The World of Data

If one were to look at the world’s highest-performing companies right now by market cap, one’d notice that 4 out of the top 5 are massive tech companies, with one oil conglomerate sneaking in. It is no surprise then to hear huge news outlets, such as The Economist, calling the data a new oil and the age we’re living in an age of Big Data. Data, like oil, has the potential to improve our lives by a great margin, but just like oil, if unprocessed, it’s of no use to us. 

However, while the impact of mishandled oil and its derivatives is known to the public, the dangers of mishandling data are still ignored. Understanding the power the data brings, it’s clear that we have to deal with it responsibly. That’s where data integrity comes in.

What Is Data Integrity?

The processes meant to ensure data’s accuracy, completeness, and consistency are called data integrity. Warranting the integrity of data ensures that it remains constant and complete, reliable and accurate, whenever and no matter how many times the data is accessed. Amongst other things, data integrity ensures that the safety of the data is up to the regulatory standards to which they are subject. 

Ensuring the safety of our data in today’s world cannot be overstated, given how prevention is a cure when dealing with bad-faith actors wanting to access and abuse your data. The first step in doing this is ensuring that the internal users handle the data correctly, implementing various validation and error checks to ensure that the data is stored and handled correctly, decreasing the potential risk of contact with malicious intents. 

While oftentimes used interchangeably, data integrity, data quality, and data security all have different meanings. Data quality, like data security, is merely a tiny but vital component of data integrity. In simplest terms, while data security aims to shield information from outside threats, data integrity is concerned with maintaining information’s accuracy and integrity over its entire existence. With various methods that gauge your data’s age, relevance, accuracy, completeness, and reliability, data quality answers whether the data satisfies established corporate standards and needs. Every facet of data quality is covered by data integrity, which goes above and beyond by putting in place a variety of regulations and procedures that control how data is input, stored, transported, and much more.

Relationship between data integrity, data security, data quality, and data accuracy 

Relationship between data integrity, data security, data quality, and data accuracy 

How Do We Protect the Data?

Data integrity entails protecting the data in two ways: physically and logically. These are broad terms for groups of procedures and techniques that guarantee data integrity in relational and hierarchical databases. 

Processes responsible for safeguarding data’s completeness and accuracy during storage and retrieval fall under physical integrity checks. Physical integrity comes into question when calamities occur, whether the power is going out or hackers meddle with our data. Human mistakes, storage deterioration, and other problems may be culprits for the inability to access accurate data. 

Thanks to logical integrity, data in a relational database remains intact while used in various ways. While physical and logical integrity shield data against human error and hackers, logical integrity accomplishes it differently. As such, logical integrity is achieved through these four general practices:

1. By using unique values for identifying individual pieces of data, that is, primary keys, we can ensure that data may be stored in relational systems in tables that can be linked together and used in many ways.

2. Referential integrity describes the procedures that guarantee uniform data storage and use, carefully regulating how foreign keys are utilized.

3. Given the specifics of each piece of data and its domain, we can create individual sets of procedures that limit how we can access, use or update data. Speaking in broad terms, these procedures are all part of the data’s domain integrity.

4. Specific business standards frequently need to be considered and included in data integrity procedures. Specific guidelines and limitations developed to meet such requirements are referred to as user-defined integrity.

How Do Breaches Happen?

Many variables can negatively impact the integrity of data recorded in a database. There are a few examples, such as the following: 

1. Human error: Data integrity is compromised when people enter information erroneously, duplicate or delete data, fail to follow the proper procedures, or make mistakes when carrying out security measures. Anyone who’s worked with data sourced by a survey knows the dangers human error poses.

2. Transfer errors: A transfer error occurs when data cannot successfully be moved from one point in a database to another. In a relational database, transfer errors happen when a piece of data is present in the destination table but absent from the source table. 

3. Malware: Viruses, spyware, and sorts of malware can infiltrate a computer and change, remove, or steal data. An excellent example of this is Stuxnet, a “simple” computer bug that managed to slow down the Iranian progress toward a nuclear bomb notably. While the Iranian nuclear problem had many fail-safe procedures, one they did not expect to happen was the use of an unauthorized USB device containing said malware. 

4. Hardware compromise: Sudden server or computer crashes, issues with how a computer or other device works, and other serious failures are some symptoms of failing hardware. Data may be rendered inaccurately or incompletely, access to data may be restricted or denied, or information may be difficult to utilize due to compromised technology.

The most common causes of data breaches and leaks can be found here.

Enough Fear-Mongering – How to Prevent These?

It is crucial to implement several strategic remedies since data integrity risk is so harmful to companies and overall society. However, it is tough to limit the risk to data integrity with only one method; it is thus advisable to combine a number of them. Some of the most effective methods for reducing the threats to data integrity are listed below, yielding the best results when they complement each other:

1. Promotion of Personal Responsibility: Workers in environments that promote an integrity-oriented culture are not only more productive, but they are also more likely to report instances when others fail to uphold their commitments concerning many facets of data integrity.

2. Good Quality Control Measures: Specific individuals and procedures should be put in place as QC methods to ensure that everyone handling the data complies with security and data governance guidelines.

3. An Audit Trail: Implementation of audit trails lowers the risk to data integrity by noting the data’s state at different times in its history, including the origin and subsequent modification or consumption. 

4. Produce Flowcharts for Each Significant Piece of Data: Controlling how, where, and by whom data is used requires the creation of process maps for critical data, which gives an organization better control over its data assets.

5. Get Rid of Known Security Flaws: While this one is self-explanatory, security flaws must be fixed to lessen the chance that maintaining data assets would compromise their integrity.

6. Follow the Software Development Lifecycle: Controlling data flow inside an organization requires adhering to a software development lifecycle. Knowledge of these development lifecycles is necessary to comprehend the many governance rules needed to manage data in line with regulatory and security requirements.

7. Verify the Computer Systems You Use: Planning, mapping, and prescribing what is supposed to happen with data is useless without periodically testing, confirming, and revalidating whether IT systems and employees are working in compliance with these procedures.

8. Use Software to Detect Errors: Anomaly detection services and error detection technologies can help find and isolate outliers, identify the root causes of mistakes, and provide guidance on future error prevention, keeping the risk to the data at a manageable level.

More on means of protecting data can be found here.

Why is Data Integrity Important?

Data integrity is crucial because it can enhance the capacity to track and restore data back to its source. Additionally, it guarantees that your company’s data may be preserved more accurately and securely, thus protecting both you and your business. As a result, data integrity is significant from more than just a legal standpoint. It has far-reaching effects, including bettering your relationship with consumers, upholding an excellent brand image, and assisting in keeping your business resistant to outside threats.

Possible consequences of data breaches

Possible consequences of data breaches (Image Source)


How to Ensure Data Integrity?” Tech Bite was brought to you by Ferid Omić, Junior Data Analyst at Atlantbh.

Tech Bites are tips, tricks, snippets or explanations about various programming technologies and paradigms, which can help engineers with their everyday job.

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