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What’s the difference between Data Quality and Data Integrity?

Author: Rivka Kyron
by Rivka Kyron
Posted: Feb 24, 2019

Data is the root of your business, helping you to maintain efficiency in your internal process. However, when we talk about data- quality and integrity stuck our mind, which is important for every organization to stay on top. But do you actually know the meaning of Data Quality and Data Integrity? Well, many businesses misuse these terms by replacing it with each other. In reality, both the terms are quite different and have their unique definition.

Data Quality vs. Data Integrity

According to Wikipedia, Data quality refers to the condition of a set of values of qualitative or quantitative variables. Data quality is usually considered high quality if it fits for its intended uses in business operations, decision making and planning. On the other hand, Data integrity is the maintenance of, and the assurance of the accuracy and consistency of data over its entire life-cycle, which is a critical aspect to the design, implementation and usage of any system that stores, processes or retrieves data.

So we can say that Data Integrity is a subset of Data Quality, which means if you lack in data quality you will never have data integrity. Because data integrity develops only the basis that data quality provides, the resulting data integrity is what takes any business to the next level.

Your organization cannot improve its processes for better business outcomes just by putting efforts in data cleansing, until you don’t understand the difference between these two. Try to put your efforts in the following to attain success:

Data Quality-

  • Data Dictionary- Helps to create and maintain most critical data
  • Data Cleansing- Make sure all the types and formats are according to Data dictionary
  • Data Completion- This process will help you detect missing data and replace with absolute values
  • Reviews and Audits- Do this periodically to spot discrepancies

Data Integrity-

  • Scientific analysis- Implement mathematical or statistical analysis of data
  • Systems analysis- Thoroughly analyze performance of systems processing data
  • Code analysis- Changes in execution methods can emerge new trends and patterns
  • Architectural design- Ensures reliability of data transference
  • Organizational structure- Maintains credentials and authorizations across departments

You can assume things through effort list that how data quality and data integrity intersect each other. Further, it is your understanding how you take these things and implement in your business accordingly. If you don’t want to go through such painful procedure, then opting for data management services is an ideal way to alleviate your burden and stay focused on core activities, i.e., business growth.

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Author: Rivka Kyron

Rivka Kyron

Member since: Feb 21, 2019
Published articles: 1