What is Data Integrity and Why is it Important?

SHAIVI CHAPALGAONKAR | July 19, 2021 | 694 views

Data Integrity
In an era of big data, data health has become a pressing issue when more and more data is being stored and processed. Therefore, preserving the integrity of the collected data is becoming increasingly necessary. Understanding the fundamentals of data integrity and how it works is the first step in safeguarding the data.

Data integrity is essential for the smooth running of a company. If a company’s data is altered, deleted, or changed, and if there is no way of knowing how it can have significant impact on any data-driven business decisions.

Data integrity is the reliability and trustworthiness of data throughout its lifecycle. It is the overall accuracy, completeness, and consistency of data. It can be indicated by lack of alteration between two updates of a data record, which means data is unchanged or intact. Data integrity refers to the safety of data regarding regulatory compliance- like GDPR compliance- and security. A collection of processes, rules, and standards implemented during the design phase maintains the safety and security of data.

The information stored in the database will remain secure, complete, and reliable no matter how long it’s been stored; that’s when you know that the integrity of data is safe. A data integrity framework also ensures that no outside forces are harming this data.

This term of data integrity may refer to either the state or a process. As a state, the data integrity framework defines a data set that is valid and accurate. Whereas as a process, it describes measures used to ensure validity and accuracy of data set or all data contained in a database or a construct.

Data integrity can be enforced at both physical and logical levels. Let us understand the fundamentals of data integrity in detail:

Types of Data Integrity

There are two types of data integrity: physical and logical. They are collections of processes and methods that enforce data integrity in both hierarchical and relational databases.

Physical Integrity

Physical integrity protects the wholeness and accuracy of that data as it’s stored and retrieved. It refers to the process of storage and collection of data most accurately while maintaining the accuracy and reliability of data. The physical level of data integrity includes protecting data against different external forces like power cuts, data breaches, unexpected catastrophes, human-caused damages, and more.

Logical Integrity

Logical integrity keeps the data unchanged as it’s used in different ways in a relational database. Logical integrity checks data accuracy in a particular context. The logical integrity is compromised when errors from a human operator happen while entering data manually into the database. Other causes for compromised integrity of data include bugs, malware, and transferring data from one site within the database to another in the absence of some fields.

There are four types of logical integrity:

Entity Integrity
A database has columns, rows, and tables. These elements need to be as numerous as required for the data to be accurate, but no more than necessary. Entity integrity relies on the primary key, the unique values that identify pieces of data, making sure the data is listed just once and not more to avoid a null field in the table. The feature of relational systems that store data in tables can be linked and utilized in different ways.

Referential Integrity
Referential integrity means a series of processes that ensure storage and uniform use of data. The database structure has rules embedded into them about the usage of foreign keys and ensures only proper changes, additions, or deletions of data occur. These rules can include limitations eliminating duplicate data entry, accurate data guarantee, and disallowance of data entry that doesn’t apply. Foreign keys relate data that can be shared or null. For example, let’s take a data integrity example, employees that share the same work or work in the same department.

Domain Integrity
Domain Integrity can be defined as a collection of processes ensuring the accuracy of each piece of data in a domain. A domain is a set of acceptable values a column is allowed to contain. It includes constraints that limit the format, type, and amount of data entered. In domain integrity, all values and categories are set. All categories and values in a database are set, including the nulls.

User-Defined Integrity
This type of logical integrity involves the user's constraints and rules to fit their specific requirements. The data isn’t always secure with entity, referential, or domain integrity. For example, if an employer creates a column to input corrective actions of the employees, this data would fall under user-defined integrity.

Difference between Data Integrity and Data Security

Often, the terms data security and data integrity get muddled and are used interchangeably. As a result, the term is incorrectly substituted for data integrity, but each term has a significant meaning.

Data integrity and data security play an essential role in the success of each other. Data security means protecting data against unauthorized access or breach and is necessary to ensure data integrity.

Data integrity is the result of successful data security. However, the term only refers to the validity and accuracy of data rather than the actual act of protecting data. Data security is one of the many ways to maintain data integrity. Data security focuses on reducing the risk of leaking intellectual property, business documents, healthcare data, emails, trade secrets, and more. Some facets of data security tactics include permissions management, data classification, identity, access management, threat detection, and security analytics.

For modern enterprises, data integrity is necessary for accurate and efficient business processes and to make well-intentioned decisions. Data integrity is critical yet manageable for organizations today by backup and replication processes, database integrity constraints, validation processes, and other system protocols through varied data protection methods.

Threats to Data Integrity

Data integrity can be compromised by human error or any malicious acts. Accidental data alteration during the transfer from one device to another can be compromised. There is an assortment of factors that can affect the integrity of the data stored in databases. Following are a few of the examples:

Human Error

Data integrity is put in jeopardy when individuals enter information incorrectly, duplicate, or delete data, don’t follow the correct protocols, or make mistakes in implementing procedures to protect data.

Transfer Error

A transfer error occurs when data is incorrectly transferred from one location in a database to another. This error also happens when a piece of data is present in the destination table but not in the source table in a relational database.

Bugs and Viruses

Data can be stolen, altered, or deleted by spyware, malware, or any viruses.

Compromised Hardware

Hardware gets compromised when a computer crashes, a server gets down, or problems with any computer malfunctions. Data can be rendered incorrectly or incompletely, limit, or eliminate data access when hardware gets compromised.

Preserving Data Integrity

Companies make decisions based on data. If that data is compromised or incorrect, it could harm that company to a great extent. They routinely make data-driven business decisions, and without data integrity, those decisions can have a significant impact on the company’s goals.

The threats mentioned above highlight a part of data security that can help preserve data integrity. Minimize the risk to your organization by using the following checklist:

Validate Input

Require an input validation when your data set is supplied by a known or an unknown source (an end-user, another application, a malicious user, or any number of other sources). The data should be validated and verified to ensure the correct input.

Validate Data

Verifying data processes haven’t been corrupted is highly critical. Identify key specifications and attributes that are necessary for your organization before you validate the data.

Eliminate Duplicate Data

Sensitive data from a secure database can easily be found on a document, spreadsheet, email, or shared folders where employees can see it without proper access. Therefore, it is sensible to clean up stray data and remove duplicates.

Data Backup

Data backups are a critical process in addition to removing duplicates and ensuring data security. Permanent loss of data can be avoided by backing up all necessary information, and it goes a long way. Back up the data as much as possible as it is critical as organizations may get attacked by ransomware.

Access Control

Another vital data security practice is access control. Individuals in an organization with any wrong intent can harm the data. Implement a model where users who need access can get access is also a successful form of access control. Sensitive servers should be isolated and bolted to the floor, with individuals with an access key are allowed to use them.

Keep an Audit Trail

In case of a data breach, an audit trail will help you track down your source. In addition, it serves as breadcrumbs to locate and pinpoint the individual and origin of the breach.

Conclusion

Data collection was difficult not too long ago. It is no longer an issue these days. With the amount of data being collected these days, we must maintain the integrity of the data. Organizations can thus make data-driven decisions confidently and take the company ahead in a proper direction.

Frequently Asked Questions

What are integrity rules?

Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution.

What is a data integrity example?

Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are:

• When a user tries to enter a date outside an acceptable range
• When a user tries to enter a phone number in the wrong format
• When a bug in an application attempts to delete the wrong record

What are the principles of data integrity?

The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives.

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Infleqtion Unveils SupercheQ, a Quantum Advantage for Distributed Databases

Infleqtion | December 09, 2022

Infleqtion, the global quantum ecosystem leader, today unveiled SupercheQ: Quantum Advantage for Distributed Databases, a scientific advance that extends the power of quantum computation to new applications involving distributed data. The emergence of commercial quantum hardware has been accompanied by new approaches to benchmarking quantum computers. In addition to application-centric benchmarking approaches such as Infleqtion's SupermarQ suite, scientists have developed benchmarks based on sampling from random quantum circuits. These benchmarks, including Quantum Volume, have enabled effective cross-platform comparisons, but until now have been disconnected from specific applications of quantum computers. The launch of SupercheQ changes this by endowing these random circuit sampling experiments with their first application. "SupercheQ achieves an exponential advantage for one of the most fundamental tasks in distributed computing: checking if two files are identical. "We leveraged recent advances in quantum information theory to show that the same families of circuits behind quantum volume can be used to realize this advantage." Pranav Gokhale, Vice President of Quantum Software at Infleqtion Gokhale will present SupercheQ on December 8th at Q2B in Santa Clara. Q2B, the world's largest non-academic quantum industry conference, brings together over 1,000 attendees from commercial companies and research institutions from around the world. SupercheQ has been experimentally validated by execution on superconducting quantum hardware from IBM Quantum, which also pioneered the invention of the Quantum Volume benchmarking metric. "The development of SupercheQ is an exciting step forward that starts to connect the dots between quality as measured by Quantum Volume to applications," said Jay Gambetta, IBM Fellow and Vice President of IBM Quantum. "The experimental validation on IBM Quantum hardware demonstrates the need for reliable and available hardware to advance quantum and build this industry together." In addition to the experimental validation on quantum hardware, the team performed large-scale simulations by leveraging NVIDIA GPUs, as well as the cuQuantum software development kit. "The launch of SupercheQ expands the possibilities of the tasks and types of data that can be addressed by a quantum computer," said Tim Costa, Director of HPC and Quantum Computing Products at NVIDIA. "The team's use of NVIDIA GPUs and cuQuantum has enabled them to validate SupercheQ's practical value at the scale of future quantum computers with hundreds of qubits." SupercheQ has been integrated into SuperstaQ, Infleqtion's flagship cloud quantum software platform, and the technical details behind SupercheQ have now been released in an academic paper, "SupercheQ: Quantum Advantage for Distributed Databases." Customers can get started today with a qiskit-superstaq tutorial notebook. SupercheQ is also in a private release as a new benchmark available in the SupermarQ suite. The development of SupercheQ—which requires both quantum computers and quantum networks—originates in Infleqtion's platform approach, spanning multiple quantum technologies. "We believe that the greatest advances in quantum will arise at the intersection of quantum computing, sensing, networking, and clock technologies," said Paul Lipman, Infleqtion's President of Quantum Information Platforms. "SupercheQ is an exemplar of this approach." About Infleqtion Infleqtion is building an ecosystem of quantum technologies and commercial products for today, that will drive the company and the entire industry toward tomorrow. The company believes in taking quantum to its limit and leading from the edge. Infleqtion is built on 15 years of pioneering quantum research from ColdQuanta. Its scalable and versatile quantum technology is used by organizations around the globe and deployed by NASA on the International Space Station. Infleqtion is based in Boulder, CO, with offices in Chicago, IL; Madison, WI; Melbourne, AU and Oxford, UK.

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