What is Data Integrity and How to Reduce Associated Risks

Data integrity issues plague businesses around the world. Decisions based on faulty and incomplete information often do not produce the desired results. Companies that do not invest in data integrity tend to lose their income due to these issues. They lose 12 percent of their revenue on bad data issues. Critical sectors like the pharmaceutical industry get punished for their negligent data integrity practices by FDA regulations. In the worst-case scenario, false data can have lethal implications in this sector.

‍Some of the top data integrity issues are: –

1. Lack of enforcement of data integrity best practices from senior leadership
2. Lack of technical and regulatory expertise to perform well
3. The ambiguity between “normal” errors and criminal activity
4. Establishment of authority that doesn’t contribute to product quality
5. Cybersecurity issues

92 percent of the surveyed senior executives worry about the negative consequences of data misuse in their organization.

What is Data Integrity?

Here’s what ALCOA stands for: –

• [A] Attributable: Businesses should be aware of the data creation and extraction processes. Your leadership should know their data sources and the reliability of the data.
• [L] Legible: Your data should be easy to read and understand, and these records are permanent.
• [C] Contemporaneous: Concerned authorities in your company should be aware of the initial data and the processes it goes through to give you the desired results.
• [O] Original: This part of data integrity implies a strong relationship with the data source and the ability to store the original data.
• [A] Accurate: Error-free data that conforms to various application protocols make up the data integrity accuracy aspect.

Why Data Integrity is Important?

Warranting data integrity helps businesses in many ways. It makes sure your information is easy to recover, search, trace (to the source), and connect. Securing the legitimacy and correctness of data enhances the steadiness and performance levels of your data management systems. Executing data integrity methods improve the reusability and maintenance of data.

Many businesses put data at the forefront of their decision-making process. However, data from reliable sources need to go through various changes and methods to fit in useful formats. These practical formats will help you identify relationships and facilitate better decisions. Hence, investing in data integrity is a top priority for modern enterprises. Improving data quality also leads to a 20 to 40 percent increase in sales.

Steps to Reduce Data Integrity Risk

1. Promote an Honest Culture

Several data integrity issues stem from basic data entry mistakes from your employees. Encouraging your employees to be honest about their work helps you avoid these issues. Truthful employees report data integrity issues as soon as they detect them and work on correcting them.

These staff members also go out of their way to use the right methods to correct these issues. So, higher management should not penalize employees for reporting data integrity issues. In fact, you should reward them for the early detection and correction of these issues.

2. Streamline your Software Development Process

A robust software development lifecycle helps you oversee quality-focused tasks throughout the cycle. Reviewing this lifecycle helps organizations address structural changes that can make their operations more quality-centric. All technology in your firm needs to be developed, skilled, verified and examined regularly and appropriately. So, tweak your quality processes constantly to make data integrity a top priority.

3. Cleanse and backup your Data Regularly

Removing identical data and backing up data regularly ensures data integrity standards are maintained. Having duplicate data complicates the database and has the potential to increase the risks of leaking valuable data.

Regularly backing up your data ensures protection against malicious cyberattacks. Your company can use various market tools to clean and back up your data at a low cost. So, improve your data quality by cleansing it and backing it up at set intervals.

4. Implement Quality Control Measures

An organization enforcing these measures should appoint people and processes. These people ensure all other employees work according to the security and data management rules. These enforcers help with the data integrity risk assessment process. You can appoint data stewards to monitor the lineage of data sources to assess security systems.

5. Have Proper Validations in Place

It doesn’t matter what data collection sources you choose. You need to take care of this raw data and process it appropriately before it enters your systems. Various data validation processes help you cleanse your data enough to ensure it has the topmost quality. Some of these processes include: –

• Ensuring data transfer doesn’t change the meaning of data through source re-verification.
• Ensuring issues in legacy systems do not get transferred to the new system by ongoing source-to-source verification.
• Tracking all issues at the same place for better management.
• Performing data validations on external data that enter your system.
• Collecting statistics on the overall functioning of data cleansing cycles.

6. Audit your Audit Trails

An audit trail is an inerasable data record in your system to include all changes made to the database files. To monitor relevant data, consider developing a robust review of your audit trail that includes all standard good practices. You can then assign responsibilities and schedules to test your audit trails efficiently. Base your audit trail review’s depth on system complexities and uses. Use your knowledge of audit trails to identify issues within them.

Conclusion

Did you know that companies lose 20 percent of their revenue due to poor data issues? Avoid these losses by investing in data integrity measures that propel your growth. We hope our blog helped you understand what your company should focus on to improve your data quality.