First of all, let’s define what we mean by “inactive” reports. These are the ones that haven’t been accessed or used in a while (let’s say more than six months). Now, you might be thinking why bother removing data from these reports? Well, there are several reasons for this:

1) Saving storage space: Inactive reports take up valuable disk space on your servers. By removing the data associated with them, you can free up some much-needed resources and improve overall system performance. 2) Reducing processing time: When an AI system processes a report, it needs to read in all of the relevant data from various sources. If that data is no longer needed (because the report hasn’t been accessed for months), then there’s no reason to keep it around and slow down your system. 3) Improving security: By removing old or outdated reports, you can also improve the overall security of your AI system. This is because these reports may contain sensitive information that could be vulnerable to hacking or other forms of cyber attack. So how do we go about removing data from inactive reports? Well, there are a few different ways to approach this task:

1) Manually deleting the data: If you have a small number of inactive reports (say, less than 50), then it might be easiest to simply delete them manually. This can be done using your favorite database management tool or by writing some custom code. 2) Using an automated script: For larger datasets, however, manual deletion is not practical. In this case, you’ll want to create a script that automatically identifies and removes inactive reports based on certain criteria (such as date of last access). This can be done using any number of programming languages or tools, depending on your preference. 3) Implementing a data retention policy: Finally, if you have strict regulatory requirements around data storage and retention, then you might want to consider implementing a formal policy that outlines how long certain types of data should be kept before being deleted. This can help ensure compliance with various legal or industry standards while also freeing up valuable resources for your AI system.