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Blog / Data Integrity vs Data Quality: What's the Difference?

Data Integrity vs Data Quality: What's the Difference?

Modern data management can be complex. Not only are businesses trying to deal with more data than they've ever had to before, but they also need to navigate their way around complex data terminology if they are going to have a consistent understanding of data management across the organization. 

When certain terms and phrases are used interchangeably (sometimes when they shouldn't be!) it can lead to confusion and misunderstandings, reducing collaboration and undermining efforts to become an efficient data-driven business.

Two terms that are common culprits for confusion are “data integrity” vs “data quality”, which actually refer to related but distinct areas of data management. 

In this article, we will provide clear definitions of both of these terms, look at the key differences, and explain how Adverity can help improve data quality and data integrity across your business. 

Data quality

In simple terms, data quality can be defined as how far the data you collect is fit for its intended purpose. 

There are five dimensions that influence data quality: accuracy, completeness, consistency, timeliness, and relevancy. 

To help ensure your data meets all these dimensions of quality, it’s important to implement effective data quality management. 

Data quality management

High-quality data doesn’t happen by accident. It’s a result of using the right tools and techniques to ensure your data meets each of the data quality principles. 

This is known as data quality management and involves the consistent fetching, transformation, monitoring, and error detection of the data that’s being consolidated. 

Effective data quality management is almost impossible to implement manually. That’s where data integration platforms like Adverity can help you achieve success across each of the data quality dimensions: 

Accuracy

High-quality data should be correct and free from errors.

A lot of common errors in data are caused by manual data integration. A simple mistake when copying and pasting data can cause significant discrepancies in the analysis that is used by the business to make decisions. 

By automating the data integration process with a solution like Adverity, you can improve data quality, immediately overcoming data accuracy errors caused by manual integration.

 High-quality data should be accurate, correct, and free from errors.

High-quality data should be accurate, correct, and free from errors.

 

Completeness

For data to be high-quality, it needs to be complete, with no part of it missing. 

This is something that is hard to monitor manually. 

Adverity has a suite of activity monitoring tools to help you check and maintain the progress and completeness of each of your connected data streams.

Consistency

Another key principle of data quality, consistency refers to the standardization of the format and structure of your data so it can be compared and analyzed effectively. 

When connecting to data from multiple sources, you’ll often find that values are formatted inconsistently. A common issue is with dates, where some sources may use DD/MM/YY, and others use MM/DD/YY. 

Adverity has powerful data transformation and enrichment capabilities to automate the process of standardizing data and improving data consistency.

Timeliness

High-quality data is as fresh as possible and available to teams across the business when they need it. 

Adverity has a market-leading data fetch frequency, fetching data from each of your sources as often as every 15 minutes - unlocking the potential for near real-time decision-making. This gives your business the confidence that you are making the right decisions based on what is happening today, rather than what was happening weeks ago. 

 

 

Relevance

The final dimension of data quality is relevance - ensuring the data you collect is useful and relevant for the business. 

More than 67% of CMOs admit to being overwhelmed by marketing data. The data consolidation process gives you the opportunity to cut through the noise and only focus on the data that’s important.

As an example, assume you’re working across 20 different marketing platforms. Each platform is going to have unique fields in its reports that you probably never use for optimization and aren’t replicated in any other source. If they aren’t relevant and aren’t comparable, why bring them through? 

When you integrate your data with a solution like Adverity, you have complete control over which fields are loaded into your data destination. So, no more irrelevant data. Just high-quality data that you can use to inform business decision-making.

Data Integrity

Data integrity goes one step beyond data quality, referring to the quality assurance and trustworthiness of your data over its entire life cycle. 

Data quality is a key part of this, so in some respects, it might be useful to think of data quality as a subset of data integrity. 

However, data integrity also covers a number of other areas, such as making sure you have the right processes, systems, and controls in place to ensure your business fully trusts your data, that it meets all regulatory requirements, and that it’s protected from any unauthorized changes. 

Why is data integrity important?

Making decisions on compromised data can lead to missed opportunities and costly, misguided decisions. 

That’s why it’s so important to make sure you have the right systems and controls in place to ensure data integrity so your business can make decisions from your data with confidence. 

The processes for ensuring data integrity are commonly included as part of a data governance strategy, and this is another area where Adverity has a number of features that can help. 

 

Control who can make changes to your data feeds

The Authorization Center in Adverity helps you control who can make adjustments to your data feeds, so there is no unexpected or unauthorized data being sent to your consolidated database.  

This can provide your business with peace of mind over the integrity of your data and is a key part of achieving successful data governance. 

Control who has access to sensitive data

Controlling access to sensitive data is important for any business - but it’s essential if your organization is managing personally identifiable information (PII). Unauthorized access to PII can lead to data misuse and non-compliance with data privacy regulations. 

Adverity has a range of data security features that enable you to control who can access different data sets within your business. The ability to manage user permissions provides a seamless and scalable way to manage access privileges across even the largest organizations. 

Monitor the performance and status of your different data feeds

The reliable performance of each of your different data streams is crucial for maintaining data integrity. Any inconsistencies or delays in the integration of your data can lead to inaccurate reporting and erroneous decision-making. 

Within Adverity’s Activity Monitor is a dedicated performance management tool, that provides detailed insights into data stream performance - alerting you to errors or integration tasks that take longer than they should. 

This enables businesses to proactively resolve any performance issues, ensuring smooth and efficient data integration that helps you achieve data integrity. 

 

Compliance with data privacy regulations like GDPR is essential for data integrity.

Compliance with data privacy regulations like GDPR is essential for data integrity.
 

 

Choose tools that are compliant with regulations

Compliance with data privacy regulations like GDPR is essential for data integrity. 

If your business is found to be non-compliant, it can lead to legal issues, financial penalties, and damage to your business reputation. 

That’s why it’s not worth the risk of manual data integration or choosing tools that aren’t compliant. 

Adverity complies with all relevant data privacy legislation and is ISO 27001 certified, which is an international standard for the implementation and maintenance of Information Security Management Systems. With Adverity, you can have complete peace of mind over the compliance and security of your data. 

Data integrity Vs. data quality: The differences summarized

We started this article with a commitment to explain the difference between data integrity and data quality. 

Hopefully, the detail and examples we’ve gone into have helped you gain an in-depth understanding - but let’s have a quick recap. 

Data quality is all about how far your data is fit for purpose. It’s about taking steps to make sure your data is accurate, complete, consistent, timely, and relevant. 

Data integrity is more about how well your data can be trusted throughout its lifecycle, looking at things like the processes, systems, and standards that are implemented for your data. 

You can’t have data integrity without data quality - and it’s important to make sure you’re taking the right action to address both as part of a robust data governance strategy. 

Improve your data quality and data integrity with Adverity

High-quality data is critical for effective decision-making within any organization. 

Adverity is a marketing-leading data integration platform that helps you ensure both the quality and integrity of your data.

From powerful data transformation capabilities to data integration, access controls, data stream monitoring, and data security features - Adverity has all the functionality you need to implement a robust and effective data governance strategy. 

Ready to find out more? Book a demo and see how Adverity can take your data governance to the next level. 

Make insights-driven decisions faster and easier!

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