Data Quality Checks: Your Key to Reliable and Accurate Information

In today’s world, data is everywhere – from social media to online shopping. Wherever you turn, we are constantly generating and consuming data.

With this surplus of data all around us, we need to ensure the data we use is reliable and accurate. That’s where data quality checks enter the picture.

Just like high-quality ingredients are essential to cooking a high-quality meal, high-quality data is essential for making high-quality decisions. Data quality is increasingly important in today’s data-driven economy, where organizations rely on good data to stay competitive.

Data quality checks enable organizations to verify the accuracy, completeness, consistency, validity, and timeliness of the data they rely on for decision-making. Without these checks, an organization risks making decisions based on bad or incomplete information, which can negatively impact the organization and its users.

In this article, we will explore some common data quality checks and outline some of the best tools you can use to perform them.

Key Takeaways:

• Every organization has unique data quality needs, but five common data quality checks include accuracy, completeness, consistency, validity, and integrity.
• Specialized software that can enhance and automate your data quality checks is a crucial investment for a data-driven business.
• Coperor offers data quality tools to take your business to the next level and optimize decision-making.

5 Common Data Quality Checks

Data can be as diverse as the individuals they belong to. Different industries (and even different organizations within the same industry) have different data quality priorities. But there are at least five general data quality checks every organization should prioritize:

1. Accuracy

Accuracy checks involve comparing data against a known source to verify that your data is correct. For example, a healthcare provider may compare its patient data against its billing system to ensure that the patients’ names, addresses, and contact information are accurate and up to date.

2. Completeness

While comparing one source of data to another is relatively straightforward, it can be challenging to recognize when data is missing from both sources. That is why completeness checks are important. Completeness checks involve ensuring that all required data is present. For example, a health insurer may check that all required fields are completed in new registrations with their organization.

3. Consistency

While some pieces of data are static (like a birthdate or SSN), other pieces of data change frequently over time—things like phone numbers, addresses, and billing information. Consistency checks verify that data is consistent across multiple sources and should include a process to resolve conflicts in data. For example, if a patient’s phone number differs between sources, a consistency check should include a solution to update the out-of-date source with the correct number.

4. Validity

While some data has no “right” answers, an organization might have constraints to validate that data is in the expected format and holds acceptable values. Essentially, your validity checks should make sure data is formatted correctly and falls within an acceptable range. For example, a phone number can be nearly any combination of 10 digits and might include other details like an extension or phone type (home, office, or mobile). However, a phone number should never include fewer than 10 digits or non-numeric characters.

Similarly, a patient’s age should comprise 1-3 digits and exclude any alphabetical or special characters. A validity check can find discrepancies in patient age that might be hard to locate without performing a targeted test.

5. Integrity/Timeliness

While accuracy, completeness, consistency, and validity checks compare data to other pieces of information, integrity checks compare data to itself over time. Integrity checks ensure that data is not corrupt or tampered with over time.

Image Source: MDPI

The Essential Tools for Data Quality Checks

Ensuring the accuracy, completeness, consistency, validity, and integrity of your data is an important part of promoting data health, regardless of your industry. Thorough and consistent data quality checks can be daunting, especially for industries like healthcare (with a large volume of consumer data) or finance (with a large volume of financial contracts). Thankfully, specialized tools like Coperor can help organizations to make the most of their data.

Gaine Coperor is a Master Data Management (MDM) platform that provides a range of features and capabilities that make it an excellent choice for performing data quality checks. Coperor’s data profiling capabilities allow users to analyze the quality of their data across various dimensions, including accuracy, completeness, consistency, validity, and integrity.

Using Coperor allows you to identify inconsistencies and take corrective actions much quicker and easier than with a manual data quality check process.

Image Source: Hubspot

Data Enrichment

The reason companies like Meta or Google are such titans in the business world isn’t just because of their revenue. Increasingly, the strength of a business depends on the quality of its data – and one of the reasons Meta and Google are household names today is because of the quality of their data.

Data enrichment is the process of “enriching” your existing data with new pieces of data. While this can be done manually, searching for and adding new data is prone to human error without specialized tools. These tools automate the data enrichment process by querying existing sources like social media, third-party providers, or the organization’s own data.

Data enrichment maximizes the usefulness of your existing resources by automating much of the process, which is substantially quicker and more accurate than a manual process. Coperor offers a range of data cleansing and enrichment capabilities, like data standardization and normalization, which help ensure data consistency across multiple systems and sources.

Taking the Next Steps with Your Data

Concepts like data completeness may seem simple, but defining the practical next steps can be challenging. Your data is too important to neglect, but where should you go from here?

Coperor Platform by Gaine is an essential tool for any organization that relies on data to make informed decisions. As data becomes more and more important in the business world, Coperor has the tools to take your data quality to the next level.

To see how Coperor can help with your data quality checks, watch a demo or contact us today.