Add-on module required
You will only have access to the features described in this article if you have purchased the Classification Intelligence add-on module with your Records365 subscription.
To view the Classification Intelligence reporting dashboards you need to be assigned to either the Application Administrator or Records Manager role in Records365.
Records365 provides a set of dashboards to display metrics for the Classification Intelligence (CI) feature set. Their purpose is to provide administrators with insights into how the feature is being utilized to facilitate governance of semi and unstructured content.
The CI dashboards can be accessed from the left-hand navigation menu, by clicking on the Intelligence section and selecting Analytics
The Overview tab displays some key metrics to an overview into how CI is being leveraged by Records365 to apply policy to records.
The first set of numbers indicate how many records have been categorized, and what portion have been categorized by CI. The total number includes rules-based assignment, manual assignment via reschedule or physical record assignment and finally assignment through the use of CI. Records which have a suggested category from the CI feature but are not yet accepted are excluded.
The subsequent group of numbers shows how much of your file plan is covered by CI. It highlights how many of the total file plan categories are included as part of the currently active machine learning model. For more information on including categories into a model see the documentation section on Training.
The Classification Acceptance Rates charts provides an overview of what percentage of suggestions are accepted over time. This is indicative of the overall performance of the currently active model.
A rejected suggestion is specifically when a user performs a Reschedule operation from the Intelligence page. It is worth noting that Rescheduling from any other pages in Records365 is not treated suggestion rejections. For more information on applying records categories using CI see the Managing Suggestions documentation.
The information in this chart makes it really easy for you visualize your model’s performance over time. The higher the percentage of acceptance rates, the better the model. Model performance, and therefore acceptance rates, may be improved with continual training using content that is most representative of each file plan category.
The Classification Breakdown chart shows what portion of content is being categorized using CI over time. It demonstrates what percentage of content has been categorized by CI as opposed to metadata-based rules. All unclassified content, including records that have pending suggestions, is excluded from this chart.
With the right level of investment into training and curating content you should see an upward trend in the ratio of content classified using CI. As this upward trend occurs, metadata-based categorization can be optimized to strike the right balance between metadata and AI-based categorization to give your organization complete coverage.
Record categories can be applied to content using features other than metadata-based rules and CI, such as rescheduling and manual classification for physical items. The above graph only tracks CI and metadata-based categorization, so you may notice that some segments in the graph may not reach 100%.
Please find links below to other documentation articles mentioned in this page