About
Information below is in relation to the dashboard generated by running the Voleer Microsoft Teams Assessment available at Voleer.com
Click on the sections of the image below to understand more about the different sections of the assessment.
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Overview
The data below shows an overview of the number of private and public teams and guest information
Why is this important
Questions to ask when viewing this data…
- Did I expect to see this many teams?
- Is the number too small meaning the adoption of Teams is low?
- Is the number too high potentially meaning improper use of Teams or defunct Teams not being archived?
- Did I expect to see this many public teams?
- Is public teams the right forum for communications or is that communication better suited to Yammer?
- Did I expect to see this many guests?
- Is the access to the guests still required?
Teams Activity
The data shows the number of Microsoft Teams that haven’t had any activity (chat, files added, group mailbox messages) in the stated period.
Why is this important
Out of the box, Microsoft Teams allows the freedom to create Teams and Channels without approval, potentially resulting in sprawl. Identifying Teams that haven’t had recent activity could mean that Team was created but not used, making it ideal to archive to reduce sprawl.
Also, Teams without activity could possibly mean the original purpose of the Team is no longer required, i.e. a Team used to respond to a tender.
Team Owner Summary
The data shows the number of Team owners that haven’t logged in for x days and the number of Team owners who have their accounts disabled.
Why is this important
Team owners have the ability to add members and approve pending membership requests. If the owner of a team is no longer active or disabled (i.e. no longer with the organization or on a long break), the ability to add members to the team has to be done by the IT Administrator.
Similar Teams and Channel Names
The data shows the number of teams and channels with similar names within the Office 365 tenant.
Note - Similar names are calculated using the string metric formula Levenshtein distance.
Why is this important
One of the key benefits of using Microsoft Teams is to help with faster and easier collaboration and coordination of work using teams and channels for discussions and sharing information. However, by default, any person in the organization can create a team and channel which can quickly lead to sprawl and duplication.
Recommended actions
Review and share the information to the Team owners for them to identify if information being shared in the channels is duplicated.
Similar Team Names
This shows the number of teams with names that are similar across the entire tenant. For example, a team name of US Sales Q1 2020 and Q1 2020 - US Sales would be flagged as similar.
Similar Channel Names within Team
This shows the number of channels with names that are similar within a Team. For example, a channel within US Sales Q1 2020 called Tender Response for ACME Inc and ACME Inc Response would be flagged as similar.
Similar Channel Names with Owners in Same Department
This show the number of channel names that are similar with other channel names where the owner of the Team is the in the same department. For example, John in the Sales Department is the owner of the Team US Sales West and Debra, also in the Sales Department, is the owner of the Team US Sales Central. A channel named Tender Response for ACME Inc in Team US Sales
West would be flagged as similar to a channel named ACME Inc Response in
Team US Sales Central.
Distribution of Channels in Teams
The example to below shows that 59 Teams have between 1-10 channels, 3 Teams have 11-50 channels, and 1 team has 51-150 channels.
Why is this important
Microsoft Teams has a limit of 200 channels per team (inclusive of deleted channels). By keeping track of the number of teams reaching this limit can help prevent unnecessary delays when creating new teams within the channel.
Distribution of Teams joined for Users
The data shows the bell curve for the number of users per team. In the example to the right, 44 users are in 1-4 teams, 58 users are in 5-8 teams, 54 teams are in 9-12 teams etc
Why is this important
Showing the data in a bell curve shows the distribution of users in teams. Values left of the curve’s peak represent users who may not have access to needed information and values right of the curve’s peak represent users who may have too much noise (information overload).
Comments
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