What is Data Profiling?

Data Profiling can be described as the process of analyzing, reviewing, and summarizing the data to gather valuable insights from the data. The process helps in discovering the issues in the quality of the data, risks involved, and the overall trend. The quality of this data is examined on the basis of accuracy, completeness, accessibility, and consistency. Data profiling is generally integrated with the ETL process to cleanse and deliver quality data to the target site. It can also eliminate the common errors in the databases.

Data profiling can also be called data discovery, data quality analysis, or data assessment. At the beginning of a project, many organizations use the process of data profiling to know if they have collected enough data, if the data can be reused, and if the project is worth continuing or not. It helps to figure out whether the collected data aligns with business standards and goals or not.

Data profiling includes:

• Identifying the types of data

• Grouping the data into different categories

• Discovering metadata and evaluating its reliability

• Collecting statistics like min, max, sum, count, etc

• Tagging of the data with different categories and keywords

• To carry out inter-table analysis

• Recognizing the embedded value and functional dependencies

• Carrying out data quality assessment

Types of Data Profiling

There are three types of data profiling:

• Structure Discovery:

In structure discovery, the main focus is on the formatting of the data, the data should be uniform, consistent, and formatted correctly. The process helps us to know how well structured the data is. The process also involves using statistical tools like min, max, sum, count, etc and also performing some mathematical checks on the data.

• Content Discovery:

In content discovery, the quality of individual pieces of data is assessed to identify the errors. It even helps to identify the specific row that is causing issues or problems in the data. For example, it helps to identify incomplete or null values like phone numbers with no area codes.

• Relationship Discovery:

In Relationship Discovery, we get to know how different parts of data are interrelated. It helps in discovering the similarities, differences, associations, and connections among various data sources. Understanding the relationship is a crucial step in reusing the data.

Steps involved in Data Profiling Process

Data Profiling is a four-step process:

• It is used at the start of a project to know if data is appropriate for analysis—and make a “go / no go” selection on the project.

• To recognize and solve the data quality issues in the source data before moving it to the target site.

• Identifying the issues in the quality of the data which can be rectified by Extract-Transform-Load (ETL). Learn What is ETL.

• Identify hierarchical structures, and relationships between foreign and private keys, and use them to streamline the ETL process.

Benefits of Data Profiling

• Higher Quality and more Credibility:

Data profiling ensures that the data that we are using is of the best quality so that we can gather important and helpful information from it. The process can prove to be very useful in making business decisions, identifying issues that the organization is facing, and predicting the future health of the business.

• Accurate Decision-Making:

Data profiling helps in keeping a check on small mistakes from becoming notable obstructions. It helps in predicting different conclusions of a business scenario. To keep in check the future health of the business accurate decision-making can prove to be a very beneficial feature of data profiling.

• Dynamic Crisis Management:

Data profiling helps businesses to anticipate possible obstructions before they even arise. This way businesses prevent themselves from getting affected by such unforeseen issues.

• Organized Sorting:

Databases usually interact with data from various sources such as surveys, feedback, and social media. Data profiling can be used to know the exact source of that database to keep in check that it is encrypted for ensuring data security. It also helps to ensure that the data matches the required business rules and standard statistical measures.

• Eliminating Errors:

Data profiling can prove to be very useful in eliminating the errors like outliers or missing values and protects the added costs of a data-driven project.

Best Data Profiling Tools

• SAS DataFlux Data Management Server:

It consolidates data quality, data management, and data integration. It provides its users with the capability to standardize schemes and data profiles. It can be used by various types of businesses to monitor, bring out and verify data. It keeps in check that the businesses make use of quality data in every process.

• IBM InfoSphere Information Analyzer:

It is used to evaluate the structure of the data and the quality of the data across different systems. Various profiling functions can be carried out Column Analysis, Primary Key Analysis, Natural Key Analysis, etc. It can be contemplated for data warehousing, data management, data intelligence, etc.

• Talend Open Studio:

It provides open-source tools for customizable data assessment, analysis with graphical charts, fraud pattern detection, advanced matching, a pattern library, and a time column correlation.

• SAP Business Objects Data Services (BODS):

It is one of the most popular data profiling tools and it helps businesses to do a thorough analysis to recognize the unpredictability of data. It has many valuable features like quality monitoring, data profiling, metadata management, etc. It also can be used to carry out detailed profiling, redundancy checks, and pattern distribution.

• Informatica Data Explorer

It provides users with various data quality and profiling solutions to do an in-depth and fast analysis of the data. This tool can examine every data record from all the data sources to recognize the obstructions in the available data. It also identifies the hidden relationships in the data records. The tool easily finds connections between multiple data sources on highly complex datasets. It has numerous pre-built regulations that are applicable to both structured and unstructured data for profiling.

• Melissa Data Profiler

The tool has the capability to perform various functions like data verification, data matching, data profiling as well as data enrichment. It helps to check the consistency and quality of the data before its even loaded into the data warehouse.