EP021 - Using people analytics to create more productive teams with Anita Zbieg of Network Perspective
About the episode
This episode focuses on people analytics and the importance of metrics for better team operations. Why do we need to use people analytics to boost team engagement? How to use data to create better team operations? Why providing a visual data dashboard for managers can help with better team management? I discuss the topic with Anita Zbieg, founder of Network Perspective.
About the guest
Anita Zbieg - expert with over 10 years of experience in Work & People Analytics. Network, collaboration, and communication analyst. She’s done PhD in economics and psychology, applying ONA (Organizational Network Analysis) in organizational design. Over a dozen scientific papers on network analysis. She's built her experience into Network Perspective - an analytical software giving teams & leaders data with insights about teams’ collaboration habits.
About the host
My name is Peter Benei, founder of Anywhere Consulting. My mission is to help and inspire a community of remote leaders who can bring more autonomy, transparency, and leverage to their businesses, ultimately empowering their colleagues to be happier, more independent, and more self-conscious.
Connect with me on LinkedIn.
Want to become a guest on the show? Contact me here.
Quotes from the show
Remote companies search for more data & tools that can visualize how the work and collaboration is done. Few years ago task management tools did it for projects, or OKRs for goals, but there are still a lot of hidden areas of how we work, for example collaboration. If we have this visualized, we just synchronize better, and we don't need to ask others about many things. It is hard to see what is happening remotely on the scale of a team, business function, or company-wide outside of our own work. It is why employees and managers have different views on productivity. Tools that visualize remote work and collaboration can help in reducing this bias.
We see in the data that teams are working with many other teams, but a lot of the collaboration can be simplified just by contracting communications rules. On the other hand, collaboration might not happen because people don't have time, it is uncomfortable, or it does not have the right priority. The trick is to understand better how we collaborate and how we can manage and improve it.
Synchronous collaboration through meetings is great, and it's needed in a remote environment, but there are some thresholds. We found such a threshold at a minimum of 5 hours per week to be bonded to the team and sync the tasks. We also found that the maximum of this threshold is around 15 hours per week per employee, which results in three meetings 1 hour long, or six meetings 0,5 hour long per day. That is where the meetings may start to be contra-productive for the individuals and the team: as we don’t have time to prepare and take next steps. Also too many meetings are overwhelming employees with screen time causing zoom fatigue and tiredness, worry or burnout associated with the overuse of virtual videoconferencing.
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Welcome everyone. Yet another day to talk about the future of work and the future of leadership. Today we will discuss workplace habits driven by numbers, also known as people analytics. It is such an important topic, and I do believe that data will serve as a backbone for the future of work as we won't need to rely on impulses and theories and practices, but instead hard facts. I have Anita Zbieg, CEO of Network Perspectives to discuss this topic. Hi, Anita.
Thank you for the invitation. It's great to be here.
Appreciate of your time. Thank you for coming. So the first question is usually always the same for every guest of this show because this is all about remote work and future of work. So what was your personal journey? How did you end up here? How did you end up founding network perspectives and how do you end up in the future of work?
So about 10 years ago, I started a PhD in the topic of organizational network analysis, which is the approach of seeing, instead of seeing individuals independently to look at companies or other systems as complex systems and through the lens of network science and network theory. So that was the beginning. And then I was working in a consultant, advising medium and large size companies. While using still the organizational network analysis as a tool. I started this journey while gathering the data from surveys. So asking people who do they collaborate with or to whom they go to seek advice or knowledge. But about three, four years ago I started thinking about the software that can make this process much more easier and use the data from collaboration and communication company systems. So, for example, calendar emails or chats. And the pandemic. So it was a great, you know, time...
Best timing ever?
Yes, because the world has switched to remote work. You know, like in a very large scale, I would say.
Yes.
And we started building the analytics as network perspective software, and we ended up with work habits, analytics, and actions software that we provide directly to teams, because what we've learned through this, we, I mean me and network perspective team, what we've learned through the years is that democratization of the data from networks works best if it's spread across the company and if it's working directly with things. And the second thing we've learned through this journey is how to do the whole process with very strong ethics upfront, which may that you do not analyze individuals but teams and you do not run analytics for individual just for teams, minimum five people, which means that we hash the data on the flow, so change the names of people into the numbers, and we run analytics only on a team level, but it's still very powerful approach in terms of seeing and making the collaboration visual, even though it's quite complex to see it.
That's a great, great journey. And also again this show is more geared towards leaders who are making decisions in the future of work scene. So forgive me if I'm too practical here, but how does it work in a practical term? So I guess it is a software, right? People reach out to network perspectives, sign a contract, whatever engagement starts, and you pretty much install the software on the team within the teams in the company and starting gathering data from it, and then you can analyze it. And once the analyzation is over, then the, you know, they uninstalled the software or something.
The whole process is, you know, quite automatic. Like this is a cloud software. So we just sync with company collaboration and communication systems for the whole company at once. It takes about 28 minutes. Like we measured it last time, so it was 20...
It's very specific, not 30, 28 minutes.
We were really proud of it this less than 30 minutes. While we are talking with the system administrators. So for example, we connect with Google Workspace and Slack, and then the data flows. And after the first month the system works automatically, so it analyzes and distribute the data directly to teams, which means that you got the, I don't know, for 1000 people company that has 100 teams. You get 100 dashboards for every single team. After the first month with the first feedback based on the data.
How big are your clients? In terms of size because so I spoke with the other people analytics companies and experts before. And one of the first questions that arises is that, you know how big the data should be in order to give any conclusions, because sometimes obviously if you have a date chunk, and it's so small it's really hard to make any kind of assumptions because you know, the trends, the patterns are not there, and you might jump or go into a conclusion that is based on a small scale data, how big should it be to do any kind of, you know, assumption on how they work.
Like we work with companies that employ 250 people. So like the treshold starts here for a company size because it usually does not make sense to put this kind of analytics into smaller companies because everyone knows everyone else and people can synchronize and organize by their own. The largest company we work with employs 20,000 people. So that's quite a big, and but if you think about the size of a team it is five people to run analytics and give quite good feedback. The smaller teams we avoid, The are analytics of smaller teams because of the terms of data anonimization and if you have smaller teams, you can try to guess what the numbers mean in terms of individuals.
I understand, I understand it, and it simply makes sense. Also, I think it's super important to discuss if you are a growing company and you are, I don't know, you just got your series A you have like hundred people. Let's say 50 even. You might still face the same challenges and same issues with team collaboration as bigger guys and bigger companies. And I think, and correct me if I'm wrong, or you can tell me a really great examples here bigger companies, I know that everyone do think that they are super unique, I guess, but the trends are usually the same with everyone or at least similar to each other. So based on the learnings from big companies what can smaller companies can learn? So, I'm talking about, one example that I personally know is that everyone is organizing too many meetings, right? That's one of the most important insight that we had during the pandemic. That everyone wanted to have at least five meetings a day or even more. I heard about 10 meetings per day approaches as well. That was like hilarious. And and the collaboration didn't happen there. So, and the learning is like, like don't do it and do it a little bit better or asynchronously. So you don't really need the data evaluation to actually see that what's working and what's not. You just need the data evaluation to prove it that this is a trend in, in major corporations. Right.
Right, right. The data helps you to. You know, shorten the process of discussion.
Yes.
And decision making in terms of changes because they are more objective than opinions and feelings and you can discuss the opinion and feelings and the data brings you into the same level of discussion very quickly.
Yeah. So, yeah. So hard to argue with.
Yes. You can still argue with it. But it takes less time to discuss the topic and align with the topic.
And what are the trends that you saw in were similar learnings that data provided in medium or bigger size companies? If you can name a few.
So yeah, for example, what we see in the data, we see that the larger the companies, the more collaboration is happening. So that is one trend. And also Rob Cross in his research is showing this, that up to 85% of people's time can be spent on collaboration. And the larger the companies, the more alignment is needed, the more people discussing the topic and the more collaboration is happen. So this is one trend. The other one is that the collaboration level depends on the level in the hierarchy. So for example, people, leaders, or leaders of the leaders, they spend much more time on collaboration than individual contributors. Larger the companies, the more collaboration is happening on according to the seniority level. So for example, leaders of the leaders in large companies can spend up to 40 hours weekly on meetings. So this means no time for deep work or having deep work time after working hours.
Perfect. I don't want, I don't want to be the sociologist here, but but how do you define collaboration?
Like, based on the data we have, we define collaboration as the interaction generated by meetings. So common meetings or email or chat, you can add other sources of information here. So for example, collaboration for from called repository or collaboration happening in project management systems. But what you can do, you can standardize the collaboration across multi sources by transforming them into interactions, which means that being at the same time in one meeting, generates interaction between the attendees as well as having an email generates an interaction in the same way other sources. Of course, it's a little bit more sophisticated if you take some other resources, for example, chats. But in general you can standardize collaboration by creating interactions between people and teams.
And do you that's interesting because that's mainly, at least to my understanding, that collaboration is defined mainly in a synchronous manner. So do you measure any kind of asynchronicity? So let's say, People obviously synchronously collaborating in a meeting or through a meeting chatting emailing together. But for example, document creation, wiki creation, any kind of that, that's also can be a collaboration as well.
Yes, yes, of course. And you can also interpret the, for example, working on one document by few people as interactions between them and collaboration as well.
Okay. Okay. So can we conclude or point out any kind of trend that can be a good or a bad collaboration. Or for example, I'm trying to get to the too much meetings, for example because it might seem by the data as as a great way to collaborate. Right. But people don't have time for deep work, so, right. Again that means that most of the meetings that they have bad collaboration. I wouldn't justify like bad, bad or good, but you know, it's, it can be used for better purposes.
Yeah, sure. So of course, like synchronous collaboration through meetings is great and it's needed in remote environment, but there are some thresholds and we found such a threshold in terms of meetings, for example. And the first threshold is that it is good to have at least five hours on synchronous collaboration, weekly per employee, which means that you spend, you know, five hours weekly on meetings, for example.
So does the minimum, or sorry, that's the minimum, or does the maximum...
That's the minimum. To have this impression feeling and synchronization coming from talking to people directly. But the other threshold we found out is the maximum, like, it depends also on the roles you play in company, because if you are a developer, you do not synchronize with people so often. That compared to agile coach, for example, the core work of Agile. Talking to people. So it depends of course, on the role course, but in general, the maximum threshold we found out is 15 hours weekly per employee spend on meetings. And after this threshold some new phenomena starts happening, for example a collaboration overload, which means that we spend a lot of time with meetings, but there is also a mental fatigue. So it's a cognitive overload. We have too many topics on our minds. We have to switch the topics, and it's a fatigue for our brain. There is also a physical fatigue that we have individual spending too much time remote meetings because our eyes, our backs are suffering when we are spending too much time on meetings and we are just not as effective as we could be during the meetings because, you know, if you have more than three hours, spend meetings daily. Because 15 hours weekly, it means on average, you know, three hours daily, which you can transform into three meetings one hour long, or six meetings half an hour long. There is a lot of, you know, cognitive overload happening and people are just not effective on meetings in terms of giving really good information, feedback, decision making or just input to the meetings that are not engaged, and of course, all the things around.
Yeah, that's interesting. I think everyone can relate to these kind of too much meetings issues because everyone during the pandemic had so many meetings. Overloads because of bad management practices.
Yes. And you also it is, yeah. When you don't have time for the work, because you spend a lot of time on meetings, you are not prepared for the meeting. And you also don't have time to consume the meetings, so to move with the next steps and things you should do, and also in this term, too many meetings means that they are not effective. They are happening, but nothing is happening outside of the meetings because people do not have time for this.
Pre pandemic in office we had a saying, by the way, that meeting is not working. And do you do any kind of, so obviously data is great. You are collecting it. Insights provided by the data or sort of based on the data are also great. But the big question is all the time, what do you do with the insights, right? So usually obviously these insights can be actionable and there can be recommendations. So do you do any kind of consulting work on top of it? If yes what kind of actions do you recommend? Usually for others? Because obviously there should be a trend there too.
Yeah, we do not do consulting. We rather go into the path of building our knowledge into the software. So what we do, we provide teams not only with analytics and insights, but also with actions they can do based on the data. So data as a starting point, they data serves as motivation for change and some insights what you should focus on as a team. So which topic which work habits should you start working on at the first place. And that's the starting point because what we are measuring, we are measuring six dimensions of work habits. Three related to workload, and those are meetings, deep work and context switching. And three areas of work habits related to wellbeing, interacting bonding, crossing, learning, and work-life harmony. And the first question is, we, where, what are the work habits we should focus? And here the data comes with help. Not only to to, with the insights, what you should focus on, but also when you have this reference points for example, how much time spent on meetings is healthy reference point. You can see how far your team is from, from this point, so you can see and, and this helps you to make the decision, okay guys, this is the first thing we should focus on. For example, deep work, having more deep work, and then what is important? It is important to provide teams with actions, what they can do about it. There are few areas in which you can act as a team. So the first area is you can build work habits, like better work habits, micro habits at work. And the second area is you can make some automation. So for example, a micro habit is, let's agree with a team for minimum to hour long deep work slots and that's a micro habit. And you can also make some automations in this term. So you can just use your Google calendar and make the deep work session block out the time. Yeah. Yeah. Block out the time and make it recurring. You can also sync your deep work time in your Google calendar. We do not disturb statuses on Slack. And it all makes, you know, the process easy. You just have to click two or three times in your Google calendar and on a slack and schedule all this and make the process happening like with no effort. So the automation and helping teams with some tips and tricks in the terms of automation is also good thing. And then there is another part of synchronization of a team because if we will do some actions as a team members independently we are slacking. We don't have the synchronization within team, so sure, there are some tips and tricks you can use to synchronize better. So for example, we are showing the team visually a week of work in their calendars, but on a level of a team so they can see which are the time slots where we all are busy with meetings and which are the time slots that we can have a deep work. And it's a, a kind of help in terms, if you will choose as a team, the time slots for deep work when people are not occupied with meetings. It is much easier to, you know, make the change happening because of course people do not have to Change their old habits or and or make a, a lot of effort to make things happening. And in terms of synchronization, another thing is if you are able, as a team to agree on a common work slots, then it just works better. Because you do not disturb each other with some, you know, requests during your deep work slot. So this lens of synchronization, team synchronization are also quite helpful. And this is just about working within team, but really interesting things start happening when you think about cross team collaboration. And you have the data about cross team collaboration, and this is usually out of people's perception because they have the perception of their own work. Maybe they even have the perception of the work within the team, but that is hard. But in terms of how do we collaborate with other teams and which are the teams we collaborate most often with, or which are the teams we spend a lot of time on meetings with. This is when you think about terms of that, five for 10 people from the team are cooperating with different teams. It's really complex knowledge and the data can simplify it to you so you can find the root causes of, for example, meeting overload also across the company. Just by, you know, looking at the teams that you as a team are working most frequently with or meet most frequently with. And you can, for example, set up some routines, recurrent meetings with those teams instead of having, you know, one-on-one conversation across teams and things like.
Sure. It's especially true, by the way with with larger companies where they are already siloed where they have almost non-existing cross-functional teams because everything is, you know, mattering their own teamwork.
And what I think, I think that right now we are working on collaboration overload. But in time when Chat GPT is coming into play, when people are working from home. In time, we will be working on more and more collaboration, especially cross team collaboration rather than cutting it. Or the thing is how to prioritize this collaboration because what we see in the data, we see, for example, that teams are working with many other teams, but half of the collaboration should be simplified just by simplifying the processes, communications sources you know, knowledge and things like that. But the other part of collaboration is not happening because people don't have time or maybe the collaboration is not comfortable, but it is really vital for the business. So two sides of the coin, too much collaboration, but also collaboration that is not happening. And here the trick is how to understand this cross team collaboration and prioritize and, and work on it the way you would like to have it as a team.
And let's talk about the problems why companies are coming for you because I guess there are issues that they want to get answers for. And I think the cross team collaboration is one of it because I've seen it many, many times that I usually worked in the marketing department or the operations department. And obviously these kind of departments or like, don't call it department, let's call teams. These teams are usually cross-functional. I mean their role itself is to become a bridge between separate departments. So for example, you a marketing team, talk to the product team because you need someone from them to actually be able to sell and market the product. Right. And if the company has problems with product marketing, it means that there is no collaboration between the marketing and the product, even though that they claim there is, and I think the data here can actually be really useful because it just shows them that, okay, you think that there is collaboration between the teams, but in reality the data shows that there isn't so. And the reason why you have, for example, bad product marketing results is that you, the marketing team and the product team is not talking together. You are not collaborating anything. You maybe have a weekly one meeting and that's it. Call it a day, but nothing else. Right. So what are the big problems that companies choose to invest in data tool like you.
I think there can be few areas of challenges companies face. Like one, the simplest one is too much collaboration, like we are feeling overloaded with collaboration. But the other thing is for example, the lack of cross team collaboration and the care of an organization to, for innovativeness. So for example, companies that went working remotely, they just fear that they might lose some trust in collaboration and innovativeness this way because what the studies are showing, they are showing that in remote work we tend more to siloing cross team so we lack this long ties because it is, they have to be made by purpose. Like in the office they were happening organically. And even think about it the way that we learn for years, how to make the offices, how to structure the offices. The way the collaboration is happening. Right now, we have to learn how to structure the collaboration remotely to make a happening in terms relationships. And it's much harder because they are not happening organically. So that the next reason. And the third reason I would say is that companies need more and more visual tools while working remotely to understand, see and synchronize better. So in the offices, we could see on the screens of other people what they are doing. And in remote work, we switched into the tools for project managements to have visual workflow. So to see visually what is happening. Because if we have it visually done, we do not have to ask other people what they are doing. We just, you know, see the data. Yeah.
See the dashboard.
See the dashboard, and we know what is happening, what's going on. And the same thing you can apply to collaboration. You can, you see, know, have it visually, what is going on in terms of collaboration, in terms of our work habit and in remote environment it is really hard to know what is going on. That is the reason why employees and people leaders have different perception of, for example, productivity.
Yes.
The employees, they see their own productivity. They know what they are doing every day. On the other hand, people leaders, they cannot see it the old way, where they were in the offices. So we need more and more visual tools for what is going on in a company, what is going on within an across teams to understand and synchronize better. And that is also the reason why components are turning to us because we are making the collaboration visual and easy to understand. And it's helpful for people to do their job. And the other thing is that in remote work, in bigger organization it is really hard to scale work habits because they are, you know, micro habits. People need to repeat some things, work on them, and you know, doing it on a large scale is really challenging. And when you have a kind of, you know, visuality on what is going on, data and also actions, you can scale the work habits, good work habits, remote first work habits on large scale.
I knew that visual things are really important for people because most of the management styles that we know right now are connected to, I dunno, walk the floor and all the others. Right. And in remote, Well, you pretty much cannot do that. And I thought that maybe the wikis or you know, creating the workflow and documenting everything might be enough, but maybe a dashboard that would be really super helpful where you can see how others are interacting with each other. Well, obviously we don't, you know tracking and monitoring individuals mostly.
But yeah. Yes, yes. But you can get. A really simple insights and on a team level, like how much time do you spend on collaboration? How much time do you have for deep work as a team? What are the teams you collaborate with most frequently? From which part of organization you get the information, or most frequently. So, You can have many, many helpful things in terms of understanding and just knowing what's going on in a company, not only inside your team, but also outside of your team. And this gives you helpful. Yeah.
Yeah. It gives you confidence as. A leader. And that's, I think today's leaders, especially in a remote work environments they need that confidence because everything is around them with sort of in a crisis mode and you know, fire extinguishing and burning and flaming. So they need something that they can rely on and data and dashboards can be really helpful for them. Cool. It's around end of February, but still early in the year. And obviously you work in data, so you probably have a crystal ball at home where some, from where you tell some future telling. How do you see, how do you see the, the future of work in the next one or two years? What would be the main trends.
So I think that we are still in experimenting phase in terms of remote work. What we are doing, we are trying to find out what works best.
Yes.
And here I think that the important thing is the personalization of work experiences because we see it in the data that teams that are playing different roles in organizations have different work habits and different experiences and probably they will need to find out different ways of working. So this is one thing. The second thing is this visuality, like we will look for more and more tools that make this remote work more tangible for us in terms of not only our own individual work, because that's obvious for us, but in terms of how our teams are working and how our teams are working with other teams or with customers or partners from outside the company. So this visual tools would also be helpful, I guess. And also chat, G P Ts coming into the play. So I guess that we will interact at work more and more with chat, G P T. And then, and ai. And for example, it can simplify many HR areas. So, yes asking and answering the questions, searching the knowledge. But also, for example, gathering open-ended questions from surveys. It can be, you know, rising as a trend because ChatGPT is very helpful in making the insights from this kind of questions. But also what I can see even in our small team is that, for example, developers, they interact more and more with chat G P T and maybe in few years we will try to find some ways to interact as you know, humans with each other. That is why I told you that right now, maybe we are feeling that too much collaboration with other people is happening, but maybe in some years we will be looking for ways to collaborate more as a humans, because we'll be collaborating with systems or ChatGPT or other AI mostly. So, for example, interesting. When a developer, instead of interacting with a junior engineer, we'll interact with ChatGPT to create a code. Yes. Yeah. So something like this and the trend that is happening already is more and more data like companies are using more and more data to make this remote more tangible, and also this data is already distributed to teams because few years ago it was only used by a small people analytics team in a company. And right now the trend is to give the data directly to teams, to, you know, make their work.
And they, they can self decide. By the way, that's also really important. So that, so one of the, I agree everything that you just said because it's so great. But, but I also think that well, mid management has some issues, so because most of it, for example, in the data side it'll be so much easier just to, for the teams, To see how they are working in a dashboard and make efficiency related decisions just by, based on the data. And maybe use some external hat for support, but, but whatever. But that certainly there won't be needed anyone who's monitoring against reporting team related activities anymore which is one of the key tasks for, for midmanagers nowadays. So instead of these monitoring and reporting purposes they might need to step up and you know coach and support and mentor the teams on how to evaluate, for example, the data and how to make the decisions on the self-sufficient sustainable way. Cool.
Yes. And really hard part of work is the synchronization, you know? Yes. Across people and data and making the data visual, this visual management approach, it's really helpful in this. And the board, in project management system is the best example for it.
Yeah, totally. Again, this was a really great conversation. Thank you for coming here.
Same here. Peter.
Where can people find you?
On a LinkedIn and I'm also speaking at people Analytics conferences, so yeah, we can meet there. And yeah, you can reach me out just by using LinkedIn. This is the easiest way I guess.
Nice. Thank you very much. Thank you again for coming. This was really great.
Thank you for the invitation. It was pleasure to talk to you.
