What is Data Profiling?
Data Profiling is the process of examining, understanding structure, content, and interrelationships, analyzing, and identifying potential for data projects. It yields a high-level overview of data and helps to identify data quality issues, risks, and overall trends. It produces critical insights into data that companies can leverage to their advantage. It uses analytical algorithms to detect dataset characteristics such as mean, minimum, maximum, percentile, and frequency to examine the data in detail. It then uses that information to align with the business goals.
What are the types of data profiling?
There are three types of data profiling- structure, content, and relationship discovery. It improves the data quality and gains more understanding of the data.
- Structure discovery– It is also known as structure analysis. This type of data profiling focuses on discovering the structure of the dataset and determines if the data is valid and consistently organized. It also examines simple basic statistics in the data such as mean, median, mode, and standard deviation to gain insight into the validity of the data.
- Content discovery- This type of profiling focuses on the data itself. It determines if the data contains errors or other systematic issues.
- Relationship discovery- This type of profiling focuses on the relationships between the data. This process helps make it possible to reuse data because the relationships are established.
What are the benefits of Data profiling?
- Improves data quality and credibility by eliminating duplications or anomalies.
- Improves decision-making by predicting possible outcomes for different scenarios.
- It helps to quickly identify and address data-related problems.