Epic: collect and analyze team collaboration metrics

[Guidevit]

Task: Determine the Metrics and Criteria for Measuring Team Collaboration

Work with the team to brainstorm and identify key metrics that indicate successful collaboration, such as the number of cross-functional interactions, messages exchanged within and across teams, frequency of joint projects, and more.
Define specific criteria for what constitutes successful collaboration based on the context of your organization. For example, a high level of engagement in a project channel on Slack might indicate successful cross-functional collaboration.
Document these metrics and criteria for future reference and make them accessible to all team members.
Task: Extract Data on Cross-Functional Interactions from Communication and Collaboration Tools

Identify the tools where interactions happen the most (Slack, Microsoft Teams, etc.) and integrate with these tools via their APIs to extract data.
Develop scripts or bots to automate the extraction of data. The information could include the number of messages exchanged, mentions, threads, reactions, channel participation, and any other interaction that aligns with the defined collaboration metrics.
Ensure the data extraction process respects privacy and complies with the platform's and your organization's policies.
Task: Analyze the Collected Data to Calculate Collaboration Metrics

Once you have the raw data, clean and process it to prepare for analysis. This might involve filtering out non-work-related interactions or removing any outliers.
Use appropriate statistical methods or data analysis techniques to analyze the data and calculate the collaboration metrics. This could be as simple as counting the number of interactions or as complex as applying machine learning algorithms to categorize the types of collaboration.
Make sure to validate the results and adjust the analysis method as necessary.
Task: Design and Create Visualizations for Team Collaboration Metrics

Use data visualization libraries or tools (like D3.js, Plotly, Tableau, or even Excel) to create charts, graphs, or other visual representations of the calculated metrics.
Design the visualizations to be as intuitive and meaningful as possible. They should highlight patterns, trends, and areas of improvement.
Update the visualizations regularly and share them with the team. Ensure there's an avenue for feedback and questions to improve the accuracy and usefulness of the visualizations over time.