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Data Integrity – Special Journal Of Sociology
Data Integrity – Special Journal Of Sociology
1 Data Integrity – Special Journal Of Sociology
Special Journals Publisher SJP, the publisher of SpecialJournal of Sociology presents our policy position, understanding and best publishing practices in line with other big old names with regards to data integrity, our stakeholders should read and understand our guiding principles as it relates to all manuscripts and data published or we are going to publish in our database for sustainable development. We welcome all suggestions on how best we are to do our business of publishing to meet the 22nd Century research publication dynamics.
Our definition of data integrity
There are a lot of definitions depicting how different publishers see or understand data integrity. Special Journals Publisher (SJP) in the context of our publications tailored towards sustainable development within local and global dimension see data integrity as the degree of novelty and uniqueness of its data that is free from variables which challenge its originality, quality, and relevance. Thus we define and score data as one with high integrity and with demonstrable high accuracy, reliability, stringently error-free, and without bias.
Two sides of data integrity: state of data or process
Data integrity may be seen as a state of data or process, and we can say that it is both legal and perfect. Legal data are data that do not contradict the dictates of professional bodies, associations, and most importantly the law of the land. Legal data does not attract any fine or tax upon publication and no one in the society is disallowed to touch, quote, or use it.
On the other hand, if we see data integrity as a process, then it describes the measures used to ensure the validity and accuracy of a data set or all data contained in a database. Data validation and accompanying protocols are optimized protocols that also adds to the quality and public acceptance of databases,
Checks and balances
There are so many quality control checks and balances designed to reduce errors and increase the integrity of data we publish in our database. Data quality assurance principles if piloted is of a great deal of importance in promoting data integrity published by a database. These integrity checks and balances are based on existing rules and regulations with little modification to fit into the objectives of projects at hand. These rules exist in any discipline established overtime to stabilize the practice or corporate utilization of such rules for the ultimate benefit of the stakeholders
Data transformation and coding for integrity
The importance of data integrity is also obvious when creating relationships between dissimilar data elements before being stored in a database. These make retrieval easy and certain without data loss or compromise. The concept of data integrity ensures that the data being transformed and transferred from one stage of data management to another is accurate and stringently free of error so that when the information is stored in the database, its value and reliability is inferred regardless of the duration for which it is stored or the frequency of access.
Data integrity and safety concerns
Data integrity also encompasses safety concern measures that publishers take to protect data from misuse, piracy, and adulteration. Data integrity is preserved when publishers take time to decide which data is available to who and which data is not available or accessible to who. Therefore any technology that will help preserve the security of data to achieve the ultimate good the technology was designed for would be a step in a positive direction.
This is very serious because success in hacking or breaking into a database without authorization may lead to data misuse and this can affect any organization negatively to the extent of threatening the existence of such an organization. The end-goal of data security is to protect your data from external or internal breaches.
Data integrity and quality concepts
Every organization has a standard and quality of data storage and retrieval. This impacts the standard and defines the volume of traffic the database draws. Data quality concepts ensure that the data stored in an organizational database is compliant with the organization’s standards and requirements.
In doing so, it applies a set of rules on a specific or complete dataset, and stores it in the target database. On the other hand, data integrity deals with the accuracy and completeness of data present in the database. Data integrity covers all aspects of data quality and advances further by executing several rules and procedures that oversee how information is entered, deposited, transmitted, and many more.
The physical integrity of data is protected against external factors, such as natural disasters, power outages, or hackers. Moreover, human faults, storage attrition, and several other problems can also make it unmanageable for data operators to obtain information from a database. The print database is subject to external forces and interference to ensure that stored data are not retrieved when needed.
Stored data that are not retrievable are useless and definitely will drawback advances by several decades instead of advancing it. Special journals Publisher recently deals with online media and may have no big problems with Physical data integrity
Entity integrity is a logical data integrity type that depends on the grouping of data items using defined codes, and associated passwords to help systematic arrangement and storage of datasets so that retrieval of such data will not be an issue. This is because human errors and technical difficulties, contribute to major systemic challenges in database management. The purpose is to make sure that data is very unique and not recorded several times. Entity integrity stores data in a tabular format, which can be interconnected and used in a range of ways.
Referential data integrity is a logical data integrity type that defines a series of procedures that encourages data managers to store data in a proper and reliable way that makes retrieval and usage very easy. In referential data integrity, data managers make sure that only the required alterations, additions, or removals happen via rules implanted into the database’s structure about the way foreign keys and passwords are used to access data.
These rules might include conditions that remove duplicate data records, warrant that data is precise, and/or prohibit recording data that is not suitable or out of scope with database guidelines.
Domain integrity is a logical data integrity type that we can define as the ability to identify a defined area of specialization and outline how datasets are stored to reflect such identified domain. Again rules and regulations are designed and set out guides that preserve and protect the quality of such data from external uninvited interferences. An assortment of procedures is therefore designed to ensure the precision of every data item is maintained in such a domain.
Domain integrity encompasses rules and other processes that restrict the format, type, and volume of data recorded in a database. It ensures that every column in a relational database is in a defined domain.
User-defined data integrity is a logical data integrity type that comprises of the rules defined by the operator to fulfill their specific requirements in dataset management. Entity, referential, and domain integrity are not enough to refine and secure data. Particular rules must be considered and integrated into data integrity processes to meet standards.
Factors Affecting Data Integrity
Entering or managing data manually increases the chances of errors, duplications, or deletion. Often, the entered data fails to follow the apt protocol or the errors in the manual entry can extend to the execution of processes, hence corrupting the results. All these issues put data integrity at risk. Computer systems and high technology software’s are now available to check for accuracy at the point of data entry so as to remove or reduce error even reduce such data are stored
Transfer of Errors
A transfer of error occurs if the data is not successfully transferred from one site within a database to another. These errors usually occur when a data item exists in the target table but is absent from the source table within a relational database. Again the computerized data management systems take care of any incongruity eliminating the multiplicity effects in data management
Bugs and Viruses
Your data’s integrity can also be compromised due to spyware, malware, and viruses invading a computer and altering, deleting, or stealing data. Again there is a lot of antiviruses and antimalware’s that can be installed to take care of these
Significance of data integrity
- Data integrity ensures that it can be retrieved when and where needed.
- It can also be seen as widely available and can be traced back to the original
- Protecting the validity and accuracy of data also increases stability and performance
- The ability to reuse and maintain data adds to its integrity as well.
- For data to be complete, its rules, relationships, dates, definitions, and lineage must be accurate
- Ensures consistency in the data model, value, and types before and after storage and retrieval
- Ensure that the data stored in a database can be found and linked to other data
- External data back up guarantees that such an entire data set can be recovered in database accident
- It strengthens the stability of data, offers optimum performance, and makes it reusable and maintained easily.
Data Integrity best practices
- Data backup and duplication is critical for ensuring data integrity.
- input validation to preclude the entering of invalid data,
- error detection/data validation to identify errors in data transmission, and
- Security measures such as data loss prevention, access control, data encryption, and more.
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