By Bob Deakin
Part II in the Data Integrity Series
One of an organization’s most valuable assets is data, but it must be reliable and usable. Otherwise, it’s only in the way. Inevitably, the discussion of data integrity and data quality follows. So, what’s the difference between data quality and data integrity?
What Is Data Quality?
Quality refers to reliability, accuracy, and consistency. Integrity represents the dataset’s totality, correctness, and trustworthiness over its life cycle. Integrity goes beyond quality. It ensures context and maintains standards governing how data is accessed, entered, edited, stored, and utilized.
A mail carrier analogy from Data reliability company Monte Carlo applies. Data quality checks that it is the correct letter, clear and understandable, arrives exactly when needed, and follows a consistent format. Data integrity ensures the letter gets from point A to point B with no content changed.
Maintaining the highest data integrity standards protects against unauthorized access or modification. Also, it ensures GDPR compliance and increases the speed and ease of data recovery in the event of unplanned downtime.
What Is Data Integrity?
“Data integrity is similar to quality control in traditional product-oriented businesses and ensures the raw material is correct, secure and appropriate for its intended use,” says IBM contributors Tim Mucci and Cole Stryker in What Is Data Integrity? “The adage “garbage in, garbage out” is highly relevant when using data to inform sound business decisions, treat customers fairly and correctly, and facilitate accurate businesses reports that comply with industry regulations. Bad data, once operationalized, leads to undesirable outcomes.”
The five most important factors of data integrity are most commonly named are:
Compliance with General Data Protection Regulation (GDPR) regulations.
Improved Decisions by organizations as a result of accurate information.
Operational Efficiency due to the reduction of errors and resulting time and costs.
Security in preventing unauthorized access to your data.
Trust in your organization by customers, partners and the team.
How Is It Secure?
Data security is another component of data integrity. Simply put, it encompasses the collective measures used to protect a database. Efficient data integrity teams accurately and consistently test and validate data to assure efficiency, reliability, and security.
The Difference Between Data Quality and Data Integrity
Software company BMC offers the following graphic differentiating data integrity from quality:
“Data quality is directly linked to the quality of decision-making,” says Melody Chien, Senior Director Analyst at Gartner. “Good quality data provides better leads, better understanding of customers, and better customer relationships. Quality is a competitive advantage that D&A leaders need to improve upon continuously.”
Quality of data is about how well data serves its purpose. It includes accuracy, completeness, consistency, timeliness, and relevance. Data integrity keeps it in check, protecting against access without authorization.
Clean, Accurate and Disparate Data
Many organizations possess an abundance of data without realizing what they have. It may even be a hindrance until a partner can turn it into an asset. Clean, accurate, and disparate data working within one ecosystem is a phrase we invoke to describe our use of data at scale. Your data is your history and tells your story. It is an asset waiting to happen.
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