The emergence of blockchain technology and its subsequent spike in popularity over the past decade has left a deep imprint on the tech scene, as companies and industry giants seem poised to uncover new and innovative ways blockchain can be put to use. Besides the obvious technological ramifications, blockchain also brought a series of subtle changes that can be somewhat easy to overlook.
Concepts such as data integrity and data immutability have existed in the vocabulary of developers and technology experts long before blockchain was even a thing. But as this technology got more and more traction, the terminology associated with blockchain has slowly permeated into the discourse of entrepreneurs and businessmen who try to market and sell their new blockchain-based services or products.
The reality is that in their attempt to highlight the value and the benefits of their technological offering, the target audience is usually left scratching its head when bombarded with unfamiliar tech concepts. One of these buzzwords that strongly gravitate around everything related to blockchain technology is data integrity.
What is data integrity
Data integrity is an essential component of information security that measures the overall accuracy, completeness and consistency of data throughout its life cycle.
The concept of data integrity can be used to describe a state, a process or a function. As a state, data integrity measures the authenticity and consistency of information. As a process, data integrity determines if the information has remained unaltered after it has transited to a new location or after it has been utilized in various operations. Lastly, when viewed as a function, data integrity is closely related to security, namely processes and procedures that maintain information in the same state it was introduced in the system.
Data integrity is commonly imposed through standard protocols and guidelines during the designing stage of a database, data warehouse or any other type of data storage medium. It is conserved through multiple error-checking validation procedures, rules, and principles based on a predefined set of business rules. When evaluating data integrity the following metrics are taken into consideration: accessibility, authenticity, completeness and transparency.
Furthermore, depending on the sphere of activity a company activates in, data integrity also calls for ensuring compliance with international regulations that focus on the storage management and processing of sensitive data. The compliant status is achieved by following a series of protocols, guidelines and criteria stipulated in the body of legislation such as the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR) and the Payment Card Industry Data Security Standard (PCI DSS). Failure to comply with international regulations attracts considerable financial fines.
The four principles of data integrity
Integration – regardless of its source, data has to incorporate seamlessly into on-prem databases, data centres, warehouses, cloud storage environments, as well as legacy systems
Quality – in order to act as a strategic business asset, data needs to be accurate, valid, reliable, timely, relevant and complete
Location-intelligence – ensures the accurate identification of location-based data by companies
Enrichment – in a data-driven economy, data is actively used as a means of getting a competitive edge in the business worlds. By consolidating data from multiple sources, companies get access to a more contextualized view for the decision-making process, setting a stronger foundation for future strategies
Why is data integrity relevant?
In our increasingly digital world, data has cemented itself as the lifeblood of the enterprise and business sector as it acts as a foundation for new decisions and strategies, as well as an integral component in day to day operations. Paired together with big data analytics, now more than ever, data in its raw form can be processed and refined to uncover new trends and business opportunities, underlining how companies can better isolate and target consumer needs.
The explosive rate of development of the technology sector has further stimulated the amount of data companies can collect, manage and utilize which often means that the integrity of that data is often relegated to the sidelines. But low quality, incomplete or corrupted data presents little to no value to businesses and enterprises.
Given this context, it becomes increasingly challenging to preserve data integrity while it is gathered, especially if we take into consideration data transfers between different departments and business partners. By prioritizing data integrity, companies can minimize downtime, improve the flow of operations, and unlock new business opportunities and momentum based on accurate data analysis.
The concept of database integrity goes hand in hand with data integrity, as both of them focus on understanding the health and maintenance of digital information. The only difference between the two concepts stems from where the data is located. When talking about data integrity in general, we are referring to information sent by a system or an application.
In contrast, database integrity is concerned with information that resides within a database system. Even so, in both cases, the senders and the recipients of data need to be concerned with the consistency and accuracy of the information that propagates, as there is always a risk for data to inadvertent or maliciously be corrupted or compromised during this process.
When designing a database system, it is highly recommended for companies to take into consideration data integrity practices and procedures to deter and even circumvent any future data-related issues. In an enterprise environment, database systems are generally multi-user which means that many employees and different departments have access and work at different time intervals. This does however pose a series of challenges because it creates a lot more room for mistakes, be it logical, human or system errors that may result in corrupted data. For example, an employee might mistakenly enter a social security number in the wrong field, or insert a letter by accident in a column destined only for phone numbers. With the right data integrity implementations set in place, these types of errors would be automatically rejected by the system.
Types of data integrity
Data integrity can be separated into two categories: physical and logical integrity. Both are a collection of processes, procedures and methods that reinforce data integrity in database systems.
Physical integrity is concerned with the challenges associated with storing and retrieving data properly. As the name implies, physical integrity is often associated with external factors such as natural disasters, power outages, and external tampering from hackers. In addition, human errors, hardware malfunctions and several other factors can make data access and processing impossible. A good starting point for ensuring the physical integrity of your data is to connect your system to an uninterrupted power supply or backup generators and the inclusion of redundant hardware to take over the workload in case of a failover scenario.
Logical integrity is concerned with the correctness or rationality of data. In more simple terms, it keeps data unchanged while it is utilized in various operations and processes. Logical integrity challenges can range from software bugs to design flaws, sometimes overlapping with physical integrity challenges such as human errors and tampering from hackers, but it addresses them differently.
Logical integrity can be split into four categories:
- Entity integrity ensures that each row from a database table is unique. Entity integrity is an essential feature of relational databases that store data in tables that can be interconnected and used in a variety of ways
- Referential integrity encompasses a series of procedures that guarantee that data is stored and used uniformly. A set of rules encapsulated in the design of the database structure ensures that only acceptable changes, additions or deletions of data take place. The rules set in place can exclude data duplicates, guarantee data accuracy and reject incompatible data entries
- Domain integrity is a collection of methods and techniques that guarantee the accuracy of data in a domain. A domain represents a set of suitable values that a column is allowed to contain. Domains can be customized to include restrictions that limit the format, type and volume of data that can be entered
- User-defined integrity allows users to create custom rules tailored to follow the business logic of their company. It is usually employed when the other three types of data integrity aren’t enough to safeguard data.
What data integrity isn’t?
Data integrity vs data security
Although data security plays an important role in successfully achieving data integrity, the two concepts shouldn’t be confused. Data security is an assortment of measures, methods and mechanisms employed to protect information against corruption or unwanted access, while data integrity is concerned with keeping information intact and accurate throughout its life cycle.
Considered one of the many facets of data integrity, data security isn’t expansive enough to encompass the myriad of processes required to maintain data intact for long periods of time.
Even so, data security is a pivotal element for modern enterprises that incorporates systems and procedures aimed at keeping data inaccessible to malicious actors who may use it in harmful or unintended ways.
Data integrity vs data quality
Similar to data security, data quality is an integral component of data integrity. Data quality is a collection of measures and processes designed to make sure that the information introduced in a company’s database system is compliant with the organization’s requirements and standards. From a high-level overview, the concept of data integrity can be seen as an umbrella term that includes every aspect of data quality, further expanding the concept with additional rules that dictate how data is introduced, stored and transferred between different users.
The quality of data is determined by the following attributes:
- accuracy: data needs to be sufficiently accurate in order to be used for its intended purpose
- validity: the collection and usage of data is governed by a set of rules, standards and regulations that underline core principles. Validity helps ensures and maintains legality and business ethics while also maintaining consistency between different data sources
- reliability: data needs to demonstrate transparency all through its life cycle, from the moment it is collected, stored and processed
- timelessness: to circumvent any operational bottlenecks, data needs to be easily and quickly accessible. Once operations successfully conclude, data needs to be collected, stored and made accessible as soon as possible
- relevance: the content of the data stored needs to consistently provide valuable insight to the user’s are of interest
- completeness: data needs to be comprehensive and whole, in the sense that no gaps or missing data should occur as incomplete data is often unusable.
Data integrity risks
As the core element that springs in motion business operations, initiatives and decision making processes, data is exposed and susceptible to a wide range of factors that can affect its integrity:
- Human error – entering or maintaining data manually opens up a window of possible mistakes that can affect data integrity such as incorrectly entering information, duplicating or deleting data. Human errors can also stem from deviating from company protocols, mistakes during the implementation of procedures meant to safeguard said data. The human factor ranks among the most common threats to data integrity
- Transfer errors – data can get corrupted when it is in transit between different departments or users. Transfer errors can be a result of a power outage at an inopportune moment.
- Compromised hardware – malfunctioning computer terminals, servers or faulty power supplies can have a negative impact on data integrity. Compromise hardware can make data incomplete or inaccessible
- Bugs, malware, spyware or viruses – cybercriminals rely on malicious software to alter, delete or steal data from companies. Ransomware is a type of malware designed to lock users from their data, making it inaccessible unless they cooperate and pay a ransom to the attackers. The advent of Ransomware as a Service (RaaS) has made this type of software available to prospective cybercriminals, leading to a significant increase in ransomware occurrences
- Deviation from international regulation and legislation – failure to comply with data protection legislation like HIPAA, GDPR and PCI DSS make companies liable to considerable fines with the risk of prosecution
- Security breaches – data security is closely linked to data integrity. Unauthorized access to company data can lead to data corruption, hijacking, and costly data breaches that can significantly damage a business
- Unreliable data – an umbrella term that encompasses incomplete and inaccurate data that offers a truncated business perspective. Utilizing unreliable data is a dangerous gamble which in most cases leads to additional operating expenses
Data integrity best practices
There isn’t a one fit all solution for implementing data integrity. Achieving integrity isn’t a straight forward process as each company has its own data requirements that reflect the nature of its business logic. Even so, by following a set of industry guidelines, and practices, organizations can slowly start to consolidate the integrity of their data.
- Encrypting valuable data – a component of data security, encryption is a process through which data is transformed from plain text to unintelligible ciphertext. Over the years encryption has proven to be a powerful instrument for data protection, as it can ensure the security of data in storage as well as in transit.
- Backing up critical data – a well thought data restoration plan as well as regular data backups can transform a potential disaster into an inconvenience or setback. For maximum effectiveness, it’s highly recommended to store backup data in an alternate location to prevent tampering. Recent cybersecurity attacks that also target data backup suggest that companies need to combine multiple approaches and processes to consolidate data security and integrity
- Imposing the principle of least privilege – employees should only have access to information that is necessary for completing their job attributions. Paired together with micro-segmentation and access control mechanisms, the principle of least privilege can prevent the lateral movement of an unauthorized individual in the system
- Input validation – applications and databases should verify and validate that the information introduced by the user is relevant and in an acceptable format
- Remove duplicate data – data duplicates pose a real threat to companies because unauthorized individuals can access and further distribute an omitted data duplicate
- Maintain an audit trail – the ability to pinpoint the exact origin of a piece of data, who accessed it and when it was accessed provides the transparency necessary to immediately identify the source of a problem
- Enforcing quality control procedures – users from every department should have a common set of procedures and guidelines that ensure a common standard for maintaining data compliance and confidentiality
- Strong passwords and Multi-Factor Authentication (MFA) – your data is as secure as your password, as such it is highly advised that users change passwords frequently and use strong passwords that utilize uppercase and lowercase letters, numbers and special symbols. Under no circumstances should users store their passwords in plaintext in a document. MFA complements traditional password-based security systems by introducing more variables to the authentication process, making it harder for potential attackers to get their hand on user credentials
- Conduct employee data security training – employees should receive regular data security and threat assessment training. Contrary to the common belief, a large segment of cybersecurity attacks are not oriented towards the IT systems themselves, but on the people that operate them. Credentials stolen by phishing attacks and other social engineering techniques are the most common data security threats faced by companies.
Modex Blockchain Database strengthens data integrity
Modex BCDB is a middleware software solution that combines the functionality and familiarity of traditional database systems with blockchain, a technology designed to facilitate unparalleled levels of data integrity. Bundled as an Infrastructure as a Service offering, Modex BCDB is devised to act as a building block that companies can use to build an infrastructure tailored to their specific business requirements.
What makes the Modex technological layer stand out is the fact that it incorporates a blockchain component that unlocks a series of powerful features and functionalities like data integrity, decentralization, transparency, distribution and data immutability for their most valuable asset, their data.
Available on the Microsoft Azure Marketplace, Modex BCDB can be easily deployed in cloud environments as well as on on-prem infrastructures. With a modular and agnostic approach to its two core components, the database engine and blockchain framework, companies can utilize what blockchain and what database is best suited to answer their data-related needs. Organizations can use the Blockchain Database solution to build a new infrastructure for their business or complement their existing IT framework to unlock a slew of data security benefits.