Business decisions in large enterprises and startups are increasingly being made based on data. Data is now being generated and collected across several layers of business operations. The data extracted is used to fine-tune work processes, create metrics to determine business performance and better understand the prevailing market environment. With the increased importance and dependence on data for business viability, it has become critical that the data that is being used has integrity. This means that every business has to ensure that the data on which it draws insights for its operations is reliable, accurate, and dependable.
Businesses have to ensure that they are not using wrong, faulty, or altered data for their data science projects as relying on data that lacks integrity is a recipe for disaster since the insights derived will be amiss and any strategies built on top of them will likely fail in the real world. Data Integrity is, therefore, to be seen as an integral part of any successful data science workflow because the information derived from data ultimately relies on the accuracy of the input data.
Regardless of how expensive or elaborate enterprise data modelling tools are, they cannot in and of themselves provide and authenticate that the insights or suggestions generated are not skewed if the source data has been tampered with or corrupted. In this article, you will be introduced to the concept of Data Integrity, what it means, why it is important, the methods through which you can ensure/maintain Data Integrity within your organization.
By the end of all sections of this writeup, you will have been equipped with a working knowledge of how to think about and implement Data Integrity.
Table of Contents
What is Data Integrity?
Data Integrity in its simplest form can be thought of as the reliability and trustworthiness of data over its entire lifecycle, from the moment it is generated or collected, transferred, stored, backed up, archived, or used in performing analysis. Data Integrity answers the question of whether data is accurate, consistent, and can be trusted.
Data is said to have Integrity if it can be shown that the contents of that data have not been corrupted or compromised whether through a mistake as in the case of human error or maliciously updated as in the case of a data breach such as a ransomware attack.
Data Integrity can be used to describe the state of your data, is it valid or invalid, accurate or inaccurate. It can also be used to describe the processes through which you try to attain Data Integrity for your data such as data validation, error checking, outlier detection, etc.
Data Integrity usually goes hand in glove with Data Security. Whereas Data Security is concerned with limiting access to data to those with the requisite privilege, Data Integrity is more encompassing as it seeks to maintain the overall consistency and reliability of data. Data Integrity is a supporting component of Data Security alongside others like data validation and data quality.
In the event of a malicious or unauthorized change in data, a system that adheres to the standards of Data Integrity should be able to answer questions such as which data changed, who changed the data, when was the data changed, what permission level was required to change the data, etc. The entire role of Data Integrity is to ensure that records are not corrupted during the entire period they are in existence.
More information about Data Integrity can be found here.
Types of Data Integrity
Understanding the two types of data integrity, physical and logical, is necessary for maintaining data integrity. In both hierarchical and relational databases, both are collections of procedures and methods that maintain data integrity.
1) Physical Integrity
Physical integrity refers to the safeguarding of data’s completeness and correctness during storage and retrieval. Physical integrity is jeopardized when natural calamities hit, power goes out, or hackers interrupt database functionality.
Data processing managers, system programmers, applications programmers, and internal auditors may be unable to access accurate data due to human mistakes, storage erosion, and a variety of other difficulties.
2) Logical Integrity
In a relational database, logical integrity ensures that data remains intact when it is used in various ways. Logical integrity, like physical integrity, protects data from human mistakes and hackers, but in a different way. Logic integrity can be divided into four categories:
A) Entity Integrity
To ensure that data isn’t listed more than once and that no field in a database is null, entity integrity relies on the generation of primary keys — the unique values that identify pieces of data. It’s a characteristic of relational systems, which store data in tables that may be linked and used in various ways.
B) Referential Integrity
The term “referential integrity” refers to a set of procedures that ensure that data is saved and used consistently. Only appropriate changes, additions, or deletions of data are made, thanks to rules contained in the database’s structure concerning how foreign keys are used.
Rules may include limits that prevent redundant data entry, ensure proper data entry, and/or prohibit the entering of data that does not apply.
C) Domain Integrity
Domain integrity is a set of operations that ensures that each piece of data in a domain is accurate. A domain is a set of permitted values that a column can hold in this context. Constraints and other measures that limit the format, kind, and amount of data entered might be included.
D) User-Defines Integrity
User-defined integrity refers to the rules and limitations that the user creates to meet their own requirements. When it comes to data security, entity, referential, and domain integrity aren’t always enough. Business rules must frequently be considered and included in data integrity safeguards.
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The Importance of Data Integrity
The explosion in data generated is set to increase, not decrease. It is not uncommon to hear of sayings like “Data is the new oil”. This just underlines the importance that is being attached to data and its role in shaping future outcomes. Companies within the same market niche now analyze data to come up with strategies to outwit and outlive the competition.
Data is a crucial differentiator for companies as troves of it is being generated, stored, and mined. Information is derived from these tranches of raw data to enable competitiveness in the marketplace.
As a result of the dependence on data to drive business decisions, smart organizations are not only investing heavily in their data workflows but want to be able to trust that the insights generated are indeed accurate and can be a game-changer.
The trustworthiness of data is paramount for executives to have full confidence in the insights garnered by their data science teams. This confidence in data processes cannot be achieved without a sound culture of Data Integrity within an organization.
A well-implemented Data Integrity regime eliminates human errors, insider threats, malware, misconfiguration and security errors, etc. It also helps to provide an audit trail that can help with understanding data as it flows through an organization.
Data Integrity creates an atmosphere of quality assurance where control measures and procedures are put in place to ensure that best practices are being followed in the handling and management of data according to set down rules.
All of this ultimate feedback is to establish a more robust data operation that continuously churns out actionable suggestions that can be pursued for measurable success.
Factors Affecting Data Integrity
The integrity of data recorded in a database can be affected for a variety of reasons. The following are a few examples:
- Error due to human error: Data integrity is jeopardized when people enter information erroneously, duplicate or delete data, fail to follow proper protocols, or make mistakes during the implementation of procedures designed to protect data.
- Errors in the transfer: A transfer error occurs when data cannot be effectively transferred from one database location to another. In a relational database, transfer errors occur when a piece of data is present in the destination table but not in the source table.
- Bugs and Viruses: Spyware, malware, and viruses are types of software that can infiltrate a computer and change, erase, or steal information.
- Hardware that has been compromised: Significant failures include sudden computer or server crashes, as well as issues with how a computer or other device performs, which could indicate that your hardware is compromised. Compromise hardware might cause data to be rendered inaccurately or incompletely, limit or remove data access, or make information difficult to use.
The following steps can simply be taken to reduce or remove data integrity risks:
- Limiting data access and modifying permissions to prevent unauthorized parties from making changes to data
- Validating data, both when it’s collected and when it’s utilized, to ensure that it’s accurate.
- Using logs to keep track of when data is added, edited, or deleted is a good way to back up data.
- Internal audits are carried out on a regular basis.
- Using software to spot errors.
Best Practices to Maintain Data Integrity
This section deals with how you can maintain Data Integrity in your organization. You will be introduced to methods that you can use to preserve Data Integrity.
For Data Integrity to be achieved, best practices in handling data must be followed. It is always better to standardize these processes throughout your organization instead of leaving it to the whims and caprices of individuals or teams.
The section below highlights some of the practices that can be used to achieve Data Integrity, you can think of it as some sort of checklist that will take you closer to having data that is authentic and truthful.
The Steps for maintaining Data Integrity are:
- Always Validate Input Data
- Implement Access Controls
- Keep an Audit Trail
- Always Backup Data
- Adopting Security Best Practices
- Educate your Workforce
1) Always Validate Input Data
Input data should always be validated before it is allowed into your data storage system. Validation is the process of checking data to make sure it is correct and useful. Data should be checked for accuracy regardless of the source of the data, be it data from end-users of an application, internal systems, or external sources.
2) Implement Access Controls
Access to data should be tightly regulated to ensure that only those with the proper authorizations have access to data. A least privileged model of security should be used in which access is only granted on a need-to-know basis.
Broad access such as administrative rights of entire systems should seldom exist. Instead, employees should have access to only data that enable them to perform their specific job roles. Data should be isolated so that incidences of unauthorized access are pretty much non-existent.
3) Keep an Audit Trail
It is important to maintain an audit trail mechanism that can track the source of data changes. In the event of a data breach, it is vital to know the source of the breach, the documents or data that may have been accessed, and how the breach was possible.
An audit trail should be generated through an automated process in which individuals do not have access to tamper with the results of the audit trail.
It should also have the ability to track data events such as create, delete, update, etc. along with the time the events occurred and the individual that triggered them. A well-managed audit trail can help a lot in the case of investigating a data breach.
More information regarding maintaing an audit trail can be found here.
4) Always Backup Data
Having regular, reliable, and timely backup of data systems is essential to ensure that data can be recovered in the event of data loss. Data loss may be occasioned by hardware failure, software bugs, or even ransomware attacks. A backup process ensures that your organization will not suffer from permanent data loss.
More information regarding backup data for an organization can be found here.
5) Adopting Security Best Practices
The security of systems that contain your data should be checked regularly. Software patches should be installed in a timely fashion, and known security vulnerabilities of software packages should be mitigated.
Physical access to data centers or server farms should be restricted to only authorized personnel. Authentication systems should also be used so that only individuals who have been authenticated according to their access level can have access to data.
More Information regarding Data Security best practices can be found here.
6) Educate your Workforce
The employees in your organization should be trained to always maintain the integrity of data in all work processes. A culture of sound data management should be established whereby individuals adhere to Data Integrity guidelines and team members are encouraged to always handle data in a way that ensures the consistency and reliability of data.
In this article, you were educated on what Data Integrity is, the need for Data Integrity in data workflows, and the ways through which an organization can enshrine the principles of Data Integrity to reap the benefits of accurate and reliable data. An in-depth overview was provided on the steps that can be taken to make sure that data has Integrity throughout its entire lifecycle.
It is important to also note that Data Integrity can be supported at the software level, for example, databases can enforce the Integrity of values on columns and rows. Since software packages can provide some level of Data Integrity checks, care should be taken to choose software that encourages Data Integrity. One of such platforms that supports Data Integrity is Hevo Data.
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