Data Integrity and Why Is It So Important?

Data Integrity can be defined as it is critical part to the plan, execution & practice of system that processes, stores & or retrieves data and maintenance & assurance of data accuracy and consistency throughout its life cycle. Another best alternative term is Data quality which is sometimes defined as the proxy term of data […]


Data Integrity can be defined as it is critical part to the plan, execution & practice of system that processes, stores & or retrieves data and maintenance & assurance of data accuracy and consistency throughout its life cycle.

Another best alternative term is Data quality which is sometimes defined as the proxy term of data integrity. It is the wide term & it can be used in different meaning depending on the framework. For data integrity, data validation is prerequisite. Data corruption is the just opposite term of Data integrity.

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What is the difference between Data and Information?

Confirm that data is recorded exactly as projected and most of the cases Data Integrity technique is the same for all type of probable data source.
The main target regarding is to prevent any type of alteration of the exact data recorded during collection of data and it can’t change at any cost. At the time of retrieval of data, it must be same as collected before.

Data Integrity denote the accuracy & consistency of data over its lifecycle. Sensitive data may loss upon uses of Negotiated data. Under this consideration, continuing it is a main focus of many enterprise security solutions. During replicated or transferred of data, it shall be intact and unaltered. To ensure the integrity of data, Error checking methods and validation procedures are typically best way to serve the same.

Don’t confuse with Data Integrity and Data security, it is process to prevent unauthorized entry to the projected data or protecting data from unauthorized expert.
Any unintended changes to data as the consequence of a storage, retrieval or processing operation, including malicious intent, unexpected hardware failure, and human error, is failure of data integrity.

Failure of Data Integrity means any unplanned changes to data as the consequence of storage, unexpected hardware failure, retrieval or human error or processing operation malicious intent etc. If you unable to protect your data from unauthorized change then it can define as data security failure.

Data Integrity denote that it is a state as well as process, so a confusion may arise. Data Integrity is a state as it denotes a data set is both valid and accurate. Validation methods & Error checking are considered as Data Integrity processes.

Study of Data Integrity

Data Integrity is significant for several reasons & need to maintain the same. Data Integrity confirms searchability, recoverability, connectivity, & traceability. To increases stability & performance as well as improving reusability & maintainability; need to protect validity & accuracy of data. Data plays a major role to drive enterprise decision-making and data undergo several stages of changes to form raw to format to become more practical and identifying relation between them. In modern enterprises Data Integrity consider the top most priority.

In a database system, Data Integrity may be Compromised in various type of ways. In the following ways, Data Integrity may be compromised-
[][]Compromised hardware, like device or disk crash
[][]Compromise of Physical devices
[][]Cyber threats, Bugs, Viruses/Malware, Hacking, & other unfamiliar process
[][]Human error, whether unintentional or malicious

Transfer errors, unintended alterations or data compromise during transfer from one device to another device.
Most of the cases, some type of data security may protect this data compromises. Data duplication is the critical para meter for data security as well as data backup process.

Data Integrity & Databases

Data Integrity encompasses strategies for data specifying, retention, or guaranteeing the length of time data. Any lessening of enforcement could cause errors in the data; all of the rules shall be consistently & regularly implemented to all data entering the system.

At time of data input, checking system shall be implement which will lessen up the number of data error for the system. Data Integrity rules shall be strictly implemented to the system which will save troubleshooting time, erroneous data subsequently errors to algorithms.

A standard Data Integrity rules must have the strict definition regarding data relation; as which type data shall link with which type of data. A selling record of a item of certain product may be linked with the specific product but is shall not be related to unrelated data such as company asset, policy, loan, promotion etc. Based on predefined rules, it may be included check & correction system for the invalid a data.

Data derivation rules shall be applicable, mentioning data derivation procedure that how a data value shall be derived based on contributors, conditions and algorithm of the system. Re-derived procedure for data value shall be mention on which condition shall be considered for this process.

Types of Data Integrity

Organizations can maintain Data Integrity through integrity constraints, which define the rules and procedures around actions like deletion, insertion, and update of information. The definition of Data Integrity can be enforced in both hierarchical and relational databases, such as enterprise resource planning (ERP), customer relationship management (CRM), and supply chain management (CRM) systems.

Organizations can achieve it through the following:

Physical Integrity

Physical integrity deals with challenges which are associated with correctly storing and fetching the data itself.

Physical integrity indicates the right storing & fetching the data itself & its associated series of challenges. Various types of Challenges are involved with it such as Physical flaws, design flaws, power outages, electromechanical faults, corrosion, material fatigue, natural disasters, environmental hazards such as ionizing radiation, high temperatures, pressures & g-forces [the force of gravity].

Various methods are available to maintain the physical integrity such as UPS [uninterruptible power supply], redundant hardware, various type of RAID arrays, error-correcting memory, radiation hardened chips, clustered file system, watchdog timer & cryptographic hash function for critical system.

Error-correcting codes is extensively use as error detecting algorithms in Physical integrity of data management systems. Simpler checks & algorithms as the Damm algorithm or Luhn algorithm is use to detect Data Integrity errors. This system is use to uphold Data Integrity at manual transcription from one computer system to another computer system through a human intermediary such as credit card numbers. Hash functions are more beneficial to detect Computer-induced transcription errors.

These techniques are used together to ensure various degrees of Data Integrity in production systems such as a fault-tolerant RAID array may be use to configured a computer system but in silent data corruption block-level checksums might not provide.

In a nutshell, Physical integrity means protecting the accuracy, correctness, and wholeness of data when it is stored and retrieved. This is typically compromised by issues like power outages, storage erosion, hackers targeting database functions, and natural disasters, which prevent accurate data storage and retrieval.

Logical Integrity

It is concerned with correctness or rationality of a piece of data provide a particular context. It denotes the topic such as Entity integrity & referential integrity in a relational database system. Design flaws, software bugs, and human errors are the major challenges. Foreign key constraints, check constraints, program assertions, and other run-time sanity checks are the common methods to ensure logical integrity.

Design flaws & Human errors, both are the common problems for physical and logical integrity which must be properly deal with the contemporaneous requests to record and retrieve data. Physical error for a specific data system is more critical than logical error. If a data system suspected to logical error, it can be reused by overwriting with the new one but if it faces physical error then the data sector is totally used of its own condition.

Logical integrity ensures that data remains unchanged while being used in different ways through relational databases. This approach also aims to protect data from hacking or human error issues but does so differently than physical integrity.

Logical integrity comes in four different formats:

Entity Integrity

Entity integrity is a feature of relation systems that store data within tables, which can be used and linked in various ways. It relies on primary keys and unique values being created to identify a piece of data. This ensures data cannot be listed multiple times, and fields in a table cannot be null.

Referential Integrity

Referential integrity is a series of processes that ensure data remains stored and used in a uniform manner. Database structures are embedded with rules that define how foreign keys are used, which ensures only appropriate data deletion, changes, and amendments can be made. This can prevent data duplication and guarantee data accuracy.

Domain Integrity

Domain integrity is a series of processes that guarantee the accuracy of pieces of data within a domain. A domain is classified by a set of values that a table’s columns are allowed to contain, along with constraints and measures that limit the amount, format, and type of data that can be entered.

User-defined Integrity

User-defined integrity means that rules and constraints around data are created by users to align with their specific requirements. This is usually used when other integrity processes will not safeguard an organization’s data, allowing for the creation of rules that incorporate an organization’s Data Integrity measures.

Types of Integrity Constraints

A set of integrity constraints or rules are followed to implement Data Integrity in a database system. Relational data model suggests the three types of integrity constraints such as domain integrity, entity integrity, & referential integrity.

First of all, Entity integrity denote the concept of a primary key. As per this system, Entity integrity states that every table shall contain a primary key and existing column or columns of the table shall be identified by the primary key & it shall be inimitable and not null.

Concept of a foreign key denote Referential integrity. As per referential integrity rule, any foreign-key value can only be in one of two states. In general condition, foreign-key value refers to a primary key value of some table in the database system. Sporadically, a foreign-key value can be null and this will rest on on the rules of data owner. Under these circumstances, it can be stated here that this relationship is unknown or there is no relationship between the objects represented in the database system.

All columns in a relational database must be stated upon a defined domain is the main concern of the Domain integrity. In the relational data model, the primary unit of data is the data item. This type of data items is known as atomic or non-decomposable. A set of values of the same type is defined as domain. Actual values appearing in the columns of a table are drawn from the Domains which are considered as pools of values.

As the User-defined integrity are set by the specific user with a set of rules which is not related to domain, entity, and referential integrity classes. The database which supports these features, it is the sole responsibility of the database to confirm Data Integrity and reliability model for the data storage & retrieval system. The database which does not support these features then it is the accountability of the applications to confirm Data Integrity though the database supports the consistency model for data storage & recovery process.

A well-controlled single and well managed data-integrity system increases.

[][]Maintainability
=>All Data Integrity administration commences from a single centralized system.

[][]Performance
=>As a single operation unit, all Data Integrity operations perform as consistency model.
[][]Re-usability
=>A single centralized Data Integrity system provides the all applications benefit.
[][]Stability
=>As avoid the multiple system, so Data Integrity operation performs sound as well as better retrieval on a one centralized system.

Examples
Data-integrity mechanism is often considered as the parent-and-child connection of interrelated records. When one or more related child records exist for a parent record then all of referential integrity methods are handled by database itself & inevitably ensures accuracy & integrity of data, so no child record can exist without parent record and subsequently no parent drops their child records. In this system if the parent record owns any child records, then no parent record can be deleted and all of the process handled by the database system.

File systems

File system including Ext, JFS, UFS, XFS, and NTFS or hardware RAID solutions can’t provide satisfactory protection against Data Integrity problems. Some special file system such as BTRFS and ZFS use for silent data corruption can provide extra protection for data integrity. Upon provide this protection and being chance raise to corrupt the data then such file system can construct the data is widely known as end-to-end data protection.

Data Integrity as applied to various industries
[][]FDA has created the draft guidance for Data Integrity system as per 21 CFR Parts 210–212.[12] for pharmaceutical manufacturers. Same guideline has been developed by UK [2015], Switzerland [2016], and Australia [2017].
[][]Data Integrity also addressed by ISO as per ISO 13485, ISO 14155, and ISO 5840.
[][]FINRA [Financial Industry Regulatory Authority], implement the Data Integrity system on 2018 under technology change management policies and procedures” and Treasury securities reviews as Data Integrity problem found in 2017 on automated trading and money movement surveillance systems.

Why Data Integrity Matter?

Now a days Data are becoming more available, a smart business strategy which are using to make decision are obtaining the several times benefits.
According to recent research, a data driven organization is more than 23 times better performer in customer acquisition, nine times more performer to retain their customer and more than 19 times profitable to their competitor.

As the power of data is increasing day by day, so Data Integrity shall be valued properly and its importance can’t be denied at current situation. Presence of any type of error in data can spoil the total organization goal. A data driven organization must protect their database system at any cost to provide better security solution.

Threats involve in Data Integrity

[][]Human error:
It may arise in such case of transferring of data manually from one share drive to another, copying data from one spreadsheet to another and subsequently deleting of row or column of a spreadsheet. Storing data on excel sheet may cause formatting problem during manually data transferring process. Updating of excel sheet from old version to new one may cause formatting problem of subsequent data.

[][]Inappropriate format:
Stored data on Microsoft excel based on cell referencing may not accurate in different format. Failure to determine the same may case Data Integrity problem.

[][]Collection error:
During collecting any type of data, proper precaution shall be taken. Collecting of data on wrong method may cause storing of incomplete data and actual data may not represent the total situation.

[][]Internal security breaches:
If the database system hacked by third part or internal or external competitor may cause serious Data Integrity failure.

Why is Data Integrity Important?

Generally, a specific individual or group of people are involved in database system of a organization. Problem arises when multiple people are responsible to operate the database system. Anyone of the team member may not aware about Data Integrity of the organization, then all of the individual shall educate regarding protection of the database system and tech them the importance of data quality, accuracy, completeness etc. & they must learn how to combat when potential data security threat arises.

If all of the team member are aware about the Data Integrity and know its importance then it is very much effect to maintain the database system.

A better Data Integrity system can save company effort, time as well as valuable overhead cost. Wrong decision may take place based on inappropriate data. Data driven organization always take critical decision based on available data, if Data Integrity of that organization are compromised by any situation, then inappropriate result may arise and the organization suffer in long run.

Data always help to make important decision additionally it protect your company image. If you are collecting your customer information then protect the collect data which you have collect from your individual customer, failure to protect the customer data in proper database system may leak your customer information to another one cause image problem of the company and also mispresent your customer to other party.

Any type of customer information may be tracked and may be asked or run a campaign over them to collect specific target data. All type of collected information may not be sensitive as SSN [Social Security number]. To protect your valuable customer, you have to take a proper step regarding Data Integrity of your existing system.

a company staff always demand to data access to the database to trace any type of data on time manner upon request. He needs to uninterrupted access to the data system. For this reason, Data Integrity is so important for the organization. Data Integrity confirm the traceability & searchability of the data from its mother source.

Effective data accuracy and data protection shall be confirmed to increase Data performance and its stability. It is very much crucial to confirm the completeness & integrity of data. Compromised data always carry the wrong value for the organization & is of no use for most companies.

Same scenario arises for big data management. It is very much important to secure the big data management system as well as to maintain the total database system. All type data is totally worthless Without integrity & accuracy. Your data can be compromised If you fail to do the same. Under this circumstances, awkward & expensive data audit trails shall be mandatory to find out error & recover the total database system.

Most companies have set specific goals for their data & it is now more important than ever. But if integrity is not assured then data is not of much use. If data loss, corrupted or compromised then data can considerably damage any type of business. To maintain it, data security shall be confirmed using proper tools.

Compromised Data is the big challenge to maintain data integrity. There are several ways to compromised the valuable data. Todays almost all of the data are digital and store then in the same than traditional method and its transferred vary rapidly in different places of the globe. So, security shall be considering first & also the collection data is main concert maintain its integrity.

Data can be unaltered if data transferring occur maintaining valid system. Every time Data is moving from one place to another and it is not static, every user of the specific system is using the data and transferring the same in different way.

Management of Data Integrity

A group of steps are available where you can maintain & achieve better Data Integrity for your organization.

Collection of Accurate, Complete, and High-Quality Data

Quality of data depends on the collecting process of the projected data; a collection process is crucial and collection depends on the proper collection method. Failure to select proper data collection technic may cause collection of erroneous data. Sourcing is prerequisite to collect data. Ensure the high-quality data source may provide one step forwards to competitor.

Meticulously Check for Errors

To make common error during collection of data is the main problem of manual collection of error but it can be rectified successfully to involve the second one to the same project. Various type of error can be overcome if proper checking process can be initiate by appropriate body. For most critical data double check or triple check can be initiate. Growing continuous attention during data collection may reduce the data error. Sometime a review of the related data may decrease the data error. A color shading on projected excel sheet in the alternative row may help to track the mentioned data.

Cybersecurity Threats

Most of the time you can’t realize that a hacker or third party is trying to access your database. Person or individual who are trying to control your data send a short link with attractive or recent hot topics or similar to company email address link. Thousand of ways a hacker can try to damage or control your database system, so a strong security system shall be established to protect Data Integrity of the system.

Data Science Course

Failure to know the technical framework of database system, you can’t protect your data properly. If you are capable to handle the system then you need not to collect data science but if you want to update your existing your practice and eagerly want to update your team capability then you can involve your team in any data science course which are already available online which help your organization as well as development of self-confidence.

Devotion to Data Integrity

To keep subjects’ information safe & giving organization’s stakeholders the highest quality, accurate, complete, most data on which to base decisions need daily commitment for your Data integrity. A proper security system and group of trained individuals can support the organization in this situation to continue the company progress.

Data Integrity vs. Data Quality

Data quality is a crucial piece of the Data Integrity puzzle. It enables organizations to meet their data standards and ensure information aligns with their requirements with a variety of processes that measure data age, accuracy, completeness, relevance, and reliability. Data quality goes a step further by implementing processes and rules that govern data entry, storage, and transformation.

Data Integrity vs. Data Security vs. Data Quality

Data security involves protecting data from unauthorized access and preventing data from being corrupted or stolen. Data Integrity is typically a benefit of data security but only refers to data accuracy and validity rather than data protection.

Data Integrity & data security are more relevant to each other. Each of them plays a vital role for each other for their individual achievement. Data Integrity only refers to validation and accuracy of data but didn’t involve to protect data. On the other hand, data security confirms the protection against corruption or unauthorized access.

Data security plays a crucial role to maintain data integrity, on the other hand, Data Integrity is the end result of data security. To maintain data integrity, data security is the vital point and this situation may arise when accidental compromise occurs for data integrity.

Data Integrity is the essential component for the modern business procedure while making decision based on accuracy and efficiency of database. The main focus of the data security leads to Data Integrity and various type of procedure are applied to achieve the same.

Data quality can be defined that the data stored in database is compliant with the organization’s standards & requirements. It maintains integrity in a database. A set of rules to a specific or whole dataset and stores it in the target database shall be implement to do the same. Data Integrity shall be considered as data accuracy as well as correctness of data.

How to Protect Your data?

Validate Input

To ensure data accuracy data entry must be validated & verified input is significant when data is provided by familiar or unfamiliar sources, such as end-users, applications, & malicious users.

Remove Duplicate Data

Sensitive data stored in secure databases cannot be duplicated & it important to ensure that publicly available spreadsheets, emails, documents, & folders. To prevent unauthorized access to business-critical data or personally identifiable information duplicate shall be remove as soon as possible with the help of authorized personnel.

Back Up Data

To confirm data security & integrity data backups are crucial. To prevent the valuable data from permanent lost data backup shall be perform on regular manner at the end of everyday work. Organizations that suffer ransomware attacks, Data backups are especially important for them to protect their potential resources.

Access Controls

To maintaining data integrity, appropriate access controls shall be introduced in the organization. Data privileged option shall be implemented for specific user to control the database access procedure. This process will help the user understand their limitation to use the database system as well as to maintain the whole system.

Audit Trail

An audit trail is the standard practice to trace the unfavorable event. Data breach may occur at anytime in a renowned organization. If audit trail facility are available for the organization then it is very much to find out when and how data breached where. If proper information is available then it is easy to trac the source of attack. So audit shall be introduce at your organization for your database management system.

Assurance of Data Quality

It is the part of the Data Integrity process. Regular shall be conduct so that data can meet the certain standard. The processes of data accessibility, data cleaning, , data standardization is the main concern of the Data quality assurance. Data cleaning deal with inputting missing data, removing invalid entries, update same on timely manner.

Data accessibility deal with availability of the data to the stakeholders in secure and appropriate manner. For encoding and entering data, business shall be maintained and unauthorized data entry or transfer shall be prohibited. All type of company rule shall be implemented increase of transfer access of data to potential sector.

Data Corruption vs Data Integrity

Data corruption shall be considered as the serious Data Integrity failure. Based on the current practice, data corruption may be occurred through multiple channels. Most of them a very common problem is human error occur during collection or transferring of data. Malware and physical damage are another potential cause of data corruption.

Most human error often cause in wrong entry of collected data, unauthorized entry of database system, involving newcomer to sensitive practice with our prior training, programming etc.

It can be traced with appropriate data validation checks & restricting access to database system. Extensive & systematic use of backups can support restore databases in case of improper data entries.

Malware is another common cause of data corruption and this is basically occurred from external source which main purpose is to stole the data from potential data server. Cyberattacks are almost always unpredicted and instantly can’t recognized the source on the most of the cases. So here come the data encryption, always try to encrypt the critical and sensitive data and if possible tight security system shall be introduced though cost may be high to do the same.

To ensure organizational network security, regular penetration testing shall be done, this will help you to secure the organizational network system. A physical damage is may cause the data lose which mainly cause by accident and disasters. To protect data, data may be store in different physical location will protect from accident and natural disasters.


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