Data Integrity Vs. Data Quality: Know the Difference?


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Possessing high-quality data became one of the most valuable things a company can have. Data is now an asset, and for some even a commodity, that can define the value of the company and be the main reason why they survive and thrive in competitive markets.
The definition of high-quality data may be different for each company and depend mainly on what they use their data for, but for most, it can be defined by a few variables, like its dimensions, accuracy, completeness, consistency, validity, uniqueness, and integrity.

Data Integrity Vs. Data Quality: Know the Difference

Those variables combined are what ensure that the company will make the right decisions, create accurate business plans, grow, gain revenue and competitive share against its competitors, as data will only be valuable if it can be trusted. Incorrect data leads to bad insights, wrong analysis and poor decision-making.

That’s why the need for accurate data made the terms data quality and data integrity so popular, both are used to describe the data condition. And although those terms are often used together, there are differences between them, and the companies that want to have precise, reliable, accurate and consistent data need to understand their differences to apply these processes correctly to ensure correct enterprise data processing.

Understanding Data Quality

Data quality is all about data reliability. It refers to the characteristics that will determine if the information is adequate to serve its purpose to inform and help in planning and decision-making. It’s what shows if the data can be used for its specific purpose.

To define if the data is reliable and data quality is present, a few key questions need to be asked:

  • Is the data complete? If the data on the hands of the company is the total or a large percentage of the total data required.
  • Is the data unique? The data stored have to be unique and free of redundant or extraneous information.
  • Is the data valid? The information on the data must serve the syntax and structure defined by the company according to their requirements.
  • Is data timely? When was it sourced? Past data may not help companies to make fast decisions and understand their market. For the data to be fully valuable, it needs to be as up-to-date as possible.
  • How consistent is the data? All the data stored has to be kept to the same standards.

Quality data evaluation must be based on the principles above to see how complete, unique, valid, timely and consistent the data is, lacking one of them would be enough to compromise any data-driven decision or initiative.

But simply having high-quality data is not what will ensure useful information to companies. For example, having access to accurate information on a customer name and address will probably not be useful to the company if they don’t have any context about this customer behavior. So that’s where data integrity is useful and can normally be used combined with data quality.

Understanding Data Integrity

If data quality is what ensures that the data is accurate and reliable, data integrity goes a bit further to ensure that data will also be complete, consistent and in context. Data integrity is the process made to guarantee that the data is useful.

There are also main pillars that companies need to keep in mind to achieve data integrity:

  • Integration: It doesn’t matter the source or if it’s stored on legacy systems, relational databases, or cloud data warehouses, the data must be integrated, so the company can see everything quickly and in a singular view.
  • Quality: Before being useful, the data has to be complete, unique, valid, timely and consistent.
  • Location Intelligence: Adding location insight and analytics will make the data more complex and useful.
  • Enrichment: To make the data richer, it’s important to add context, nuance and meaning with external sources of information. Companies can add business, consumer or location information from those external sources to contextualize the data and make their insights and analysis more powerful.

So basically, data integrity can be considered as a subset of data quality. And as organizations are leveraging data to support their decision-making processes in almost all strategic parts of the business, data quality should be just the first step, while data integrity is what will enrich the insights they can take from the data. Although data integrity and data quality are different processes, using them together will guarantee powerful data.


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