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Understanding Data Profiling

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  • Last Updated : 16 Jul, 2021
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Overview :
Everything in today’s world is all about generating data. With all these huge amounts of data lying around, there is a requirement for standard and quality. Data profiling comes into the picture here. Data profiling is the method of evaluating the quality and content of the data so that the data is filtered properly and a summarized version of the data is prepared. This newly profiled data is more accurate and complete.  

Example –
For example, we can use data profiling in an organization while starting a project to find out if sufficient data is available to pursue the project and whether the project is even worth pursuing. This insight helps the organization to set realistic goals and pursue them.

Categories of Data Profiling :

  1. Structure analysis or structure discovery –
    This type of data profiling focuses on achieving consistency and properly formatted data. This is done by using systems like pattern matching that also helps the analyst find the missing values very easily.
  2. Content discovery –
    This type of data profiling takes an intensive approach and focuses on the data directly. The data is checked individually and the null, incorrect values are picked out.
  3. Relationship discovery –
    This type of data profiling emphasizes the relationship between the data i.e the connections, similarities, differences, etc. This decreases the chances of having unaligned data in the database.

Challenges :
Data profiling sounds very easy at first however the huge amount of data that is generated every day is very hard to monitor and profile. This situation happens mostly in old legacy systems that have a lot of redundant and unorganized old data. Hence, to tackle this situation an expert is needed who has to run a lot of queries to sort out the meaningful data.  

Best practices in data profiling techniques :

  1. Column Profiling – 
    It is a type of data analysis technique that scans through the data column by column and checks the repetition of data inside the database. This is used to find the frequency distribution.
  2. Cross-column Profiling –
    It is a merge-up method consisting of two methods, dependency and key analysis. Here, the relationships inside the database are embedded inside a data set or not is checked.
  3. Cross-table Profiling – 
    It uses foreign keys to find out the orphaned data records inside the database and also shows the syntactical and semantic differences inside the database. Here, relationships among data objects are determined.
  4. Data rule validation profiling – 
    It checks and verifies that all the data follows the predefined rules and standards set by the organization. This helps in batch validating the data.

Importance :

  • It generates higher quality, valid, and verified information from the raw data.
  • There is no orphaned data remaining in the database.
  • It shows us the relationship among the database.
  • It ensures that all the generated data follows the organization’s standards.
  • The data remains consistent and connected.
  • It becomes easier to view and analyze the data.

Conclusion :
Finally, Data profiling is used generally at places where the quality of data is very much required. These projects may require gathering data from multiple databases for generating a final report. Here if we apply data profiling we can ensure that not corrupted or orphaned data goes into the final report and all the issues are caught. Also, when we convert or migrate the data from a database system to another one, we can use data profiling to ensure that the quality of the data is not compromised during the transfer.

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