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Data integrity

Once you correct your data mistakes, ensuring that the data is valid is critical to success.

Improve your data integrity today

Data integrity solidifies your data management strategy by assessing its validity and authenticity. Where data quality fixes the mistakes, data integrity ensures that the data is accurate.

At the end of the day, it’s what you do with your quality data that matters—that’s where data integrity comes in. Make sure you build your data management plan with a sturdy foundation and an inflow of quality data. Without this, you won’t be able to use your information to the best of its ability.

Data integrity helps you unlock the true potential of your data so you can drive your business forward.

Common data integrity issues

  • Overflow of data affects quality through partial and truncated fields
  • Disparate data sources with conflicting data elements
  • Legacy repositories with outdated information

40% of organizations lack trust in the data needed to achieve their key business objectives

— 2019 Global Data Management Report

 

Why data integrity is important

More than a third of businesses don’t trust their data. Do you trust your data for your business? Build a data management plan that brings you valid, authentic, and quality data that you can use to execute on other business strategies. In other words, make sure you have data integrity.

Data integrity is important because it keeps the process going. With a constant flow of data, the integrity of the information is what allows you to maintain a database that is unimpaired and complete.[1]

Sure, it’s great to have massive amounts of data flowing throughout your organization, but it isn’t useful if it’s not of high quality. To override these common data integrity issues, make sure you have a manageable amount of data flowing through your database, reliable sources, and modern practices that stay on top of the inflow of quality data.

Kempe, Shannon. Data Integrity Rules 1.” Dataversity.net.

Data integrity vs. data quality

Data quality and data integrity intersect. Data quality involves the completeness, accuracy, timeliness, and consistency of stored data, while data integrity checks for its validity and authenticity. Both are important for building a database that users trust with business-critical decision making.

Let’s consider some examples of data integrity issues vs. data quality issues that relate to address, phone, and email data to show the difference between the two.

For address data, two examples are:

The address “78 Markit Street” should really be “78 Market Street.” That’s a data quality issue.

The address “78 Market Street” doesn’t exist in the related city or town. That’s a data integrity issue.

For phone number data, two examples are:

A phone number has a different format other than the typical standardized “NNN-NNN-NNNN” format. That’s a data quality issue.

A Massachusetts phone number with a typical area code of “617” appears as “671.” That’s a data integrity issue.

For email data, two examples are:

An email contains invalid characters, such as (kvm)@comcast.net. That’s a data quality issue.

The domain for kvm@invaliddomain.com does not exist. That’s a data integrity issue.

Data integrity is about recoverability, traceability, and connectivity. The key to realizing the potential of data integrity is to build a trusted data environment. This is the path to infusing your organization with the information you need to win in the market.

Data integrity means actionable data insights

 However, though data integrity is a critical part of maintaining data accuracy, it is just one of many components in managing the overall quality of your data. Explore data quality management today

7 things you can do today to gain more confidence in your data

Are you skeptical of your data’s quality and effectiveness? Here are 7 tips to help make your data more trustworthy.

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Resolve your data quality and data integrity issues together today!

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