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

Authors Photo Precisely Editor | September 14, 2020

Data can be your organization’s most valuable asset, but only if it’s data you can trust. When companies work with data that is untrustworthy for any reason, it can result in incorrect insights, skewed analysis, and reckless recommendations.  

Two terms can be used to describe the condition of data: data integrity and data quality. These two terms are often used interchangeably, but there are important distinctions. Any company working to maximize the accuracy, consistency, and context of their data to make better decisions for their business needs to understand the difference.

Data Quality

Data quality refers to the reliability of data. Data quality is an essential subset of data integrity. If data is to be considered as having quality, it must be:

  • Complete: The data present is a large percentage of the total amount of data needed.
  • Unique: Unique datasets are free of redundant or extraneous entries.
  • Valid:  Data conforms to the syntax and structure defined by the business requirements.
  • Timely:  Data is sufficiently up to date for its intended use. 
  • Consistent: Data is consistently represented in a standard way throughout the dataset.

Quality data must meet all these criteria. If it is lacking in just one way, it could compromise any data-driven initiative.

However, simply having high-quality data does not, of itself, ensure that an organization will find it useful. For instance, you may have a database of customer names and addresses that is accurate and valid, but if you do not also have supporting data that gives you context about those customers and their relationship to your company, that database is not as useful as it could be. That is where data integrity comes into play. 

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Data Integrity

While data quality refers to whether data is reliable and accurate, data integrity goes beyond data quality. Data integrity requires that data be complete, accurate, consistent, and in context. Data integrity is what makes the data actually useful to its owner. (Related: What is Data Integrity?)

Obviously, data quality is a component of data integrity, but it is not the only component. Data integrity is based on four main pillars:

  • Data integration: Regardless of its original source, on legacy systems, relational databases, or cloud data warehouses, data must be seamlessly integrated in order to gain visibility into all your data in a timely fashion.
  • Data qualityData must be complete, unique, valid, timely, and consistent in order to be useful for decision making. 
  • Location intelligence: Make data more actionable by adding a layer of richness and complexity to it with location insight and analytics. 
  • Data enrichment: Add context, nuance, and meaning to internal data by enriching it with data from external sources. Adding business, consumer, or location information gives you a more complete and contextualized view of your data for more powerful analysis. 

The bottom line

Data is a strategic corporate asset, and both data quality and data integrity are essential for organizations looking to make data-driven decisions. Data quality is a good starting point, but data integrity elevates data’s level of usefulness to an organization and ultimately drives better business decisions.

To begin your journey to data integrity, you may first need to address issues of data quality. Companies that make a proactive effort to fix data quality issues and prevent future ones see better outcomes from all their data-driven initiatives.

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