Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It is an absolute necessity for reliable decision-making for businesses in today’s digital age. While it’s easy to define data integrity, it’s difficult to ensure it.
Maintaining data integrity is not merely a best practice but a fundamental necessity. It ensures that the information businesses rely on for critical decisions remains trustworthy and free from errors, manipulation, or unauthorized access. Moreover, data integrity intersects closely with other vital areas such as data quality and data security. Without taking robust measures to uphold these standards, businesses risk facing financial losses, missed opportunities, and irreparable damage to their reputation.
Hedera is on the cutting edge of data integrity, using its decentralized ledger technology and Hedera to enhance data integrity. For one example, Starling Lab, a joint project between the USC Shoah Foundation and Stanford’s Department of Electrical Engineering, uses Hedera’s tools to ensure the integrity of digital media information.
In this article, we’ll dive into:
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The types of data integrity.
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The issues that threaten it.
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How distributed ledger technology (DLT) enhances data integrity for businesses to thrive in the digital age.
Types of data integrity
Preserving data integrity requires meticulous collection processes and robust storage protection mechanisms. There are two fundamental types of data integrity: physical integrity and logical integrity. Both are essential to maintaining data accuracy, consistency, and reliability throughout its lifecycle.
Physical integrity
Physical data integrity refers to the safety of physical facilities storing data. This involves protecting data from risks such as natural disasters or power outages. Failure to set up protection mechanisms for physical storage facilities or to adequately back up data poses a significant risk. It could lead to the permanent loss of valuable information.
Logical integrity
Logical integrity ensures that data remains consistent and unaltered throughout its lifecycle. This involves preventing unauthorized changes or misrepresentations as different parties interact with the data. Logical integrity can be compromised by human error, transfer errors, and malicious intent by individuals seeking to manipulate the data.
Challenges to data integrity
Ensuring data integrity involves facing various threats that can undermine the reliability and accuracy of data. By understanding these challenges, businesses can strengthen their data management strategies and safeguard against potential threats.
In this section, we explore different ways in which data integrity can be compromised, ranging from inadvertent human errors to deliberate malicious actions:
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Human error. Data quality can be drastically damaged by the types of mistakes that individuals are prone to. Committing a transfer error, deleting rows in a spreadsheet, misunderstanding a report while entering data, and putting a decimal point in the wrong spot are just a few examples of how human error can compromise data integrity.
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Formatting errors. As information is moved from one system to another, differences in formatting may lead to changes in the data items that affect data integrity.
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Data breaches and cybersecurity threats. If unauthorized parties can access your data, they can change it without anyone knowing. This ranges from cybersecurity threats, like malware and hacking attempts, to disgruntled employees with proper access.
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Hardware issues. If an organization relies entirely on physical hardware to store data, it runs the risk of the hardware failing and compromising integrity.
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Data collection errors. Collecting complete data is one of the easiest ways an organization can ensure data integrity. Incomplete data samples can skew your analysis and may lead to bias.
How to maintain data integrity
Without reliable data, the foundation of decision-making crumbles, leading to potential financial losses and reputational damage. To uphold accuracy and reliability, businesses can adopt a series of data integrity best practices, each playing a crucial role in safeguarding data:
Input validation
One of the main methods for maintaining data integrity is input validation. This process involves verifying the accuracy, completeness, and consistency of data entered into a system. By implementing robust input validation mechanisms, businesses can mitigate the risk of erroneous or fraudulent data infiltrating their databases.
Data governance policies
Data governance policies establish guidelines and procedures for data management. These policies outline the responsibilities of stakeholders, define data standards, and spell out compliance with regulatory requirements. By enforcing strict data governance policies, businesses can foster a culture of accountability and transparency for data integrity.
Data backups and recovery
Data backups and recovery mechanisms are indispensable for safeguarding against data loss and corruption. Regularly backing up data ensures that even in the event of hardware failures, natural disasters, or cyberattacks, businesses can recover their data with minimal disruption. Additionally, implementing robust data recovery processes allows organizations to quickly restore data to its original state, preserving its integrity and reliability.
Protecting data integrity with DLTs
Data security and data integrity are closely related: security measures are the main line of defense to help prevent data from being compromised. Data security methods such as access controls and encryption can help prevent unauthorized access to systems and data, thereby protecting data integrity.
DLTs are well-suited to protecting data because of their encrypted, immutable nature. A distributed ledger technology (DLT) solution involves a database that’s shared and duplicated across a network of computers in different locations. This makes DLTs an outstanding tool for ensuring data integrity and strengthening security.
Real-world data integrity use cases on Hedera
EQTY Lab
EQTY Lab provides enterprises with a hardware-based solution to govern and audit AI workflows. Developed in collaboration with Intel and NVIDIA, the Verifiable Compute framework creates cryptographic certificates that prove the integrity, lineage, and compliance of AI models and agents throughout their lifecycle.
The platform uses Hedera Consensus Service to anchor attestations from trusted execution environments, creating an immutable audit trail for every AI computation. This enables organizations to meet emerging regulations like the EU AI Act while maintaining full transparency over their AI supply chain—from training data to deployment.
Hyundai/KIA
Hyundai Motor Company and Kia Corporation developed the Integrated Greenhouse Gas Information System (IGIS) to monitor, quantify, and manage carbon emissions across the entire vehicle lifecycle. The platform uses Life Cycle Assessment methodology to track emissions from raw material procurement through manufacturing and distribution.
IGIS integrates with the Supplier CO2 Emission Monitoring System (SCEMS), which combines AI with the Hedera network to ensure data integrity and prevent tampering. By leveraging Hedera Consensus Service for secure and transparent emissions data, the companies can meet external certifications like CDP and RE100 while working toward their 2045 carbon neutrality goal.
MVC Global
MVC Global created a blockchain-based platform called the Track and Trace Program to address threats to the pharmaceutical supply chain. Complex supply chain systems can help lead to substandard drug quality and drug counterfeiting. AVC Global uses DLT to validate and record transactions across pharmaceutical supply chains.
Each drug is given a GS1-compliant serial number, which is recorded and verified by Hyperledger and HCS. The serial number is then scanned at each stage of the journey, with each transaction being verified through a non-corruptible, dual notary system.
Acoer
Acoer addresses drug overproduction and underproduction by tracking and logging pharmaceutical supply chain events from end to end. Acoer provides public data visualization tools, letting drug companies make informed production decisions.
Neuron
Neuron is building an open-source decentralized service network (DSN) for IoT communication and data exchange. Originally focused on drone tracking, the platform has expanded its vision to support a wide range of mobility solutions—including air taxis, autonomous vehicles, and ground robots—by connecting sensors, vehicles, and management systems.
Neuron uses Hedera Consensus Service to provide trusted timestamping and fair ordering for real-time data from IoT devices. Each device registers via smart contract and establishes verifiable communication channels, enabling service providers and customers to discover, connect, and transact securely. This peer-to-peer infrastructure eliminates centralized intermediaries, allowing data sellers to keep a larger share of revenues while maintaining tamper-proof records for regulatory compliance.
Data integrity with Hedera
Businesses need to maintain data integrity, but it is essential in many areas. Hedera’s work with Project Starling focuses on preserving the accurate presentation of history. Inaccurate data can lead to severe consequences for businesses and their customers. However, the value of preserving data integrity for historical photos and news accounts can’t be measured.
DLTs are a viable solution for many of the challenges to preserving data integrity. These immutable ledgers offer companies a secure way to store data without fearing physical integrity issues. Additionally, data can be automatically recorded and logged in a decentralized manner, eliminating human error, invalid data, and storage erosion.
Many data security projects turn to the Hedera network for its secure ledger technology and low, predictable fees.