The digital transformation of work presents new opportunities to understand how informal workgroups organize around the dynamic needs of organizations, potentially in contrast to the formal, static, and idealized hierarchies depicted by org charts. We present a design study that spans multiple enabling capabilities for the visual mapping and analysis of organizational workgroups, including metrics for quantifying two dimensions of collaboration culture: the fluidity of collaborative relationships (measured using network machine learning) and the freedom with which workgroups form across organizational boundaries. These capabilities come together to create a turnkey pipeline that combines the analysis of a target organization, the generation of data graphics and statistics, and their integration in a template-based presentation that enables narrative visualization of results. Our metrics and visuals have supported hundreds of presentations to executives of major US-based and multinational organizations, while our engineering practices have created an ensemble of standalone tools with broad relevance to visualization and visual analytics. We present our work as an example of applied visual analytics research, describing the design iterations that allowed us to move from experimentation to production, as well as the perspectives of the research team and the customer-facing team at each stage in this process.<\/p>\n","protected":false},"excerpt":{"rendered":"
The digital transformation of work presents new opportunities to understand how informal workgroups organize around the dynamic needs of organizations, potentially in contrast to the formal, static, and idealized hierarchies depicted by org charts. We present a design study that spans multiple enabling capabilities for the visual mapping and analysis of organizational workgroups, including metrics […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563,13554],"msr-publication-type":[193726],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-655761","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2020-5-4","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/abs\/2005.00402","label_id":"252679","label":0},{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/pdf\/2005.00402.pdf","label_id":"243132","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Darren Edge","user_id":31509,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Darren Edge"},{"type":"edited_text","value":"Jonathan Larson","user_id":32385,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jonathan Larson"},{"type":"text","value":"Nikolay Trandev","user_id":0,"rest_url":false},{"type":"text","value":"Neha Parikh Shah","user_id":0,"rest_url":false},{"type":"text","value":"Carolyn Buractaon","user_id":0,"rest_url":false},{"type":"text","value":"Nicholas Caurvina","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Nathan Evans","user_id":36539,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Nathan Evans"},{"type":"user_nicename","value":"Christopher White","user_id":31442,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Christopher White"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[734455,901101],"msr_project":[912408],"publication":[],"video":[],"download":[],"msr_publication_type":"unpublished","related_content":{"projects":[{"ID":912408,"post_title":"Graph AI for organizational analytics","post_name":"graph-ai-for-organizational-analytics","post_type":"msr-project","post_date":"2023-01-12 17:14:14","post_modified":"2023-01-13 14:15:15","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/graph-ai-for-organizational-analytics\/","post_excerpt":"New advances in graph machine learning paired with telemetry unlock a disruptive new ability to measure and reason about how organizations function. 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