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How to Use Data Analytics to Adjust Worker Collaboration Experiences

Throughout the COVID-19 pandemic, the way the world works has changed and it is still in transition. Businesses can easily find themselves playing catch-up trying to get a clearer picture of how their remote and dispersed workforce performs their day-to-day tasks.

As a result, collaboration analytics has become a powerful tool to provide businesses with more insights. “Collaboration analytics provides insight into how people might work,” said Will McKeon-White, Forrester infrastructure and operations analyst.

A catalog of services – such as Microsoft’s Viva Insights (part of its Microsoft 365 platform), Google Work Insights and Cisco Webex, among others – helps executives, managers and IT managers better understand how to improve the productivity and communication in the new status quo.

What insights can collaborative analytics provide?

Remote work models require a diverse range of collaborative tools, all of which offer unique data-driven insights into how people work.

Through artificial intelligence analysis of accumulated messages, leading collaboration solutions allow companies to better understand how employees share information, how tasks are collaborative, and the exchanges between departments. Analyzing employee activity in Google Workspace, for example, can help better demonstrate which services, such as Gmail, Google Docs, or Google Sheets, are most critical to employee collaboration.

Meanwhile, analytics from the video services that power hybrid work models can help organizations see when employees are working, how long they’re spending in meetings, or how stable their internet connections are.

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How can companies use Collaboration Analytics?

When companies leverage collaboration analytics to gauge workforce habits, optimization follows. An application should prioritize the well-being of employees. “Organizations should collect telemetry to ensure people have a decent experience,” says McKeon-White. AI can track instant messages for keywords that indicate low morale or job satisfaction, as well as potential signs of burnout, giving businesses the opportunity to step in and help.

On the other hand, messages from an organization’s top performing teams can be analyzed to better identify how they communicate and collaborate, so that their methods can be replicated across the enterprise. For example, Google Work Insights might reveal that a company’s top team members collaborate equally (i.e., there are no silent or inactive participants) in Google Docs in terms of time spent and number of changes made. This can avoid scenarios where, according to a Harvard Business Review study, up to 35% of the most successful collaborative efforts come from just 3-5% of employees.

Insights can show that successful teams have a high percentage of interactions with other departments on projects. Once these key metrics have been identified, an organization can then encourage all departments to adopt these practices and track their progress in doing so.

Another application of collaboration analytics can give insights into video meetings. Tracking their duration, frequency, and attendees can help companies make meetings more efficient, moving 30-minute recordings to emails when appropriate, or reducing time spent on tangential topics. . Organizations can also aim to reduce the number of large group meetings where many participants do not contribute because too many people make it difficult to speak.

Some tools, like those provided by Zoom, can even monitor a worker’s bandwidth stability, allowing businesses to ensure videos aren’t sabotaged by poor connectivity.

“They can then help employees optimize those connections,” says McKeon-White. “That way, someone with the faulty connection on the line doesn’t constantly feel like they have to troubleshoot instead of collaborate.”

Despite these benefits, businesses should beware of some barriers to using analytics.

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What pitfalls should you avoid with Collaboration Analytics?

The data provided by the collaboration analysis can be substantial. This is both an advantage and a challenge, as data overload can easily occur. Organizations can avoid this pitfall by taking a strategic approach to their analyses.

Determining in advance which data to prioritize based on desired improvements (more efficient meetings, deeper collaboration, or improved video chat quality) can alleviate data overload.

At the same time, analytics alone will not provide a clear map of the transition to more efficient work habits. Companies need to carefully consider the data they have collected and know how to successfully drive change across the organization. It’s not enough to say that team members don’t contribute equally to Google Docs. Organizations need to leverage their analytical insights into the action steps workers need to take.

On the other side of it all is the potential for improved productivity and efficiency, which is why so many companies are enthusiastically embracing collaboration analytics.

“We’ve seen a very rapid increase in the willingness to use these solutions over the past two years and an increase in the number of organizations experimenting with them,” McKeon-White says. “With the right set of systems, you can achieve some pretty remarkable results.”

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