Data Profiling and its benefits

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Data Profiling

Data Profiling is the process of examining data sources to determine the level of quality and complexity. It is an important phase in the Data Acquisition and Data Analytical processes. In Data Acquisition, profiling helps to design the table structures based on the findings like the probable candidate of Primary Key, nullable fields, etc. In Data Analytical processes, profiling is used to examine which datasets can be used for modeling, and to understand their relationships and the quality of the data.

This process is an essential part of data quality assurance, as it helps to identify potential issues that could impact the accuracy or completeness of data. It provides a complete view of all the attributes and the volatility of the data. Such information is used to determine the relationship with the other database objects and identify the join criteria.

How Data Finz handles Profiling activities:

Data Finz manages the profiling activities in two ways – 1) at the object level and 2) at the attribute level. Both the outputs are represented in visuals for easy understanding and making decisions.

When object-level profiling is done, it performs the statistics of all the attributes in that object and provides a visual report. It can be scheduled to run on a defined frequency, so the charts are available for review or reference at any point in time. Here is the output of Data Finz object-level profiling for a sample dataset.

Datafinz profiling activities

When attribute level profiling is done, it determines the KPI based on the aggregation measure used and shows the results in a chosen visual. It can be scheduled to run on a defined frequency, so the charts are available for review or reference at any point in time. Here is the output of Data Finz attribute level profiling for a sample dataset.

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Data Profiling Uses

  • Data profiling is a critical component of any data migration project. It helps to assess the feasibility of the project and to establish expectations for the accuracy of migrated data. Data profiling can be used to detect errors, outliers, and inconsistencies in source data. It can also be used to generate statistics about the data, such as the number of records, the number of unique values, and the distribution of values.
  • It can be a useful tool for business intelligence or other purposes. It allows you to analyze data to see patterns and trends. This can be helpful in identifying business opportunities or in understanding customer behavior. Data profiling can also be used to detect anomalies or outliers. This can be useful in fraud detection or in understanding business processes. Data profiling is a powerful tool that can be used in many different ways. With the right approach, it can help you to uncover hidden insights in your data.
  • Data profiling is a feature of many data analysis tools that allow users to generate statistics and information about the data set as a whole. This information can be used to validate data models, support data mining and machine learning algorithms, and even generate test data sets. Data profiling can help users identify errors and anomalies in the data, and it can also provide valuable insights into the relationships between different variables. In short, data profiling is a powerful tool for understanding and working with large data sets.
  • Data profiling is the process of examining data to assess the completeness, accuracy, consistency, and validity of the data. Data profiling is typically conducted on a sample of data, but can also be performed on the entire dataset. Data profiling can be used to assess the accuracy of data by comparing it to a known baseline or by using statistical methods.