If you wonder what is data profiling and how it could help, the short answer is that it represents the act of transforming raw data into a meaningful and understandable form. One can do it for many purposes, such as statistical analysis or making data easier to understand. Data profiling is sometimes called data classification, categorization, or taxonomy. One can analyze many different data profiling methods and types of data. For example, large companies may have a dozen or more different types of profiles for manipulating user data.
Data profiling is a method to discover various aspects of data from within a database. The number of missing values in each column, the most common values in specific columns, or the number of relationships among different tables can all be found through data profiling.
Types of Data Profiling and Their Uses
1. Structure Discovery Data Profiling
It is the process of identifying the form and structure of a database. The structure discovery results in a list of the database tables, column names, types, and other descriptive information. In many cases, this can be used to identify whether the business needs structured data.
2. Content Discovery Data Profiling
It is the process of identifying data items that appear in a database. The result is a list of columns and the values in each column. Typically, this process does not include data types or descriptions, but this varies based on the analyst’s needs and the amount of data. Content discovery is often helpful for business analysis, competitor analysis, or market research purposes. For instance, it is used to gather aggregate statistics and identify trends.
3. Data Discovery Data Profiling
It identifies large quantities of missing data in a database. The result is a list of columns with missing values or other indications that data is missing. It can be performed manually by reading through tables or automatically using special-purpose software. In most cases, missing data indicates something has gone wrong with the database. However, it may be necessary to manually determine where the problem occurred – a process called data scrubbing or quality analysis.
4. Relationship Discovery Data Profiling
It is the process of identifying all relationships in a database, including foreign keys and any references to related rows. The result is a list of unique identifiers for each connection and a list of every value that exists in each row that is associated with it. This data can be used for reporting purposes to determine if the database schema is consistent and complete. One can also use it to ensure that changes to the database align with existing business practices.
5. Row-type discovery Data Profiling
It is the process of identifying all unique values that appear in one or more columns and mapping them back to the rows that contain them. The result is a list of row types, including customers, orders, and products. The result can then be used to perform data analysis or other tasks. This method can be helpful for data warehouse operations and other business processes. Also called required attribution.
6. Attribute Discovery Data Profiling
It is the process of identifying all unique values that appear in one or more columns and mapping them back to the rows that contain them. The result is a list of attributes, including first name, last name, and e-mail address. The result can then be used to perform data analysis or other tasks. This method can be valuable for data warehouse operations and other business processes.
Data profiling is a process used to characterize metadata. It can be used for various purposes and is highly beneficial for business. It helps organizations identify the right level of insight to proceed with a project. It also reduces time wasted and shortens the project lifecycle. It can also help ensure that the data used is of good quality. This is especially important when extracting data from manual systems.
Data profiling is a valuable tool to have in any business for the quick identification of errors. Businesses can use it for targeted tasks such as data warehouse quality analysis. In most cases, only those who manage databases need to know about these details. They can provide highly beneficial information for those who work with data daily. The process of data profiling is essential, and it’s necessary to make sure that things are done correctly.