Data Profiling, Part 1: Why It Matters and What It Is

@SudhirNakka07|April 10, 2025 (5m ago)7 views

Introduction

This is Part 1 of a multi-part series on Data Profiling. We set the stage by explaining why data profiling matters, what it is, and the main categories involved.

Problem Statement

An increasing number of organizations and solutions depend on data to make decisions. AI, ML, LLMs, etc. are all dependent on large amounts of data to make decisions. The quality, accuracy and completeness of these solutions directly depends on the data that they depend on. Imagine training a model on a dataset that is incomplete, or has unclean data. The model will not perform well, hallucinate unexpected solutions and will not be able to make accurate predictions. This poses a billion dollar problem for solutions which rely on such services.

Most data accumulated or collected by organizations is incomplete or unreliable. Reasons for this include:

• Collecting data from a varied number of sources
• Sources not reliable enough
• Data not clean enough (Mixed data, random incomplete data)
• Data not accurate enough (Different error thresholds for different sources leading to a mix of error ranges)
• Data not consistent enough (Different formats, different sources, different time zones, different time intervals)
• Data not complete enough (Missing data, incomplete data, incomplete data due to errors)
• Data not relevant enough (Data not relevant to the problem)
• Data not useful enough (Data not useful for the problem)

So, are we all doomed?

Not necessarily. We have always depended on Data Analysts, Data Stewards to ensure that the raw data collected from different sources is always maintained, cleaned and accounted for. The only problem being - The rate at which we collect data vs the analysts we have do not match. This leads to an overload problem which inherently causes data quality degradation.

This is where solutions which provide intelligent Data stewarding services come into play. These services can help us/Analysts in identifying, cleaning and maintaining the data even when the volumes are extremely large. A core concept of such solutions is Data Profiling.

What is Data Profiling?

A process where we go through the data/files, make sense of it and identify the patterns, anomalies, and outliers.

Data profiling involves examining data to verify its structure, consistency, and overall quality. It provides an in-depth understanding of the data's content, enabling evaluation of its condition and the use of profiling tools to address any problems. This process enhances data analysis by uncovering relationships across various data sources, databases, and tables. By leveraging data profiling, businesses can identify patterns, anticipate customer behavior, and develop a strong data governance framework.

The data profiling process begins with gathering data from multiple sources into a centralized repository. Following collection, the next phase is data cleaning, which includes eliminating duplicates, filling missing values, and resolving inconsistencies to create a cohesive, structured dataset. Subsequent steps involve conducting quality assessments, analyzing data distributions, and performing cross-table analyses to gain deeper insights.

Data Profiling types

Profiling helps us with discovering the data we have and can be broken down into the following broad categories:

• Structural Discovery: Structural discovery is the process of identifying the structure of the data. It involves identifying the data types, the number of columns, the number of rows, and the data types of the columns.
• Content Discovery: Content discovery is the process of identifying the content of the data. It involves identifying the data values, the data ranges, the data patterns, and the data distributions.
• Relationship Discovery: Relationship discovery is the process of identifying the relationships between the data. It involves identifying the data relationships, the data dependencies, and the data dependencies.

These can be further broken down into the following sub-categories:

• Data Schema Discovery
• Data Consistency
• Data Distribution
• Data Anomalies
• Data Quality
• Data Outliers

Benefits of Data Profiling

Data profiling offers numerous advantages that ensure superior data quality and consistency. Some key benefits include:

• Risk Reduction: Data profiling provides clear visibility into your data’s structure and content, helping you comply with industry regulations on data handling. It also aids in identifying possible data vulnerabilities, thereby preventing breaches.

• Enhanced Data Governance: It plays a crucial role in supporting data governance activities such as data discovery, which helps you understand the data being used; data lineage, which traces data flow from origin to destination; and data literacy, which facilitates effective communication of data insights.

• Cost Savings: By uncovering potential issues related to data quality and content early on, data profiling helps avoid the significant expenses associated with correcting poor-quality data later.

Next in this series: Part 2 — Practical Profiling Checks and Metrics (coming soon)