September 12

Data Integrity vs. Data Quality: How are They Different?

In the world of data analytics, you’ll hear a lot of terms that sound awfully similar to each other. For example, data integrity and data quality are terms that, on the surface, could mean the same thing. In fact, many people use these terms equally, but that is inaccurate.

Both data integrity and data quality, in their proper definitions, are equally important. Your business can’t thrive if your data has one but not the other.

What is Data Quality?

Data quality has to do with the state of your data.

If you have data quality, your data is suitable for your needs. It’s reliable and meets your specific criteria, so it gets the thumbs up from your company. Data quality means that your data is full of practical and valuable information for your business.

Data without data quality won’t serve the purposes that you have in mind for it.

Your company may have fantastic, out-of-this-world data, but if it isn’t useful to your business, it isn’t quality. For example, if you own a thermometer company and come into some primo data on legwarmers, will that be beneficial to you? Nope. Keep moving along, please.

There are six widely accepted components that are considered part of data quality. It must be complete, unique, timely, accurate, valid, and consistent. 

Why Data Quality is Important

Your business has a higher chance of making more impactful and beneficial decisions if it has reached data quality. On the other hand, when your data is substandard, you’re at risk of making decisions that lead to a negative financial impact. As per Gartner, non-quality data can cost a business $9.7 million annually.

Making ill-informed decisions are as dangerous as making blind decisions. And it doesn’t only affect your bottom line. These poor decisions based on flawed data can trickle down to impact your employee’s productivity. This is because they may be incorrectly basing operations on the wrong data, leading them down the wrong path.

This can all result in missed sales opportunities or essential information and goods going to the wrong place. None of that is a positive thing for any company.

With data quality, your business can:

  • Make better decisions 
  • Zero in on the right audience
  • Improve its marketing
  • Offer better customer relations
  • Incorporate good data more easily
  • Compete better in its field
  • Enjoy a rise in profits

What is Data Integrity?

Can you trust your data? That’s one of the biggest questions of data integrity.

Data integrity includes data quality but also so much more. It goes further to include how consistent your data remains as it’s integrated and updated. Data integrity means your information remains uncorrupted and unchanged across its lifecycle.

There’s even more to it, though. Data integrity also incorporates data security. It is imperative to protect your company from security breaches and keep it in accordance with regulatory compliance.

Data quality is only one pillar of data integrity. The other is data integration; this is the process of taking business information from multiple sources. Location intelligence and data enrichment give context to internal data by supplementing it with external data, offering a well-rounded data experience. 

Why Data Integrity is Important

Data integrity is a process that makes sure your information is useable so that you can maximize its use. With good data, you’ll be able to plug it into the proper systems because you’ll know exactly where it belongs and what aspect of your operations it speaks to.

Your employees will also have an easier time searching for the data they need. Data integrity ensures that your information is optimally stored, searchable, and traceable. If you have ever pulled a data set that you’ve questioned and then been unable to verify it, that is an excellent example of a lack of data integrity—a frustrating experience for all employees.

Data integrity is also useful for helping your company form better and more personal customer relations. You can target your communications to a specific subset of your customers and have better information at your disposal to pinpoint their needs. 

Of course, data security also plays into this area because if you suffer a data breach and customers’ sensitive data is compromised, they’ll lose trust in you. It doesn’t matter if you’ve done everything perfectly for them up to that point; keeping private data safe is a tremendous responsibility.

You can also free up valuable data storage space through data integrity. Since you would be cutting out all redundant data, you aren’t storing as much. That space means you can collect more data and search your existing data more efficiently.

The Differences Between Integrity and Quality

A business needs both data integrity and data quality if it’s going to flourish. So it’s essential to recognize the differences between the two in order to ensure you have both.

The two concepts are so interrelated that it almost isn’t fair to compare them. The heart of the matter is that you can’t have data integrity without data quality, although data quality without data integrity is possible.

Data quality is where your data process needs to start. If it isn’t quality, it isn’t worth your time. It is the first hurdle your data needs to leap for it to be acceptable for your company to use. Data integrity is the process that makes your data usable.

Data integrity and data quality aren’t an either/or situation. Instead, it’s an and situation. So you need to go further than mere quality.

Conclusion

Your data is meant to work for you, not the other way around. So before you even begin the process of cultivating your data, set your parameters in place; define what type of data you need and how you’ll use it. Right there, you’ll be setting yourself up for data quality. When you’re only targeting the information you need, you streamline your process and save the later work of weeding out unqualified data. Then set up your strategy for data integrity. How will you process and store your data? That will set you well on the road to data success.

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