• Helen Pritchett

Don't Compromise on Data Integrity

Why Data Integrity Matters

Data integrity is the overall accuracy, completeness, and consistency of data. The term also refers to the safety of data in regards to regulatory compliance, such as GDPR compliance, and security. It is maintained by a collection of processes, rules, and standards implemented during any database design phase. When the integrity of data is secure, the information stored in a database will remain complete, accurate, and reliable no matter how long it’s stored or how often it’s accessed. Data integrity also ensures that your data is safe from any external (sinister) forces.

Data integrity is vital to us all in both our personal and business lives. Without data integrity we are at risk, are non-compliant and will suffer the consequences. Working with data as we do, we know the importance and value of data integrity. Without it our business model would be flawed, and our credibility would suffer. This is why, at ProspectaBase, we maintain extremely high levels of data integrity and ensure our systems and database function to exemplary standards.

As mentioned above, the term data integrity refers to the accuracy and consistency of data. A good database will enforce data integrity whenever possible. For example, a user could accidentally try to enter a phone number into a date field but if the system enforces data integrity, it will prevent the user from making these mistakes.

Data integrity vs data security

Data integrity and security are closely related but distinctly different. Data security refers to the protection of data against unsanctioned access or corruption and, therefore, is vital to ensure data integrity. Data integrity refers only to the validity and accuracy of data rather than the act of protecting data.

Data integrity for databases

The term data integrity often mostly related to database management. There are four types of data integrity relating to databases as described below by Digital Guardian. Be warned, this gets a little technical. As L’Oréal would say “Here comes the science bit.”

1. Entity Integrity: In a database, there are columns, rows, and tables. In a primary key, these elements are to be as numerous as needed for the data to be accurate, yet no more than necessary. None of these elements should be the same and none of these elements should be null. For example, a database of employees should have primary key data of their name and a specific “employee number.”

2. Referential Integrity: Foreign keys in a database is a second table that can refer to a primary key table within the database. Foreign keys relate data that could be shared or null. For instance, employees could share the same role or work in the same department.

3. Domain Integrity: All categories and values in a database are set, including nulls (e.g., N/A). The domain integrity of a database refers to the common ways to input and read this data. For instance, if a database uses monetary values to include dollars and cents, three decimal places will not be allowed.

4. User-Defined Integrity: There are sets of data, created by users, outside of entity, referential and domain integrity. If an employer creates a column to input corrective action of employees, this data would be classified as “user-defined.

Data integrity can be jeopardised in several ways and must be protected using a variety of data security tools. Examples of how data integrity can be compromised include:

· Human error

· Transfer errors

· Bugs, viruses/malware, hacking, or other cyber threats

· Compromised hardware, for example a device or disk crash

· Physical compromise to devices

In the modern world, data integrity is essential for the smooth running of business processes, for maintaining compliance and for informing business decisions and therefore needs to be taken very seriously. If you’re buying data, always ensure you’re working with an organisation that can validate and reassure you of its data integrity (and data security) credentials. Data accuracy is invaluable. Making business decisions based on incorrect data can result in huge issues. Data-driven business decisions are now routine, and by using data without integrity, those decisions can have a dramatic effect on the company’s bottom line.

A new report from KPMG International reveals that only 35% of senior executives say they have a high level of trust in the way their organization uses data and analytics. 92% are concerned about the negative impact of data and analytics on an organization’s reputation. For C-level executives to make sound business decisions, faith in data integrity must be beyond reproach.

Data integrity empowers decision making

In order to give your decision makers ultimate confidence in the data you are using, try following the checklist below to preserve data and minimize risk:

1. Validate Input: data should be verified and validated to ensure that every input is accurate

2. Validate Data: ensure that your data processes haven’t been corrupted

3. Remove Duplicate Data: be prudent – clean up stray data and remove duplicates

4. Back up Data: backups are critical and helps to prevent permanent data loss

5. Access Controls: implement (at the very least) a privilege model where only users who need access to data get access. Don’t overlook physical access; the most sensitive servers should be isolated and bolted to the floor or wall

6. Audit Trail: audit trails provide an organization with the ability to accurately pinpoint the source of any problem that might occur

Several years ago, collecting data was the challenge. Now we have access to huge amounts of data the challenge is keeping its integrity intact so that C-level execs have the confidence to use it to make data-driven decisions to steer a company in the most positive (and profitable) direction.

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