What is Data Profiling? Definition, Techniques, and Benefits

Introduction to Data Profiling

In order to effectively extract valuable and actionable insights from data, these metrics must be profiled beforehand. By conducting data profiling, organizations can effectively manage their information's quality.

This is becoming increasingly important as more companies are generating large volumes of data each day. Currently, the average business manages 162.9 terabytes of data, while the average enterprise has 347.56 terabytes.

However, according to the Harvard Business Review, only 3% of data meet quality standards. Additionally, on average, 47% of new data have at least one critical error. With mismanaged information, businesses may miss out on profitable opportunities and waste valuable time and money. Organizations can prevent this by establishing a well-defined data profiling system.

What is Data Profiling?

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Data profiling is the act of reviewing and analyzing datasets to understand their structure and information. This process enables organizations to identify interrelationships between different databases and trends.

It also helps to ensure that the metrics align with business rules and standard statistical measurements. Therefore, with data profiling, all generated information will be consistent and accessible for users. The following are general processes that profiling entails.

  • Collection of descriptive statistics
  • Identify different data structures, types, and patterns
  • Employ keywords, categorize datasets, and create descriptions
  • Conduct data quality examinations
  • Determine metadata, which is data that describes or provides information about another dataset
  • Pinpoint distributions, functional dependencies, embedded value dependencies, and foreign-key candidates in the database

Types of Data Profiling

There are 3 main types of data profiling tools that organizations commonly capitalize on. Effectively implementing these processes will help improve data quality and enable users to gain more insight into their information sources. The following are the 3 key ways to profile data.

1. Structure Discovery

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Structure discovery is the process of validating data to make sure it is correctly formatted and consistent with other datasets. Also referred to as structure analysis, this practice can be used for various techniques.

For example, organizations can use structure discovery for pattern matching which is the process of finding sequences in a dataset. A company may have a database of addresses and will use pattern matching to find specific sets within it.

Organizations can also use structure discovery to assess basic statistics. In which they can identify minimum and maximum values, averages, modes, and standard deviations in their data.

2. Content Discovery

Content discovery involves closely examining every element in a database to ensure data quality. This process helps business owners highlight null or flawed values, to which they can promptly rectify them.

Content discovery also entails a standardization process to make sure that data is consistent. For example, a database with customers' phone numbers must be in the correct format of 1-123-456-7890 for proper analysis and extraction. In the case that data is in a non-standard format, the company will be unable to effectively communicate with its consumers.

3. Relationship Discovery

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Relationship discovery is the process of identifying which datasets the company is using and understanding the relationships between different sources. To perform relationship discovery, brands must conduct metadata analysis to find connections and overlapping data.

Data Profiling Techniques

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According to a 2019 study, 31% of companies are considered to be data-driven. This entails leveraging metrics and analytics and employing data management tools, such as data profiling. To effectively assess their trove of data, brands have been utilizing the following profiling techniques.

  • Column Profiling
Column profiling is the method of assessing tables and quantifying the entries in each column. This helps reveal frequency distributions and data trends within the column.

  • Cross-Column Profiling

Cross-column profiling consists of key analysis and dependency analysis. Organizations conduct the former analysis by assessing data values for a primary key. On the other hand, dependency analysis is a complex method of identifying relationships and structures in a data set. By using both of these analysis techniques business teams can analyze the dependencies of data attributes in one table.

  • Cross-Table Profiling

This practice uses key analysis to pinpoint stray data and semantic and syntactic discrepancies. Doing so helps eliminate duplicates and redundant information and streamlines data mapping. By performing cross-table profiling, organizations can also analyze the connection between columns from different tables.

  • Data Rule Validation

Data rule validation verifies that datasets are following established rules and measurement standards. Organizations use this technique to improve their data's quality and usability.

Benefits of Data Profiling

The mismanagement of data quality can cause negative effects on business operations. In fact, issues with the quality of data cost businesses in the U.S. more than 3 trillion dollars a year. Not only is capital wasted, but organizations must also spend time re-strategizing and rebuilding their reputation. To protect their bottom line, businesses must profile and control their incoming metrics. The following are other benefits to data profiling.

Improved Data Quality and Reliability

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Through data profiling, organizations can guarantee that there are no duplications, null values, or anomalies. It also helps filter data, ensuring that the brand has useful and valuable information on hand. Therefore, managers and senior-level executives can rely on their data's quality and credibility to make important business decisions.

Make Data Driven Forecasts

Organizations can identify potential future outcomes regarding the market and their business and make predictive decisions with profiled information. This prepares the brand to address issues before they occur and allows them to effectively safeguard their financial health.

Enhanced Data Organization

Organizational data can come from various sources, from business software and social media. Data profiling tools allow business teams to trace their metrics to their source and guarantee encryption for security.