What is data integrity?
At its core, data integrity is the reliability, accuracy, quality, and consistency of data over time and across systems. It includes measures aimed at preventing data corruption, unauthorized access, and data loss. A commitment to data integrity allows organizations to trust their data, leading to more confident insights and more well-informed decision-making.
Why is data integrity important?
The rise of data-driven organizations has placed an increased emphasis on the reliability and security of the data itself. Ensuring data integrity can help:
- Drive better decision-making. Reliable, accurate data can deliver more informed and valuable decisions, which can positively affect an organization’s bottom line. Conversely, incorrect or inconsistent data can lead decision-makers to draw erroneous conclusions, which may hamper business outcomes.
- Guarantee compliance. Specific industries can be subject to various compliance rules and regulations that require data accuracy and protection. Data integrity can help companies adhere to these requirements and avoid costly penalties or other legal issues.
- Strengthen customer relationships. Protecting sensitive data, such as personally identifiable information (PII), health records, or financial data, not only helps companies comply with various industry regulations, but it also secures customer privacy and can help build customer loyalty.
- Facilitate data integration. Many organizations use multiple systems to intake and manage their data. Solid data integrity can minimize errors and ensure that data is accurate and valid, so it can be used seamlessly and efficiently across systems.
- Enhance operational efficiency. Invalid or incomplete data can result in poor decision-making and business inefficiencies. Data integrity can help systems operate smoothly by limiting data inconsistencies, rework, and other related costs.
Types of data integrity
Various rules define how data can be inserted, deleted, and modified within hierarchical and relational databases. Two types of data integrity can be used to enforce these rules: physical integrity and logical integrity.
Physical integrity
Physical integrity is aimed at protecting data accuracy during storage and retrieval. This includes ensuring reliable power and storage sources and safeguards from threats like hacking, natural disasters, or storage erosion.
Logical integrity
Logical integrity ensures that data remains consistent and usable across various systems and guards against human error or system design issues. It employs four different methods:
- Domain integrity relates to specific values allowed to be stored within a table’s columns, such as dates, integers, or text strings. Each dataset or domain has certain rules that limit the format and type of data that can be entered. Domain integrity ensures that data stays within these domain constraints so it doesn’t cause an error, such as by trying to input a line of text into a date field.
- Referential integrity guarantees that records between tables in a relational database are uniform, unique, and cannot be duplicated. This prevents errors and ensures that data is entered and modified correctly. It also ensures a record cannot be null, since multiple null values would make them impossible to uniquely identify.
- Entity integrity employs primary keys and unique values to identify specific data within a table in a relational database. This prohibits data duplication by preventing the same data from being stored multiple times. Consider an online purchase for the same item at the same price. The addition of a distinct order number primary key distinguishes it from other orders and prevents fulfillment errors.
- User-identified integrity refers to data constraints that don’t fall within the areas of referential, domain, or entity integrity. Users can create these rules to ensure specific business requirements are enforced, such as requiring country information on an order to determine whether and how a product can be shipped.
The role of data integrity in different industries
Data integrity is critical for businesses across industries, ensuring the reliability, consistency, and accuracy of their data. Let's explore the role of data integrity in financial services, healthcare, and retail and e-commerce.
- Financial services. In the finance and banking sector, data integrity is essential for maintaining accurate financial records, transactional data, and customer information. It’s also crucial for detecting fraud, complying with regulatory requirements, and ensuring seamless financial transactions.
- Healthcare. Electronic health records, medical imaging, and clinical research data must be reliable and accessible to provide accurate diagnoses, personalize treatments, and support medical research. That makes data integrity crucial to ensuring patient safety, improving healthcare outcomes, and enabling efficient healthcare operations.
- Retail and e-commerce. Retailers and e-commerce businesses heavily rely on data integrity to enhance customer experiences and optimize business processes. Maintaining accurate inventory records, customer profiles, and transaction data is vital for effective supply chain management, personalized marketing campaigns, and seamless online shopping experiences.
Key data integrity risks
Human- and machine-related data integrity risks exist, and failing to recognize and account for them can affect everything from decision-making to compliance. Here are a few of the most common risk factors.
Data entry errors
When users manually enter data, they increase the risk for errors, inaccuracies, duplications, or other mistakes. This can lead to incomplete data and incorrect data analysis.
Malware, cyberattacks, and bugs
Malicious attacks or bugs can corrupt your data, steal it, make it inaccessible, alter it unknowingly, or even destroy it. This not only makes the data unreliable, but it can also expose sensitive customer information. Likewise, software bugs can corrupt the data, producing unexpected results.
Hardware issues
Unforeseen hardware failures, such as crashes or memory errors, can lead to data loss or corruption, making it unusable. Those issues can be amplified if a robust recovery plan isn’t in place.
Data migration errors
Migrating data between systems can result in transfer errors and corrupt or incomplete data. System incompatibilities, network interruptions, or other communication or storage issues can cause errors.
Poor quality controls
Subpar data quality procedures can also result in incomplete or inaccurate data storage and use, which can cause downstream errors and delays and may require costly, time-consuming fixes.
How to ensure data integrity
The best way to ensure data integrity in an organization is through a combination of threat prevention and stakeholder buy-in.
Validate and verify data
Ensure that entered or imported data is accurate and usable by implementing validation and verification checks. This is especially key when importing data from new or unknown sources, which could be susceptible to hacking or other risks.
Create an audit trail
Keep detailed audit trails and logs that detail when changes to the data were made, who made them, and what the changes included. Review logs regularly to identify unauthorized activity or usage.
Back up your data
It’s critical to back up your data regularly to protect against inadvertent data loss or resolve errors. Plus, you’ll want to develop a robust data recovery plan in case any information is accidentally deleted or corrupted.
Implement access controls
Create comprehensive rules around who can view, edit, and delete data, along with who has access to sensitive or business-critical information. Review permissions regularly to ensure the right users are accessing only the data they need—especially following organizational restructurings or team changes.
Create a data integrity culture
Data integrity is not the responsibility of one department or team. Make sure leaders communicate how everyone in the organization can play a role in ensuring it. That includes training employees on data integrity best practices and internal policies, so they can more quickly identify and solve data-related issues.
FAQs
What is the difference between data integrity and data quality?
Data quality pertains to the fitness of data for use, ensuring that it meets the intended purpose and is free from errors or inconsistencies. Data integrity also refers to the accuracy and consistency of data, while also ensuring it remains reliable over time, even as it travels among different systems and processes.
What is the difference between data integrity and data security?
While data integrity covers the data accuracy, reliability, and consistency, data security focuses on protecting data from unauthorized access, ensuring its confidentiality, integrity, and availability.
Teradata recognizes how important data integrity is to the success of businesses across industries. Our comprehensive suite of data management, data validation, and data harmonization solutions help organizations maintain accurate, reliable, high-quality data to enable faster, more confident decisions, safeguard sensitive information, and grow enterprises.
Teradata VantageCloud, the complete cloud analytics and data platform for AI, delivers best-in-class data integrity tools to protect your data, unlock innovation, and deliver value faster than ever before. Find out how VantageCloud can help you ensure the reliability, accuracy, and consistency of your data so you can drive better business decisions. Connect with us today.