Developing a solid retention policy is therefore pure necessity to avoid risks such as data breaches and unnecessary storage costs. Yet in practice, such policies are often impossible to implement without automation. The missing piece of the puzzle? Data classification.
You cannot apply retention or deletion rules to data you do not recognize. To effectively implement a well-developed retention policy, a system using data classification must first understand the context of a document:
Without this classification, chaos ensues – even if you manage your organization's information flows as tightly as possible. Too much is saved (with all the legal and security risks that come with it), or vital information is accidentally deleted. You solve such problems by automatically classifying documents with an Enterprise Information Management (EIM) platform, finally making your retention policy enforceable and allowing retention rules to be applied consistently.
Manual cleaning of archives is time-consuming and extremely error-prone. By using an EIM platform, you transform your organization's static retention policy into a dynamic, automated process using metadata:
A good retention policy is not just ‘tidying up’; it is an essential prerequisite for being able to apply modern technologies such as AI. If you use AI models to improve processes, these models must work with a clean and legally accurate data set.
Outdated documents or information that really should have already been removed can cause wrong answers, bias, and privacy incidents. Data classification prevents that and ensures that you know exactly which data is suitable for AI, while your retention policy keeps your dataset clean.
A retention policy without data classification is a paper reality. By integrating both processes within an EIM platform, your organization regains control of the full information lifecycle. You work in compliance with the law, lower your risk profile, and you're optimally equipped to securely deploy AI.