Let’s consider a hypothetical example: A pharmaceutical organization flaunts the safety of its latest wonder drug, but when the FDA orders inspection of its offshore production facility, the work is halted on an immediate basis. Why? Because crucial data for quality control standards is missing.
The unfortunate part is that this hypothetical example of compromised data integrity is actually very common. Problems related to data consistency and accuracy exist across every industry, and they can create all sorts of hassles and hazards for a business.
In the modern-day world revolving around big data, where more and more information is being stored and processed with each passing day, implementing measures to preserve data-integrity is critical.
But for such measures to be implemented effectively, understanding data integrity fundamentals is the first step. This article will cover exactly that.
Data Integrity – What Is It?
Data integrity can be summed up as the overall consistency, completeness, and accuracy of data. It can also refer to data safety with respect to regulatory compliance and security.
One such example is GDPR compliance. Data integrity is maintained through a collection of standards, rules, and processes implemented during the design stage.
When data integrity is ensured, the information stored within a database can be termed as complete, reliable, and accurate regardless of the period for which it is stored and the number of times it’s accessed. Data integrity further secures your data, keeping it safe from external forces.
The Various Aspects of Data Integrity
There are various aspects of data integrity, however it can mainly be categorized into two types: logical integrity and physical integrity. Both are a collection of methods and processes used for enforcing data integrity in both relational and hierarchical databases.
1# Physical Integrity
Protecting the accuracy and wholeness of the data as it is retrieved and stored, is deemed as physical integrity. It is usually compromised due to power outages, database hacks, or natural disasters.
Obtaining accurate data can be problematic for internal auditors, application programmers, system programmers, and data processing managers because of storage erosion, human error, and other similar issues.
2# Logical Integrity
With logical integrity, data remains unchanged as it is utilized in various ways within a relational database.
This form of data integrity protects data from both hackers and human errors, but in a different capacity as compared to physical integrity. The four sub-categories of logical integrity can be described as below.
i) Entity Integrity
Relying on creating unique values or primary keys which identify pieces of data, entity integrity ensures that there’s no data duplication and no field across the table is null.
An aspect of relational systems used for storing data in tables, it can be linked and utilized in several ways.
ii) Referential Integrity
Referential integrity is the collection of processes that ensure uniform usage and storage of data. Thanks to rules that are embedded within the structure of the database regarding the usage of foreign keys, it ensures only appropriate deletions, additions, or changes occur in data.
Rules might consist of constraints eliminating entries of duplicate data, thereby guaranteeing data accuracy and disallowing data entry that doesn’t apply.
iii) Domain Integrity
The list of processes implementing the accuracy of data in a domain is referred to as its domain integrity.
In this form of logical integrity, a domain is described as a set of acceptable values contained in a column. Constraints and other measures can be included in it, limiting the format, amount, and type of data entered.
iv) User-defined Integrity
Constraints and rules created by a user so their specified needs can be catered, are called user-defined integrity.
In some instances, domain, referential, and entity integrity are unable to safeguard data. This is when specific business regulations require being accounted and incorporated within the data integrity measures.
Risks Associated with Data Integrity
A wide range of factors can affect the integrity of data stored in databases. Some of the examples are as follows:
1# Hardware Compromises
Unexpected server or computer crashes are common occurrences of significant failures. They are indications of the fact that the hardware might be compromised.
Due to hardware compromises, data may be rendered incomplete or incorrect, resultantly eliminating or limiting access to data and making information hard to utilize.
2# Viruses and Bugs
Viruses, malware, and spyware are types of software known for invading computers and stealing, deleting, or altering data.
3# Transfer Errors
When it is not possible to transfer data from one database to another, this is referred to as a transfer error.
Transfer errors occur due to a piece of data being present in the destination table, but not being found in source table within a relational database.
4# Human Error
The deletion, duplication, or incorrect entry of data by an individual is called human error. It tends to occur when the designated data entry person fails to follow appropriate protocols or makes mistakes while implementing procedures meant for safeguarding information.
Risks associated with data integrity can be eliminated or minimized conveniently by carrying out the following protocols:
- Ensuring there are proper data integrity protocols in place
- Installing software that detects data errors
- Having a backup of your data
- Carrying out regular audits internally
- Keeping track of when data is deleted, modified, or added through logs
- Ensuring there are data validation measures in place both when it’s used and gathered
- Limiting access to the data and altering permissions to disable unauthorized parties from making changes
Protecting the data integrity data integrity of a company through traditional means may seem like a tedious task. With the help of secure, cloud-based integration platforms, a modern alternative is available that offers real-time monitoring of all the data.
Having cloud integration tools helps connect several source data apps and attain access to all the company’s data on one premises.
How Data Enrichment Can Play an Integral Role
Most companies suffer from lackluster data received from leads, which doesn’t provide enough information to work with. In such cases, the data is called non-enriched, which basically denotes that the data is incomplete. Utilizing non-enriched data is next to impossible, and in such cases, incomplete data is mostly discarded.
To make things worse, data tends to get stale over time whereas it can also contain false information from the beginning as well. This is where data enrichment plays a pivotal role in improving business value for any company that’s dependent on gathering data and generating leads.
In short, a data enrichment tool can prove considerably useful during the process of enhancing or appending the data at hand, as it can resultantly ensure data integrity.
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Javeria Gauhar, an experienced B2B/SaaS writer specializing in writing for the data management industry. At Data Ladder, she works as Marketing Executive, responsible for implementing inbound marketing strategies. She is also a programmer with 2 years of experience in developing, testing and maintaining enterprise software applications. Follow on LinkedIn.