I have previously written about the value of big bucket retention categories to make it easier for users and machines to select the right retention to ensure compliance with business standards and regulations. I am a big fan of Susan Cisco´s work around big bucket categories, and below are some of her big buckets pros and cons.

The more retention categories, the more options, the more errors, the more complexity. Over-preservation of records with minimum retention requirement has no legal risk, but it can´t be done for records about people due to GDPR or similar privacy regulations. The big bucket categories are therefore easier to achieve for non-personnel/customer records, but you can still often get to some big bucket categories for records about people. And if you worry about what over-storing records may mean for your future storage and e-discovery costs, then this is best addressed by automatic deletion of non-records. Most of the information you have will be non-records.

Susan recommends consolidate records into a bigger “bucket” when:

• Records are related to the same or similar business processes

• Records have the same or similar legal & regulatory retention requirements

• Records are maintained for same or similar amount of time

As an example, this is how this could look before and after:

Before:

After:

How few buckets can you get to? The unicorn of big bucket retention categories is a US federal agency with only 4 categories, but you will need more retention categories if operating internationally to cover privacy regulations like GDPR. You may also want more categories to make it easier to find and monitor records from different areas, e.g. facility management, project & engineering.

Big Bucket retention categories to automate records management in M365

Big bucket retention categories makes it easier to implement retention in Microsoft 365 (M365) with Microsoft Records Management. Below is an example of how big bucket retention categories can be used as Record Labels in M365, and then two different ways to implement this in M365 for collaborative spaces.

Manual labelling by users

Record Labels can be published for users to manually apply them to documents that should be declared as records, but this require users to always do the right thing to ensure compliance. The categories also needs to make sense for users to ensure they can easily pick the correct label.

If you have many retention categories (no big buckets), then you can avoid label overload for users by only publishing relevant labels to sites when they are set up.

Automatic labelling based on content, metadata, and/or machine learning

Instead of publishing Record Labels to be manually applied by users, you can auto-apply Record Labels based on content (e.g. Sensitive Information Types), metadata (e.g. KQL), and/or machine learning (e.g. Trainable Classifiers).

For collaborative spaces, auto-applying record labels based metadata is often the best approach. This usually require the following:

• The big-bucket retention categories are set up as Information or Record type metadata values, and when a Microsoft Teams and site is created (e.g using the free Infotechtion Teams Hub), the appropriate big bucket retention category is then set as default metadata for the storage container based on the purpose of the Teams and site. Users can drag-and-drop files in the collaborative spaces, but also change the metadata if required.

• Information status metadata is set to draft as default for the storage container. Users can then change this to final when files are final, or save the information in final folders with final as the default metadata, or use approval process for final files with PowerAutomate that change the information status metadata to final.

• Once the information status metadata is set to final or approved, the correct Record label is then auto-applied based on retention category and information status metadata.

For collaborative spaces with information covering many big bucket categories, then users can either select record category when storing files (e.g. mandatory metadata) or use auto-classification and automatic metadata extraction to set the correct record category metadata (e.g. Syntex, Azure Cognitive Services).

For static sites with no active files, then labels can be auto-applied a number of ways.

Feel free to contact us if you need help improving your retention categories. Based on the big bucket retention categories, we can then identify the best way to automate records management in M365.