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It’s been said that if you can’t measure it, you can’t improve it. We’re certainly not short on business metrics these days, as it gets easier and easier to collect and draw insights from data. It’s how we track productivity, employee engagement and more.
But what about collaboration?
Many organizations are starting to take a more intentional approach to collaboration, as the link between good collaboration and higher productivity, better results and greater innovation gets clearer.
But without the right measures of success, it can be difficult to tell if those efforts are paying off.
Why Should We Measure Collaboration?
Companies have been investing increasing sums in collaboration tools in recent years, particularly as they try to support a hybrid workplace. The amount of investment suggests there is a need to measure how well those tools are working, noted Jim Kalbach, chief evangelist at Mural.
“If you're going to invest explicitly and make collaboration intentional, you're probably going to need some numbers behind it,” he said.
But in their quest for metrics, businesses tend to be focused more on how collaboration tools impact productivity levels rather than how the tools support workers in their work — and they’re sometimes doing a poor job even measuring that, Kalbach said. That’s because while certain roles are more suited toward productivity metrics, knowledge workers, for instance, should also be evaluated by how work happens.
Earl Hoeg, workplace strategist at People First, said in the end, it’s more about the outcomes than collaboration itself.
“Collaboration isn't an objective in itself. It’s about the actual output, whether it's more widgets or better widgets, or better products and better service offerings,” he said.
Related Article: Why Technology Won't Solve Your Collaboration Problems
Start With the Data You Have
If measuring the ROI of collaboration tools is the goal, oftentimes the best place to start is to make use of analytics embedded in the collaboration software you’re already using.
“It is possible to quantify things and to have hard metrics, particularly in this day and age, when so much of collaboration is mediated through some kind of software,” Kalbach said.
Tools embedded into these platforms can provide analytics on how frequently team members communicate, the nature of their interactions and the efficiency of their collaborative efforts. Metrics such as the number of shared projects, frequency of communication and response times can be valuable indicators of how well collaboration is going.
There’s also a slate of next-level meeting assistants that can track sentiment, speaking time and other important metrics in real-time and offer insights (and advice). Microsoft’s Speaker Coach, for instance, offers private assistance in real-time and after a meeting by tracking pitch, pacing, informal speech, euphemisms, use of filler words, inclusive language and more.
Of course, there are drawbacks to relying solely on quantitative data. Just because there’s frequent communication or consistent meetings doesn’t mean everything is running smoothly. In fact, constant meetings can be bad for collaboration.
“I don't know if those metrics show why people are collaborating or what the quality is,” Kalbach said. “It shows what's happening. And from that, you can find patterns and then make inferences there.”
That’s where the qualitative data comes into play.
“You could do a survey and ask people a certain set of questions around their behaviors, practices and relationships with their colleagues,” Kalbach said. Such data can actually provide more insights into how employees feel about the level and quality of collaboration within and across teams.
The most effective way to understand whether collaboration is going well or not, he said, is to triangulate the two sources of data: quantitative and qualitative.
Related Article: It's Time We Revamp Our Productivity Metrics
Don’t Leave Collaboration Success to Chance
Metrics indicate what’s working and what’s not. But they’re not the be-all and end-all for optimizing collaboration and productivity. Kalbach said the key to making it all work is going beyond what employees self-report in questionnaires and the raw data you can extract from apps on meeting frequency and other patterns.
“You’re not on a high-performing team because you feel like it,” he said. “There are things that happen and cues in your environment with team members that should indicate as much.”
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This can include even small actions, such as whether team members say thank you to one another, share accomplishments or laugh during meetings. A lack of those signs can be concerning.
“If I'm on a team and nobody's saying thank you, nobody's laughing, nobody's volunteering for others, that's not a good team,” he said.
Analyzing quantitative data from collaboration platforms can help. Some tools allow users to visualize interactions between employees and teams to map out how information flows. This can help detect when specific teams or individuals might be overly isolated from the rest of the organization, paving the way for targeted interventions where collaboration is lacking and supportive practices for those teams that are doing it right.
Hoeg said that while he doesn’t know of a “magic solution” to measure collaboration, it’s a worthwhile endeavor.
“We're coming together as an organization because of our complementary skills and background and experience,” he said, “and unless we can collaborate and find ways for our employees to work more effectively together, we're not going to get the richness of that.”
