In a world that’s more connected than ever, the amount of data, as well as its sources, continues to increase. While managing such a massive amount of data is tricky, there’s another big challenge: maintaining data quality.
Do you know, data quality issues cost companies in the US more than $3 trillion annually? For many businesses, it translates into financial loss, revision in policies, and marred reputation.
But why do data quality issues occur?
Because data is often riddled with errors, lacks consistency, or contains duplicates. This can cause interruptions and complications in business processes, resulting in wasted opportunities and decreased ROI.
This is where data profiling comes in handy. It analyzes and gives a complete breakdown of the source data to help users understand and uncover actionable insights to improve business intelligence. Data profiling in ETL is a vital step to ensure data integrity and quality.
In this article, we’ll explain what is data profiling, why is data profiling essential for businesses, and how data profiling tools help simplify this task.
What is Data Profiling?
Data profiling offers critical insights into the information that an organization can leverage to its benefit for decision-making and analysis.
Data profiling helps evaluate the integrity of data by presenting a complete breakdown of its statistical characteristics, such as error count, warning count, duplicate percentage, and minimum and maximum value, enabling detailed data inspection. This information assists users in identifying quality issues, risks, and overall trends.
Data profiling tools use analytical algorithms to help scrutinize the data to determine its validity. These tools play a vital role in helping businesses streamline their data strategy with the company’s principles and objectives. Now that we know what data profiling is, let’s discuss the different processes that require data profiling.
Where is Data Profiling Used?
Generally, data profiling is used in the following processes:
Data migration involves moving a high volume of information across heterogeneous systems, such as files, databases, etc. However, before initiating the transfer via a data migration tool, it is essential to profile the data to identify discrepancies and resolve them to maintain consistency between the old and new systems.
Data profiling at an initial stage of migration can reduce the risk of errors, duplications, and incorrect information.
Data integration creates a holistic view of enterprise data by merging it from disparate sources. Profiling data in the initial phase of integration ensures that there are no errors when source data is integrated and loaded into a data warehouse, data hub, or data mart.
Data cleansing, a primary step in the data preparation process, helps with error rectification and deduplication to authenticate the validity and relevance of the data. However, data cleansing is only beneficial for data sets you know are corrupt. Often, poor quality data loiters in the system unnoticed and unaddressed until it is identified via data profiling.
Thus, data quality and data profiling tools methodically examine huge amounts of data to identify incorrect fields, null values, and other statistical irregularities that might affect data processes.
Why Do You Need Data Profiling?
Data profiling is critical to the validity of data processes as it helps you answer the following questions regarding your data:
- Does the data contain any null or blank values?
- Are there any anomalies in the data? Do they have a distinct pattern?
- Does it contain any duplicate values? What is the ratio of unique values?
- What is the range of values in the source data? Are the minimum and maximum values within your expected range?
Getting the answer to these questions can help you maintain the quality of your enterprise data and eradicate errors that can negatively influence the business processes.
Challenges Associated with Data Profiling
Data profiling becomes challenging when you are dealing with large data volumes. To tackle this challenge, it is recommended to divide the data into segments and profile smaller datasets at a time.
Opting for manual data profiling presents a different set of challenges and won’t be possible without the help of a professional, as it involves performing frequent queries to obtain essential insights about your data. This is a more resource-intensive method. Moreover, chances are that you will be able to check just a subsection of your overall data as it might be time-consuming to manually profile the complete data set.
A preferred solution is to use a data profiling tool that can help you easily segment datasets. Most data profiling tools also offer automation, reducing manual efforts and time.
Automated Data Profiling with Astera Centerprise
Understanding different aspects of your enterprise data can help you efficiently manage your business operations, strategize an efficient business plan, and decide longstanding objectives. And data profiling tools can help you accomplish these goals.
Astera Centerprise is an enterprise-grade data integration software that supports data profiling in ETL in a code-free environment with a drag-and-drop interface, in addition to data quality and cleansing. The data profiling capabilities in Astera Centerprise ensure that users get access to accurate data with minimal IT support.