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Jan 10, 2023

If there’s one thing most managers will agree on, it’s the fact that they are not typically interested in analytics around things that happened in the past. They want their analytics to reflect what is happening right now.

But it is precisely because most engagement surveys are in the past that many managers are critical of them.

After all, why would they be especially interested in data reflecting how people felt six months ago?

Real-time data is where things are heading

The quest for immediacy is why tools like MS Viva – which provides new types of real-time information – are proving to be very exciting for people analytics pros and managers alike.

In particular, it’s information about how teams are collaborating that will be a rich field of inquiry.

Note: Before we go into the analytics, it’s important to point out that whenever we’re looking at employee data there are critical issues of privacy and governance that must be addressed. That’s too big a topic to go into here, but the fact that we don’t dwell on it is not meant to underplay its importance. Any analytics perceived as being about spying or micromanagement won’t be well-received. If, on the other hand, managers find it helps them then you can expect a positive reception.

For the rest of this we are assuming your people analytics team has privacy and governance issues well in hand.

Collaboration analytics starts to tell you much more

One of the easiest places to start in looking at collaboration analytics is to study how people are spending their time in meetings.

Here are some of the things you might look at – and what insights they might provide:

  • How much time is spent in recurring meetings? (Possible insight: “Look at this, my key people are spending a third of their time in recurring meetings. Is that why they are always behind on their real work?”)
  • How much time is spent in one-on-ones? (Possible insight: “I believe one-on-one meetings between the manager and worker are important but when I review the data, I have to admit I’m doing these far less often than I thought I was.”)
  • How many lengthy meetings are there? (Possible insight: “I know sometimes meetings need to go long but I know from experience that 2-hour meetings usually signal something is messed up, so let me see why we’re having so many of those.”)
  • How many large meetings (eg those with more than 6 people) are there? (Possible insight: “Large meetings are rarely effective for my team, why do we keep having them?”)

The interpretation of the data

The cool part of the analytics here is the interpretation of the data.

It’s not obvious what a good number is for any of these measures.

It always depends on the context. An appropriate number for one team might not be right for another. The ideal numbers will vary from time to time on a team; the nature of meetings at the start of a project will be different than at the end.

Talk through the results

The best way to interpret these numbers is for the analytics professional to sit down with a manager and talk through the results.

Over time the analytics pro will begin to glean best practices for different situations and will become a better coach, pointing managers to collaboration data that suggests where there is an opportunity to improve.

However, we think we will have to uncover those best practices through conversations; we simply won’t have enough contextual insight and accurate performance data to let some kind of machine learning algorithm find the best practices for us.

The analysis of meetings is just the tip of the iceberg in terms of what’s possible with collaboration analytics.

It’s a rich area to investigate. It’s also a great opportunity to spend time with managers talking about data they care about and can act on immediately.

If you can build an opportunity to do some work in this area, we suspect you’ll find it rewarding both for yourself and the organization.


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