Data Profiling In ETL: Definition, Process, Tools, And Best Practices

The worth of your data relies on how effectively you sort out and examine it. As data becomes more complex and comes from various sources, it becomes crucial to assess its content and quality.

Surprisingly, only around 3% of data meets quality standards, which means that companies with poorly managed data suffer significant losses in terms of time, money, and resources.

This is where Data Profiling comes into play as a powerful tool to combat bad data. It involves monitoring and cleansing data to enhance its quality and gain a competitive edge in the market. In this article, we will dive into the process of data profiling, its definition, tools, and technologies, and explore how it can assist businesses in resolving data issues.

Table of Contents

What Is Data Profiling?

Data profiling is the process of analyzing and understanding data to create useful summaries. It helps you to give a quick overview of the data, which can show any quality issues, risks, or patterns associated with the data. Many companies in today’s time, use this process to get valuable insights from their data and understand their data well.

More simply, data profiling checks the data quality and accuracy by analyzing it. It uses tools to find out details like the average value, highest and lowest points, and how often certain data appears. It also looks for important relationships and patterns with in the data. This information helps business analysts to see if their data matches their business goals and standards.

By using data profiling, companies can avoid costly mistakes, such as missing or incorrect information in customer databases, spotting problems like error values that shouldn’t be there, missing data, or values that don’t fit expected patterns.

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Benefits Of Data Profiling

Bad or incorrect data can cause businesses a loss of 30% or more of their revenue. For many companies, this means losing millions of dollars and damaging their reputations in the global market. But how do data quality problems happen?

So, it is mainly due to ignorance of data quality standards. Companies usually get so focused on collecting data and managing day-to-day tasks that they forget to check the quality of their data. This can lead to lost productivity, missed sales, and lost opportunities to improve profits. This is where data profiling tools come into action.

A data profiling tool reviews data, performs data cleaning, and updates data, making sure it is accurate and helpful. It provides useful insights that you can access from your laptop. Some of the key benefits of data profiling include:

Improved Data Quality and Trustworthiness – Data profiling helps you to remove duplicate data or errors. It identifies useful information that can impact business decisions and spots problems in the system that could affect a company’s future.
Better Decision-Making – With profiled data, small errors can be fixed before they grow into bigger issues. It also predicts possible outcomes, and perform data modeling helping companies to make smarter choices based on an accurate view of their performance.
Crisis Prevention – Data profiling can detect issues early and fix them before they become larger problems, helping companies avoid crises.
Organized Data Sorting – Companies often deal with various types of data from blogs, social media, and other sources. Data profiling traces the source, checks for security, and ensures the data follows business rules and standards.

By understanding the relationship between available, missing, and needed data, companies can plan their future strategies and long-term goals. Using a data profiling tool can make these processes smoother and more efficient.

Data Profiling Tools

Data profiling tools are essential for businesses to analyze and understand the value of their data assets. These tools can handle various types of data, from big data to structured and unstructured data, making it easier to undertake large-scale data projects efficiently. Some of the top data profiling tools include:

IBM Info Sphere Information Analyzer: This widely-used tool allows users to assess data quality, content, and structure. It offers features like column analysis, primary key analysis, natural key analysis, foreign key analysis, and cross-domain analysis.
SAP Business Objects Data Services (BODS): This tool offers plenty of features, including Data cleaning, data management, data integration, metadata management, and data profiling in one package. It helps you to identify data inconsistencies and problems before transforming them into actionable insights.
Informatica Data Quality Solution: Informatica is known for its fast data profiling capabilities and in-depth analysis. It offers automated discovery functionalities, reducing specification and testing cycles for the IT team. It also supports data governance procedures.
Talend Open Studio: This suite of open-source tools provides advanced data analytics without the need for coding. It offers customizable data assessment, fraud pattern detection, graphical chart analytics, and time column correlation.
Oracle Enterprise Data Quality: This tool offers features like data quality management, data profiling, auditing, dashboards, standardization of fields, automation of matching and merging, address and product data verification, and integration with Oracle Master Data Management.

Choosing the right data profiling tool depends on business goals, strategy, and data quality cost. It is essential to evaluate the features and capabilities of each tool to find the best fit for your business

Types of Data Profiling

To effectively analyze and improve the quality of your data, there are three main types of data profiling that you should consider:

Structure Discovery: This involves validating the consistency and proper formatting of your data. It includes performing mathematical checks such as calculating sums, finding minimum or maximum values, and ensuring that data is correctly structured. By using structure discovery, you can gain insights into how well your data is organized. For example, you can identify what percentage of phone numbers in your dataset do not have the correct number of digits.

Content Discovery: Content discovery focuses on examining individual data records to uncover any errors or inconsistencies. It helps identify specific rows within a table that may contain problems and reveals systemic issues present in the data. For instance, content discovery can help you identify phone numbers without area codes or any other data inconsistencies within your dataset.

Relationship Discovery: Relationship discovery involves understanding how different parts of your data are interconnected. It helps you identify key relationships between tables in a database or references between cells or tables in a spreadsheet.

This understanding of relationships is crucial for effectively utilizing your data. It allows you to combine related data sources into a unified format or import them in a way that preserves important relationships.

By utilizing these three types of data profiling, you can gain valuable insights into the structure, content, and relationships within your data. This knowledge helps you identify and resolve data quality issues, ensuring that your data is reliable and valuable for decision-making purposes.

Data profiling with data lakes and the cloud

As more companies store large amounts of data in the cloud, effective data profiling becomes more important. But what about the storage and where to store this large amount of data?

Data warehouse in this case can be expensive for companies, this is where Cloud-based storage systems come into action, you can now store huge volumes of data in it and even can scale it according to your requirements.

So, to stay competitive in today’s data-driven world, companies are actively opting for cloud based data lakes system that are capable of performing various actions on data. One of these important feature that cloud servers provide is of Data profiling. It plays a key role in making sure that businesses are handling their data effectively by following all the compliance and regulations, which can make the difference between success and failure.

Data Profiling FAQs

What is data profiling, and how does it work?

Data profiling is the process of examining and analyzing data to gain valuable insights. It involves summarizing the data to uncover issues, risks, and trends. For example, a company may analyze sales data to identify patterns, anomalies, or errors. By collecting statistics, data types, and patterns, data profiling helps businesses understand the quality and characteristics of their data.

How do I perform data profiling?

Data profiling involves several steps:

Collecting descriptive statistics like minimum, maximum, count, and sum.
Analyzing data types, length, and recurring patterns.
Tagging data with keywords, descriptions, or categories.
Assessing data quality and the risks associated with joining the data.
Discovering metadata and evaluating its accuracy.

What are the stages of the data profiling process?

The data profiling process typically includes the following stages:
Profiling inputs: Gathering the necessary data for analysis.
Decision process models: Establishing the framework for analyzing the data.
Crime assessment: Evaluating the quality and characteristics of the data.
Criminal profiling: Identifying patterns, inconsistencies, and anomalies in the data.
Investigation: Digging deeper into the data to gain deeper insights.
Apprehension: Taking action based on the insights gained from data profiling.