The cost of non-archiving.

Non-archiving can result in significant costs due to increased processing time. As the volume of data grows, processes become slower, leading to a higher allocation of resources and time for tasks. The implications of non-archiving include:

• Scrambling processes take longer, impacting overall efficiency.
• Longer backup times and restore times affect the speed of recovery during major incidents.
• APIs may respond slower, potentially causing critical issues such as timeouts.
• Queries are not as fast as before, affecting data retrieval speed.
• End-of-day processes may take longer to complete.
• Processes may run with outdated data, prolonging their duration.
• Need more and more disk space to store it.

To tackle these issues effectively, data must be classified into categories such as IT log, IT data, and Business data. This impacts the ownership of the data. Use the help of an external company if knowledge on core applications is missing.

Once classify we can define archiving rules. For IT log, IT data, IT department can work independently and define an adequate frequency and criteria to  archive technical logs.

Involving the owner of the business process is essential for business data archiving. Seeking their approval, the frequency, and criteria for archiving can be jointly defined and scripted. While businesses may not always prioritize this topic, efficient data management by the IT team leads to time savings, allowing more focus on what matters to business.

Implementing effective data archiving not only aids in identifying files that can be safely wiped for UAT refresh but also enhances the integrity of non-personal data in the UAT environment. UAT refresh will become faster and safer.

The cost of bad architecture around your data.

Efficient data processing relies on the correct and logical organization of data. Properly ordering and storing data, such as linking customer names and birthdays in the same table or facilitating easy connections to transactional applications via a customer ID, minimizes costs and maximizes efficiency.If this is wrongly done, any process, any development will cost more and your processing time will take longer.

Incorrectly structured data architecture results in increased costs for any process or development, extending processing times. Moreover, a poorly designed architecture puts businesses at a competitive disadvantage by reducing adaptability to new technologies.

Beyond financial implications, the repercussions of bad data architecture extend to the potential for incorrect decision-making based on flawed dashboards. This, in turn, can lead to strategic failures.

To mitigate these risks, it is essential to conduct regular audits of data organization, challenge existing dashboards, identify key data requirements, and implement robust IT controls to ensure data accuracy. Simply having data stored does not guarantee correctness or easy accessibility.

The cost of inadequate data security.

Data is key for your business to run, and its sensitivity demands high protection. Safeguarding data involves:

• Ensuring only authorized individuals have access.
• Restricting granted access strictly to the purposes of its mission.
• Conducting penetration tests to maintain control over external threats.
• Granting administrative rights only to a select few to mitigate potential issues.
• Establishing a well-defined backup strategy for your data.

The cost of the personal data not well identified.

There are a couple of costs in this chapter, mostly linked to GDPR regulations.

• The incorrect classification of the sensitivity of the application could lead to inadequate security and/or non-compliance with group policy.
• This creates issues during UAT refreshing as data might not be scrambled as it should.
• Potential data breaches may occur due to poorly scrambled data in lower environments.
• Unauthorized processing of data from lower environments.

The suggested solution is to list the fields that are sensitive and test the scrambling process. Define a personal data section and scrambling process in every specification.