Objectives and Benefitsο
Objectivesο
Summary
Data profiling is the process of analyzing datasets to understand their characteristics, identify potential quality issues, and gain insights into data distribution. Data profiling is typically based on standard operations (such as statistical functions) that do not require in-depth knowledge of business rules and data understanding.
Just like in any given airport, each passenger goes through standard checks to ensure safety and compliance, data that is considered important for business needs to go through profiling to verify its characteristics.
Specifically, passengers go through systematic security scans to ensure they are not carrying anything dangerous. Similary standard column profiling helps systematically assess that data meets established format and content standards. This preventive check helps identify anomalies without having to program specific solutions, ensuring that the data is ready for its intended use.
Like identifying potential problems with baggage before they get too far into the airport system, profiling helps spot errors or inconsistencies in data early in the process. This helps resolve issues before they escalate and affect critical operations or decisions.
the enterprise Data Quality profiling approach is based on the following three levels of analysis:
Column profiling: Provides a first level understanding of format and content using statistics operations.
Cross-column profiling: Allows to identify primary keys as well as dependencies and relationships between two or more columns.
Cross-table profiling: Helps identify relationships between two tables through foreign key analysis, discovery of orphan records and potential duplicates.
Profiling is a means of executing a first level analysis with the following objectives:
Systematic data health check: Profiling helps identify anomalies in format and content without having to write specific code. Itβs a key enabler of systematic data health check approach.
Identify issues upstream: The fact that there is no need to write business specific code makes it easier to identify issues early in the data lifecycle.
Democratize data quality related information: Profiling results can be easily communicated to various stakeholders to promote transparency on data quality levels.
Benefitsο
Some of the benefits of Data Profiling are the following:
Velocity
With Data Profiling, stakeholders can quickly visualize the characteristics of the Data that is of interest. Key characteristics like unique values, missing values, minimum and maximum values allow users to get a sense of the level of quality of the Data before going to more detailed analysis.
Efficiency
Establishing systematic Data Profiling helps to improve the efficiency of teams. Manual and ad-hoc efforts related to getting a first understanding of Data are reduced. The time saved from this can thus be used for other tasks.
Resource Optimization
The results of the initial analyses enable the identification of areas requiring investment. The focus can be directed towards streams that show low level of Data Quality.