Many times the presumptions regarding the data that we store and provide are not always precise. Despite taking all necessary precautions our systems are not always totally free of bugs. As a result, the quality of data provided gets compromised and this could lead to several negative outcomes.

So, what can be done to prevent such situations? You need to profile your data.

Data Profiling

Data profiling refers to the procedure where the data sources get evaluated for their structure and quality to be sure of the accuracy of your data.

• Your data gets evaluated by comparing it to an existing data source.
• This can help you arrive at the right conclusion on its accuracy.
• Profiling your data helps in determining its completeness, precision, and validity.

Data profiling is done by combining it with the Extract, Transform, and Load process (ETL Process) most of the time. This helps in moving data from one location to another. Combining ETL and Data profiling helps to cleanse the data, fix the issues, and move quality data to the desired location. Profiling your data helps to identify the quality issues that require correction and the particular issues that can be fixed during the ETL process.

Why Is Data Profiling Important?

Using compromised data puts your entire project at risk. The problems and challenges that are faced by projects of data integration are similar to the ones faced by the IT industry. They include:

• Compromising quality to meet deadlines
• Lack of time
• Budget overrun
• Incorrect and insufficient understanding of the data source

These challenges and problems could be the result of certain issues including the following

• The difficulty in unwinding data due to its huge volumes
• The complexity of databases and applications
• The process is challenging and time-consuming
• This is also subject to errors

The quality, structure, and content of data need to be understood before getting it integrated or used in an application.

To understand the preciseness and quality of data most of the initiatives for data integration depend on external sources of information. This includes relying on the experience of staff, depending on source programs and documentation.

The external information could be wrong, outdated, or incomplete most of the time. This means you’ll have to put in more time, effort, and money to get these issues fixed and validate your data. You’ll be compromising the entire project in case you fail to do it.

Data Profiling is necessary for the following.

• To understand the data
• To organize it
• To compare and verify if your data matches with its source
• To ensure that the data match the statistical measure standards
• To make sure data is per the company’s business rules and regulations

Proper data profiling helps you to answer the following questions.

• Do you have the required data?
• Will that data be sufficient to complete your project in time?
• Is your data complete? or are there any blank values?
• How unique is your data?
• Does it support the requirements of your company?
• Does it accurately represent the needs of your organization?
• Is it possible to integrate, cross-refer, or consolidate the data for usability?
• What data requires cleaning?
• Has there been a duplication of data?
• Are the data patterns anomalous?
• What data requires transformation?
• Can you be sure of its correctness and consistency?

Being able to answer these questions correctly will ensure the quality of your data which is necessary for the overall growth and success of your business.

Data Profiling: The Different Techniques

In general data, profiling is done using 3 different techniques. They are the following.

1. Column Profiling Technique

Using this technique of profiling the number of times each value appears within each of the columns in the table is counted. This technique helps to discover the patterns in your data as well as to understand the frequency distribution.

2. Cross-Column Profiling Technique

There are two different processes under this technique of data profiling. They are:

• Key Analysis
• Dependency Analysis

Key Analysis is a process where a group of values within a table is scanned to trace out a prospective primary key.
Dependency analysis is carried out to identify the structures built/dependent relationships within the data set. Compared to Key Analysis the process of Dependency Analysis is more complex.
Both these techniques are used to identify dependencies and relationships among the attributes of data within a table.

3. Cross-Table Profiling Technique

This technique of profiling searches the entire table to identify possible foreign keys. This technique also helps to identify the differences and similarities in data and syntax between the tables. This will help in removing data redundancy and in locating data sets that can be charted together.
There is an additional step which is often considered as the final step in profiling data- Data Rule Validation. This proactive method verifies to understand the authenticity and accuracy of the data entered using a set of predefined rules.

The above-mentioned techniques of data profiling may be carried out using automated services or can be done by an analyst manually.

The data profiling process helps to verify whether the rows in the table are filled with accurate and valid data as well as to understand its quality. Once a problem is detected you need to get it fixed by mentioning the steps in your project for data quality. Data profiling helps in governing your data properly.

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