data Profiling 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:
- Collecting Descriptive Statistics such as minimum and maximum values, count of values, etc., along with any other attributes that can be used to describe the basic features of the data going through the Data 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.
Data Profiling 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.
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What are the Types of Data Profiling?
The three types of Data Profiling are as follows:
- Structure Discovery: This type of profiling involves performing mathematical checks on the data such as sum, minimum, maximum, etc., along with other Descriptive Statistics. The fundamental aim of Structure discovery is to understand how well the data is structured and ensure data consistency. For example, consider a database having the contact number of all users. A Structure Discovery process would involve finding out 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 having the contact number of all users. A Content Discovery process would involve finding out the percentage of phone numbers having no area code.
- Relationship Discovery: Relationship Discovering 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?
The benefits of Data Profiling are as follows:
- Improved Data Quality and Credibility: Data Profiling 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 coming in from various sources, such as Social Media, Surveys, and other Big Data markets. Data Profiling 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?
The three base methods for Data Profiling are as follows:
- Column Profiling: In this method, the number of times every value appears within each column of a table 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. Together, 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 Data Profiling Tools?
The best Data Profiling tools available in the market:
- IBM InfoSphere Information Analyzer
- SAP Business Objects Data Services (BODS)
- Informatica Data Explorer
- Melissa Data Profiler
- SAS DataFlux Data Management Server
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 more than 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 Big Data, Business Intelligence, Data Warehousing, Management, etc., to analyze diverse data formats. 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 Data Profiling tools available in the market. It can help businesses seamlessly carry out an in-depth analysis to identify inconsistencies in their data and other data problems. It houses features such as Data Quality Monitoring, Metadata Management, Data Profiler, etc., in one package. 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 get an understanding of 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 Profiling and Quality solutions that give businesses the ability to carry out a faster and more thorough analysis of their data. This tool can efficiently scan every single data record from all data sources to identify various 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 Data Profiling gives businesses the ability to carry out numerous functions such as Data Profiling, Data Matching, Data Enrichment, Data Verification, etc. It is considered relatively easy to use and can be used to 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 consistency and quality of data. 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.
Melissa Data Profiling Pricing
Melissa does not follow a transparent pricing model for its Data 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 give users the ability to create and explore data profiles, design data standardization schemes, etc. It can be leveraged by businesses to extract, profile, standardize, monitor, and verify the data in an efficient and secure way. 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.
Apart from these, there are the best open-source data profiling tools available in the market.
What are the Data Profiling Challenges?
- While the goals of Data Profiling are simple, the actual work required is quite complicated, with various processes occurring from data acquisition to data warehousing.
- One of the obstacles that businesses have when attempting to create and operate a successful data profiling program is complexity.
- Another problem is the sheer volume of data generated by a typical firm, as well as the variety of data sources, which range from cloud-based systems to endpoint devices deployed as part of an internet-of-things ecosystem.
- The rapidity with which data enters an organization adds to the difficulty of implementing a successful data profiling program.
- These data preparation issues are magnified in organizations that have not yet adopted current data profiling tools and still rely on manual processes for the majority of their data preparation.
- Similarly, organizations that lack necessary resources, such as Trained Data Experts, Tools, and Financing, will find it more difficult to overcome these obstacles.
These same factors, however, make Data Profiling more important 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 prior to 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 these days, and they’re realizing that data ingestion can be done with the touch of 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, Business Intelligence tool, 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, Data Profiling is more important than ever. You’ll need an automated data warehouse in a cloud-based data pipeline architecture like Hevo that can handle data profiling and preparation on its own. Instead of employing a data profiling tool to analyze and treat the data, simply pour it into the automated data warehouse, where it will be cleaned, optimized, and prepared for analysis.
How to Improve Data-Driven Decisions With Data Profiling?
Only 26% of firms believe they have created a data-driven culture, according to IT Chronicles. For many organizations, the challenge is properly evaluating data and having faith in the conclusions.
Data Profiling solves these problems by ensuring that data is consistent and accurate before it is evaluated. Because the data has been validated, this information assists engineers in proactively resolving difficulties with data input and processing. It also allows for a better level of confidence in the results. Data profiling is an important method for establishing a data-driven company.
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. Building an in-house data integration solution would be a complex task that would require a high volume of resources. Businesses can instead use existing automated No-code data integration platforms like Hevo.Visit our Website to Explore Hevo
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