Measuring Innovation in the Workplace

Below just the intro, an interesting piece our of Microsoft Research.   An example they say of using machine learning to derive and measure insights from connected data.  In a work environment that has changed considerably in the last year.    In particular interesting is how you do the measurement with tools like knowledge graphs.   Lots of interesting hints here about how they are leveraging their own infrastructure. Technical.

Advancing organizational science using network machine learning to measure innovation in the workplace

Published March 24, 2021

By Carolyn Buractaon , Senior Program Manager  Amber Hoak , Software Development Engineer  David Tittsworth , Software Engineer  Neha Shah , Viva Insights: Director of Solution Design  Jonathan Larson , Principal Data Architect

Is innovation another loss due to the global COVID-19 pandemic? Indicators reveal challenges to overcome—as well as opportunities to build on our collective experience gained in the last year. Measuring collaboration using network machine learning provides a powerful diagnostic tool to address issues as they arise. One challenge lies in how we build relationships in the workplace. With many transitioning to work-from-home environments, more flexible schedules, and new methods of working, there is evidence that interactions at work have been impacted in multifaceted ways.

REPORT

Work Trend Index 

This year’s Work Trend Index shows that the world’s information workers have begun a fundamental transition into a new world of work. According to the report, over 70 percent of global workers surveyed want flexible, remote work options to continue. At the same time, over 65 percent say they want more in-person time with teams. This future is dependent on utilizing new technologies for communication and collaboration, meeting the needs of individuals when it comes to motivation and mental health, and evolving ideas to accommodate modern workplaces—big and small.

As the world over the past year has changed in the wake of COVID-19, employees’ networks have narrowed. It’s important that we find ways to rebuild our wider work networks as we adjust to new ways of collaborating with others. Innovative teams have been shown to frequently have access to novel information from across their organization and to create new collaborations on a regular basis. Workers will need to tap into a diversity of ideas and viewpoints to foster creativity. The bottom line is that social ties and support networks are important to both our productivity and our ability to adapt at work. You can learn more about some of the takeaways for business leaders and workplaces in this Harvard Business Review article.

Within Microsoft Research, we are developing network machine learning to derive insights from connected data. In the case of the Work Trend Index, this technique can be applied to understand global patterns across Microsoft 365. Our Graph AI efforts have led us to develop tools and techniques to use connected data to service a wide variety of research application areas, such as tech fraud in the Microsoft Digital Crimes Unit, search in Microsoft Bing, and others. The graspologic open-source Python package, developed jointly with Johns Hopkins University performs partitioning, visual layouts, and network embeddings based on our research methods. Recently, we’ve been making advances in applying network machine learning to inform new solutions across Microsoft 365 and Microsoft Viva. In order to advance both theoretical research in Graph AI and applied research in organizational science, we have formed collaborations with research organizations, including Johns Hopkins University Department of Applied Mathematics and Statistics and the Michael G. Foster School of Business at the University of Washington.  ... "