Data profiling, the first step in data quality


Data Profiles is the act of analyzing the content of your data. Along with data profiling, We have two more components that would integrate data quality. It's all about data correction and data monitoring.


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Data correction It's the act of correcting your data when it's below standards. Secondly, Data Monitoring it is the continuous act of determining Data Quality Standards on a set of metrics that are meaningful to the business, Review the results regularly and take corrective action that may exceed acceptable quality thresholds.

But today we're focusing only on data profile. This provides institutions with the ability to analyze large amounts of data quickly in a systematic and repeatable procedure.

The analysis performed by data profiling

A data profiling tool makes it possible to perform different types of analysis, the combination of which makes it possible to obtain a much more complete perspective of this asset. Among them are:

  • Completeness analysis: In view of its results, You'll find out how often a certain attribute is completed and how often it's left blank or null.
  • Value Distribution Analysis: It allows you to find out what the distribution of records is by means of different values for a given attribute.
  • Uniqueness analysis: It's the fastest way to know how many unique values (Different) are found for a given attribute in all records. By means of this analytics, Easily identify duplicates.
  • Pattern Analysis: it is the medium through which data profile It makes it possible to know what formats were found for a given attribute and what is the distribution of records through that or other formats.
  • Range Analysis: It is used to find out what the minimum values are, maximum and average given for a given attribute.

In practice, the Data profiling can add value in a wide variety of situations., something you probably already know if you use it regularly in your organization. Some of the the scenarios where their contribution is most enriching son:

a) Source System Data Quality Initiatives. One of the goals of such a project is to try to correct existing problems and prevent others from appearing in the future. Data profiling can help maximize project ROI. Through data profiling, the areas within the system that suffer from the most serious data quality issues can be identified, and / or numerous. Profiling would also make it easier to detect quality issues related to incorrect manual inputs or faulty system interfaces.

B) Data Migration Projects. Data profiling can help minimize the risk of moving data from a legacy system to the new destination. Here, Data profiling would uncover existing quality issues before migrating data.. Therefore, You could act on the code or make the necessary changes to the target system.

c) Data Warehousing & Business Intelligence Initiatives. The common note to both types of projects is the need to compile data from disparate systems. For this case, Profiling can help ensure project success by identifying three types of issues:

  • Those related to the quality of the data at the source, to be corrected there.
  • Those by quality attributes that can be corrected in ETL processing.
  • Those that have to do with the discovery of rules deal which could lead to the revocation of the project.

Anyway, All of these benefits are multiplied when data profiling is done automatically, instead of doing it manually. The Data Profiling Tools They will help you gain speed, Completeness of analysis, allowing, repeatedly, You can also enjoy a centralized repository for the data and metadata warehouse that facilitates the exchange of information by different business users.

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