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Data Profiling - How to Perform Data Quality Checks

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Data Profiling - How to Perform Data Quality Checks Data Profiling
Table of Contents

Introduction

Data Profiling is all the rage these days, and with good reason! It has revolutionized data analysis, making it much easier to ensure that your data will meet the needs of your project. It is one of the newest and most exciting data analysis techniques available in the market today. Unhealthy or bad data can cost millions to organizations and hence, data profiling has quickly become the de facto solution for extracting value from high volumes of data and it’s no wonder why! 

In this article, let’s dive into what exactly data profiling does, how it works, and when you should use it to get the best results from the data.

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What is data profiling?

The term data profiling refers to performing certain checks on a given data set. These checks are designed to detect anomalies in the data by examining and analyzing each attribute and flag outliers of the data as well as find missing data. The goal of profiling is to ensure that all attributes have valid values. It helps an organization to find data quality issues within the datasets. 

The profiling technique examines data in greater depth and assists in identifying the data validity and quality. It helps organizations to achieve their goals by providing an overview as well as insights into their data. The profiling process should be viewed as a preliminary step used at the start of the project to determine if the data is suitable for analysis or not. 

In the nutshell, data profiling is a process that enables an organization to identify, understand and manage its data more efficiently while maintaining data integrity and quality.

Why is data profiling important

Data profiling is crucial because it allows organizations to understand what kind of data they have. It helps business leaders identify gaps, make informed decisions about changes that need to be made, and ensure that everyone has access to a consistent set of data. This process helps in identifying the data types, the volume of data, and the processes that are generating and consuming this data. It also helps in understanding the most valuable insights from the data.

Data profiling impacts business decisions and helps in minimizing costly errors. It plays an important role in both short-term business goals and long-term company strategies. Profiling is important since companies deal with massive amounts of data daily. Without proper profiling in place, companies are flying blind to any major strategic change they undertake.

Types of data profiling

There are three types of data profiling 

Structure discovery  

Structure discovery is a technique for determining how effectively data is structured. It helps to determine if data is consistent and formatted correctly.

Content discovery

Content discovery focuses on closely examining the database’s individual elements to ensure data quality.

Relationship discovery 

Relationship discovery is all about determining connections between distinct datasets and how separate parts of the data are related.

Advantages of data profiling

Data profiling is a necessary step in the process of data preparation for analytics. It helps companies to take a proactive approach towards data integrity by identifying potential vulnerabilities and threats to the data. There are many benefits of data profiling such as – 

  • It provides insights into the quality and completeness of the data and can help identify potential issues with the data.
  • It’s easier to identify gaps in the dataset with the help of data profiling. 
  • It enables you to efficiently extract information that may otherwise be unknown. 
  • It provides a high-level overview of your data set and provides pointers for other analysis techniques to follow up on. 
  • Profiling helps identify possible problems in the data, such as inconsistencies or errors. 
  • Profiling helps in predictive decision-making. 
  • It improves data quality and legitimacy.
  • It helps to clean data, eliminate duplicates and filter out missing values. 
  • It helps to remove irrelevant information and narrow down a large dataset into a more manageable and valuable size.

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Data profiling challenges

Data profiling frequently necessitates working with a sheer volume of data. Manual profiling tasks can be extremely time-consuming and labor-intensive and small businesses may find automated profiling to be prohibitively expensive. 

Many organizations tend to store data in data silos and because data is distributed across multiple data silos, it often becomes difficult to locate data in one place. It becomes challenging for profiling as it demands having all of the data in a single destination.

Even for many organizations the lack of information about what data profiling is, how it should be done and when it should be done are some of its challenges.

Cloud and data profiling

Data profiling has become more sophisticated thanks to technology and it’s been able to provide insight and help to predict the future of the businesses. As we are generating massive amounts of data than before, businesses store all that data in the cloud and hence in the cloud, effective data profiling is more vital than ever before. With cloud computing, profiling is becoming increasingly simple and cost-effective and can analyze data on a more sophisticated level than was previously possible.

Data profiling can be done manually or it can be automated with the help of the tools. Manual data profiling is time-consuming and tedious while automated solutions are more accurate and faster but they require an upfront investment for setup and maintenance costs. There are many profiling tools available in the market. You can choose the best tool for your organization based on the size and method of operation. 

More and more businesses, large and small, are beginning to understand the importance of data analysis in today’s increasingly data-driven world. Unfortunately, many of these businesses are finding that their analysis efforts are hampered by difficulties with the quality of their data. 

As a result, many businesses have turned to new techniques in data profiling to improve and standardize the quality of their data, which allows them to better analyze the information they collect and so make more informed decisions in their business processes.

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