How is the shift to hybrid and distributed work reshaping the very fabric of your organisation’s networks? And how you can leverage these changes to build more effective teams and drive business success? 

In this episode of the Digital HR Leaders podcast, David Green is joined by Michael Arena, a pioneer in Organisational Network Analysis (ONA), to explore how networks have evolved since the height of the pandemic. 

Listen in as they explore: 

• How organisational networks have evolved in a distributed work environment 

• Key findings from Michael’s research on optimal team size 

• How HR leaders can implement network analysis insights to drive strategic action 

• The role of AI in facilitating ONA and the future of network research 

This episode, sponsored by Workday, is essential listening for HR leaders looking to optimise team dynamics and embrace the power of organisational networks to thrive in today’s world of work. 

Workday is a leading provider of enterprise cloud applications for HR and finance, recognised as a leader in the Gartner Magic Quadrant for Cloud HCM Suites. 

Organisations ranging from medium-sized businesses to more than 50% of the Fortune 500— including Netflix, Sanofi, AstraZeneca, and Rolls Royce—have chosen Workday to build their HR systems and implement Workforce Analytics solutions. Join them and learn more at workday.com 

[0:00:00] David Green: Over the past few years, we've seen significant shifts in the world of work, from the rise of hybrid and distributed teams to the rapid advancements in AI.  But what impact have these changes had on the networks within our organisations; and how can HR leaders tap into these shifts to drive better business outcomes while improving employee well-being?  I'm David Green, and joining me today on the Digital HR Leaders podcast is someone who has been at the forefront of understanding these organisational dynamics, Michael Arena.  Michael last joined me on the podcast four years ago at the height of the pandemic, where we explored the impact of remote work on collaboration, innovation and productivity.  Today, we'll take that conversation further.  We'll explore how organisational networks have evolved in today's more distributed work environment, dive into Michael's latest research on determining the ideal team size, and discuss how HR and business leaders can use organisational network analysis to drive more effective strategies.  We've got a lot to unpack today, so without further ado, let's get started. 

Today, I'm absolutely delighted to welcome Michael Arena back to the Digital HR Leaders podcast.  Michael, welcome back to the show, first time in four years.  Please can you share your background and current activities with our listeners?   

[0:01:33] Michael Arena: Thrilled to be here again.  Always enjoy and appreciate our conversations.  Yeah, I'm a social scientist, so my background is in studying networks.  I've been doing that for longer than I like to admit.  And that's both been from a research standpoint and academia.  Spent a number of years as a visiting scientist up at MIT's Media Lab, which is where I really got engaged in network analysis from a practice standpoint.  But I've been in the corporate world as well.  So, Chief Talent Officer of General Motors.  I think we had one of the early People Analytics teams there that we launched.  I'd spent some time at AWS leading their talent activities and organisational research activities.  Now, I am back in academia as a Dean of a business school here in Southern California, so Biola University, where we are applying a bunch of network analysis stuff in our lab. 

[0:02:33] David Green: Obviously, Michael, as one of the pioneers of applying organisational network analysis to business and people outcomes, how have you seen organisational networks evolve over these last four years? 

[0:02:44] Michael Arena: Yeah, it's been really interesting.  I love this question, I love answering this question because the very nature of networks, they are super-dynamic.  One of the challenges we have with networks is we tend to study them as static interactions, a set of static patterns, but the reality is they're very dynamic and they change.  And I think what we learned through the pandemic is they can change rather rapidly.  So, you'll remember back in that particular podcast, and we've had many conversations since then, one of the things that happened immediately when we went to remote work was we lost our bridging connections.   

Most everybody's probably familiar with this, but for those who aren't, there are two primary types of connections that are really critical when we're looking at ONA.  One is bonding connections, and that's, how well are you connected locally within your local team?  How cohesive is that?  And it provides magic for speed and trust, and I'm sure we'll talk about that later.  And then, bridging connections, and those are kind of cross-departmental, cross-teams, cross-geographies, and that's what connects up silos to silos.  And what we found was almost by 30% month over month, as soon as we all went remote, especially here in the United States, we saw a 30% drop in those bridging connections, and it stayed that way for quite some time.  And probably when we were talking, we were still debating about, could you ever innovate in a remote environment, because bridging connections are really important for discovering new insights? 

But our networks evolved, and we as human beings figure out new novel ways of getting stuff done and new technologies, and we ended up reforging many of those bridging connections, even before we got back to the office, and especially after we did.  And what we saw was this evolution of bridging being a problem in the early days.  And you know, David, I only look at the world through networks, so I'll speak in absolutes, but it's only with this one dimension we started to see this burnout energy crisis.  Energy and a network is one of the greatest predictors of future performance.  And as we as human beings were isolated, relational energy wasn't flowing across the network.  And we saw that in burnout and quiet quitting and a bunch of other things throughout the second wave of activity. 

Then what happened was we started to return to office, which is kind of where we are today.  And what ended up happening was we re-forged those bridge connections, we never really lost the bonding connections when we were working remotely, but when we came back to the office, we sort of created new ones.  And what we went into was what I sometimes affectionately call, "The activity avalanche".  We were holding back a bunch of activity that was non-priority work during the pandemic and this remote work episode, and when we came back in the office, we reforged connections, we turned on the spigot of new activities, and we started killing ourselves, and we're still here today, from a collaborative drag or a collaborative overload standpoint. 

[0:05:58] David Green: Michael, you and Greg Pryor came and spoke at our Insight222 Global Executive Retreat last fall for the Americans, last autumn for the Brits out there, and you were talking about innovation, and you were particularly looking at innovation in this kind of hybrid and more distributed work as well, and you showed that there are certain points in the innovation process actually where things can quite happily happen when people are working remotely.  And you showed that there were other points of that innovation process where it's important to intentionally bring people together.  So, maybe wrap that into this question, when we think about today's world of work, obviously there is more distributed work, how is that impacting our networks? 

[0:06:42] Michael Arena: Yeah, I've got to add another dimension to that question, David, just to put it in context, and that is the velocity of work has seemingly picked up.  So, you use the word 'accelerated'.  I think we not only live in a world of distributed work, but with all the inventions around AI, augmented, automated, and certainly accelerated.  And that matters, because as we learn and cope in the network with new tools, the velocity of work picks up and it creates new problems in our social interaction.  So in short, if I were to really just do the CliffsNotes version of your question, we can work incredibly well when we're on task remotely.  Heads-down work is the easy way to think about this, you know, coding, project management, analysis.  Those things tend to work better when we're isolated and we're heads down.  And actually, getting back to the office has created pretty significant distractions.   

When you get back in the office, and I've done a lot of work with the Worklytics folks, I love their data sets, so leveraging this across thousands, hundreds of companies at least, what we see is when you get back in the office and you have anchor days, and everyone's in there at the same point in time, that makes sense logically, because you're going to have all these serendipitous relationships.  But if your head's down and you're trying to deliver and execute on something, it turns out you could be distracted as much as 40% of your time, and it has a huge productivity tax associated with it.  So, we need to be very thoughtful.  And you've heard me say this, I've said this very often, intentionality is what matters most.  And there are times where I'm in execution mode and I've got to lockout distractions.  And staying at home on that day or working with three or four people as we're sort of iterating together really matters.   

Then, there are other times at the beginning or end of a life cycle of a project or product development life cycle, you'd better get back in the office because you don't know what you don't know.  And the reality is, being live, full-body experience, high energy, we are able to influence better as human beings.  So, there are certain times where you have to be back in it, where it makes sense to be back in the office if you can; and then there are other times where I think you can actually lock down and execute with greater velocity. 

[0:09:10] David Green: Yeah.  For those that are listening, Michael does public publish a lot of research.  You can usually do it via his LinkedIn, even if it's published elsewhere, so I definitely recommend those of you listening that are interested in this to follow Michael on LinkedIn.  And leading to your research, Michael, you've conducted research this year on determining the ideal team size.  I'd love if you could share more about the research and some of the key findings, to our listeners.   

[0:09:39] Michael Arena: Yeah, so this is always a fun conversation, because we love to get into conversations about what's the perfect team size, and we can talk about this in all kinds of ways.  And many of your listeners have done analysis on team size and ideal spans, and what are the number of proper layers we should have in an organisation.  And I'm going to be here to disappoint everybody in saying that the real answer is, it depends.  Form follows function once again.  But we partnered with Visier and some of the work that they did, some brilliant work that they did, in looking at a very large data set of over 4 million data points, of what the proper team size or ideal team size would be for certain attributes, like team engagement and retention of individual employees.   

What they found was that functions all have different averages, which makes sense.  Like some functions and some sectors, like healthcare and support services, have rather large teams; operations, rather large teams; software development engineers and finance, surprisingly to me, are very, very small teams.  And you can think about that as heads-down, deep concentration work and speed.  And so, what we did was we partnered with them to say, "That's great".  And by the way, you should go read this research, but the ideal team size for those attributes is somewhere between six and ten people.  You feel you belong to a team, you're more likely to stay, and you've got better engagement.  And so, those are all really, really important.   

I would add to it, based on our research, your velocity is better, your ability to move with speed and agility is better with small, on Amazon we would call them two-pizza teams.  And frankly, there's great research out there that says that you could probably be more disruptive with small teams, because your ability to think big is much greater.  But there's always a caveat.  And the caveat is, what small teams don't bring you is integration, stability, and risk mitigation.  So, what we ended up doing was we partnered and looked at all of our data sets in conjunction with some of theirs, and started to look at like, is there really an ideal team, or is this a form-follows-function question again?  And should you start with, what are we trying to solve for?  If it's engagement, small teams help.  Not that you couldn't have engagement with the large teams, but it's just easier.  But if you're trying to drive stability inside of an organisation, larger teams actually help with that.  And if you're working on precision and repetitive patterns, larger teams, bureaucracy, if some people want to even call it that, actually help to stabilise things.   

So what we did, David, was we built this continuum that we call the Speed Stability Continuum.  And we've done some really cool work with some teams that are trying to catch up on the AI frontier, some tech companies actually.  And we looked at team size and what we found is, either they have way too many small teams and they don't have the right stability, synchronisation structures in the centre; or they have teams, like we had one team, one organisation that couldn't figure out why they had all the assets to be a pioneer on the AI frontier, but on average, 60% of their employees belonged to four or five different teams.  So, what ends up happening is, that creates this collaborative drag we were talking about before and it slows things down.  And that's where this Speed Stability Continuum is helping to unlock some of our thinking on this. 

[0:13:33] David Green: I guess that leads to the wider question, Michael, how do we use these insights, and then how do we measure the success of actually putting some of these into action and to outcomes?   

[0:14:49] Michael Arena: Yeah, I think you always start with what is the desired outcome.  So, when I think about these things, I get really excited about what's happening with generative AI, because I think it's going to unleash the possibilities with network analysis at a whole different level.  But one of the challenges with network analysis in particular is the context matters.  And you always have to start with, what's the outcome?  Where are we at today and what are we trying to drive?  So actually, Greg and I and Rob Cross, have actually created this what we call, "Context flywheel", and it's very consistent with all the people analytics work, like what's the desired outcome?  Then you go look at what insights do I need to find in order to better understand how do I achieve that outcome?  And then there's a cycle of, once I have those insights and I've served them up, insights are cheap, right, we all have insights and opinions, but how do I then take action, appropriate action, to close the gap on what we're trying to do and then start all over again?   

So, this flywheel that really starts with insights, then drives actions, then produces some output, and that output creates new insights, and thinking about that as a flywheel motion I think is really important for all people analytics, and certainly important for network analytics.  And I'll give you a real example of that.  So, a whole lot of movement around, "Hey, we've got to flatten out organisations".  So, okay, that makes sense.  We've got some bureaucracy, maybe too many layers, let's flatten out the organisation.  So, you make those moves and you de-layer, and what ends up happening sometimes, if you're not careful about it, is you start to generate insights that all I've done is really push the friction point or decision point to a different point inside the network.  And what I've really done is actually, in some cases, even slowing us down, because now I've got 20 managers.  If I just got rid of 20 managers, I've got 20 other managers or more who are bogged down and now have an access-point challenge.   

One of the things we've seen is, we were in an organisation that wiped out 30% of middle managers.  And by doing that, all they did was create greater frustration, greater dissatisfaction, and they actually added collaborative drag, because the managers that remained ended up having to work two or three hours longer just to catch up on a daily basis.  And ultimately, that turns into burn.  So, putting that in this flywheel context, what happens is you can iterate your way through that without over-swinging, which is oftentimes what's happening whenever we look at things statically. 

[0:17:37] David Green: I think that leads quite nicely to the technology question.  You've mentioned that you're excited about what generative AI could do for networks, and obviously you've mentioned briefly there passive ONA as well.  And, Michael, how does technology help facilitate the insights coming out of networks, and particularly, I guess, now that, as we've talked about, we're in the age of AI? 

[0:18:00] Michael Arena: Yeah, well I mean we could probably spend the rest of our time together on this question, and I think we're only beginning to scratch the surface of possibilities.  So, if I'm being really honest with you, I'm still trying to figure this out, probably like you are many of your listeners.  But the thing that gets me really excited, David, the very first thing, without being super-repetitive, is AI can help us to better understand certain things from the very outset, especially when we're using passive signal.  Like, I can catch you in the flow of work as an employee; I know what life cycle you are in your career stages; I know that you've been here 90 days.  And all of a sudden now, based on leveraging that through AI or looking at project management technologies, like some of the work that Asana does, we can look at where you are in some different stage of the project.  By catching that contextually, what we're then able to do is then catch the passive insights around the context, so I'm talking my flywheel again, right, and then drive the proper actions that close and drive the right output, and then start over again. 

The problem with what we were just describing is, if you're not asking all the right questions, you can steer the client in the wrong direction.  The reality is, generative AI will help capture some of those insights, not all of them, but help us to capture some of those insights so we can steer our analysis in a much more direct way.  So, it helps on the front end.  From a talent management, which is the space I've hung my cap for years, from a people development, from an education standpoint, I think generative AI and the things that we're talking about right now will be more disruptive than any place else. 

[0:19:51] David Green: So, I know this is something that you've been working on as well, helping organisations leverage insights for network analysis to enhance their talent management strategies.  What approaches should they consider?   

[0:20:57] Michael Arena: I'll start by saying, being someone who's spent their career in talent management, even though I've studied networks in the context of that, I would say that, and I'm probably going to insult somebody with this next sentence, so I'll apologise in advance, but I'll just say, "Get over it, and counter-argue me offline if you think I'm wrong", there has been very little innovation, frankly, in HR, and certainly in talent management, with the one exception of perhaps people analytics and all the work that's happened, that you've spearheaded and many others have over the last decade.  But I would say that the broad-based talent management frameworks and strategies have remained static.  Part of the problem is we've narrowed in on human capital strategies.  And I'm all for human capital.  Smart people with great experiences make radical differences inside of organisations.   

But the problem is, when you've got a lot of really smart people together, and then frankly in the future, whenever you're able to augment the bottom of that curve by bringing up people as newbies to the middle of that bell curve using AI, all of a sudden your ability to differentiate yourself in your career or your talent management strategies is nullified, because you've got a bunch of really smart people around you.  But social capital, I believe, is the great differentiator.  So, one of the things that Greg Pryor and I are working on is this whole concept of, how do we help to augment human capital with social capital in a talent management lens?  And to do that, we've been really collecting research.  I would have hoped to have gotten to this probably about three years ago, but this thing called hybrid work got in the way.  So, we've been able to refocus.   

The good news is now we've got over five years of data across numerous companies from Connected Commons.  And what we're doing, David, is we're thinking about modelling out your network across the life cycle of your career.  Some of this work was published and pioneered from the Amazon days.  Rob Cross has done a lot of this work on what we call, "Fast movers".  And what we know with fast movers, new people coming into the organisation, generally speaking, it takes me or you, depending on what organisation we're moving into and how strong cultured it is, somewhere between two to three years to be at the optimal point of influence then inside that network.  But these fast movers can do that in about a third of the time.  And so, what we've been able to do is collect more and more data and we're able to then say, how do you replicate what these fast movers do?  And fast movers build relational energy, which you've heard me talk about energy already; they create network pull, which is really important.  So, rather than coming in -- sometimes the advice we give to new employees is, "Go execute, deliver on results, get results done".  And that's actually good advice.  We all have to deliver results, but it actually can get them marginalised pretty quickly if they aren't doing that within the context of the social system.   

Maybe a better way of thinking about this is, how do I help you get results?  And all of a sudden, more people start reaching out to me.  And what it does is, I start to draft off of other people's social capital, and it pulls me into the centre of the network sooner by creating more incoming connections.  And by doing those motions, and you're sort of replicating those moves, you're able to accelerate the velocity of moving in.  So, the great leaders we've worked with and interviewed review their calendars on a quarterly basis, and they ask themselves, "Am I getting enough leverage out of this on-going, standing calendar?  Can I actually manage, decrease the inflow of work so that I can free up capacity?"  And Rob has done some tremendous work on collaborative overload, where you can maybe free up as much as 18% to 24% of your capacity.  And then you can start to think about the next move which is, how do I reinvest that more broadly?  So, I don't know what level you're working at, but if I compare your network footprint, who are you interacting with, who are the people you relate with on a daily basis, you're far more likely to be promoted in the next six months if it mimics the level above you.   

Again, I don't know that that's overly profound, but the profound part is we've been able to track that, analyse it, and then even generalise it across a multitude of different organisations.  And now, what we're working on are, what are those moves, now I'm getting to the practice side, the insights are useless unless you can actually take action.  And what we're trying to do is codify the network science over the years to plug in network moves, like if you're trying to build local trust, use the fast friends protocol, or if you're trying to influence vertically, apply the 3% influence rule, which is, "I want to find my PageRank people", and, "Who are those high influencers, because I can actually leverage their network to get stuff done?"  So, again, you can tell I'm pretty excited about this, but we believe that this, along with the context of AI to be able to actually usher in these moves in the flow of work, has the potential of radically reinventing at least parts of talent management. 

[0:26:28] David Green: And also, from an individual perspective.  I mean, what you've just walked us through there, Michael, is be as intentional about managing your network as you are about managing your skills, your performance and everything else, and there's kind of a symbiotic relationship between the two.  And then, as you said there, the technology, I mean we can envisage a time where maybe we've got an AI coach giving us, as an individual, giving you advice about your network, and where you should focus.  So again, if you've managed to get yourself in the centre of the network, as you said, if you're pulling back a little bit, it's really fascinating, it's quite fascinating.  Obviously, you've done the research over five years and you're seeing some of the insights coming out, but you think how that will develop.  And our next question will be how we can use that from an HR and business leader perspective.  But from an individual perspective, that could be really powerful in helping you manage your career. 

[0:27:31] Michael Arena: Yeah, well I mean oftentimes, I'll tell you one illustration of that, David.  So, career stalls.  Career stalls are almost always a network problem.  Now again, I'm presuming that you're capable and you were hired in and your hiring systems and all that stuff are great, like somebody picked you for a reason.  And certainly, there are times where skillsets get in the issue, so I don't want to overly generalise.  But I would say that in 90% of the conversations we have, a career stall has to do more with your network configuration than what you have or haven't delivered.  And generally, what that means is you've gotten bogged down into one phase.  When it comes to connections, more is not always better, and you've got to get very intentional, again, about the form-follows-function framework, only at the individual level.   

What we also see is with fast leavers, which I hadn't talked about, this works in the inverse as well.  Fast leavers are usually those people that never get that network surge.  They never figure out how to create the network pull.  They get themselves marginalised early by trying to be the hero too soon, and then they get pushed out to the edge.  And then, we start to describe them as an organisational fit issue.  But what it really is, is they haven't leveraged the right network moves to get themselves pulled in, so they either quit on their own, or ultimately are let go because they've been marginalised and they sit on the edge of the network. 

[0:29:01] David Green: Fascinating.  It's going to be fascinating how this progresses over the next few years.  So, thinking about what you've just walked through, Michael, how can an HR or business leader leverage some of these insights to enable employee success?  And maybe, what are some of the practical steps that they can take to implement these insights? 

[0:29:20] Michael Arena: Yeah, and I think both with the sort of advancements in AI, but also I think with just our understanding of the insights, I think our next job is to make this super-accessible and easy to step down.  Like, I'm not showing them here, but I can get up and you've seen them, David, and we certainly did it at the retreat last year, where we showed lots of network diagrams, what we can do is we can overwhelm the average leader and/or HR manager by showing all the statistics and dazzling them and that, but making it inaccessible.  And the reality is, we just need to know that we're in a different stage and we need to ask different questions.   

So, one of the things that a fast shifter does is they first start to monitor the intake of their calendar.  We can all go do that tomorrow.  We can all look at it and say like, "Are the people I'm meeting with on a weekly basis the people that are actually going to help me broaden out my mindset, because I've got to get to the fast-scaler move?"  I mean, if the answer is no, then it's just simple personal time management, you've got to actually help somebody.  So, an HR professional can help leaders, and great HR leaders do this already, help leaders coach themselves around their calendar.  And are they really creating a multiplying effect?  Are they scaling themselves one on many, or are they still operating in a one-on-one mindset?  And then, you've got to balance that with, you've got to be empathetic as a leader and you don't want to be distant.  So, you've got to think about all those things.  But a great HR business partner is already doing these things.  All we're trying to do is inform them with science, and then ultimately provide them with, here are the five or six high-leverage moves that anyone can go use, even absent the analysis insight of your own organisation.   

I'll give you a real example of that: PageRank people.  So, again, I'll get scientific for a moment.  PageRank people are your people that are connected to other important people.  So, David, I know this about your network already, you're super-connected to other influential people.  So, if I can get myself connected to three other high influencers, I can minimise the amount of effort I have to expend, and I can optimise my overall network footprint, because I've just connected myself to -- it's called the 3% influencing role.  I'm able to actually now reach 85% to 90% of the network through five or six key high PageRank influencers.  If you do that, now all of a sudden that works in both directions.  I can actually create incoming ties, but I can also scale new ideas more broadly by tapping into that.  And to do that, all I have to do is ask a simple question.  I could ask you or anyone I'm talking to, who are the highest influence people you know?  And they're going to give me three names.  And then I go ask that question again, and they give me three more names.  And then I do that ten times, and then I look at who are the three names most repeated and boom, I just did a network analysis in the everyday conversation, and now all of a sudden I can go partner with those three, four or five people and amplify my ability to scale myself. 

[0:32:45] David Green: So, how can organisations use workforce data to help drive culture, inclusion, and engagement? 

[0:32:52] Michael Arena: I mean, I love the D&I work that we're doing across the world, and certainly representation is essential, that's a starting point.  But I think far too often, we haven't thought about, what does inclusion really mean?  Well, it means I'm at the optimal point of influence.  So, I want diversity at the core of my network.  Then, I know that sometimes we describe that as a seat at the table, and what I really want is diverse folks at the centre of the network, or in certain bridge connections, in order to better influence.  And I think, so when I was at Amazon, one of the things I loved at AWS, it was they flipped the I and the D by saying I&D, so that you're driving towards inclusion, otherwise you're just creating a revolving-door approach.  So, I think that's certainly a network challenge.   

Culture is far more nuanced, so I won't go deep into this, other than to say we've done some pretty significant research on this as well.  And certain behaviours, certain values are caught, and other values are taught.  We'd like to think that we can teach all of culture, but the reality is some things need to be modelled.  It's tacit knowledge, it's implicit, it's very hard to describe.  And the best way to do that is also, in my mind, a network challenge.  Like, if you're in a small, tight, cohesive pocket, I can absorb and you can model behaviours and I can absorb those.  And there's this catching, contagion effect of those desired behaviours, and that's why leaders are so essential in modelling culture.  So, those are the things that come up for me as we think about inclusion and culture and engagement.  Clearly, the more you are in relationship with other people, the more likely you're going to be in positive, high relationship with them.  So, certainly there are network opportunities there. 

[0:34:48] David Green: Well, Michael, thank you so much for joining me again.  As I said, it's always an absolute pleasure.  Before we end for the day, can you let listeners know how they can stay in touch with you on social media, follow more about your great work, and please tell them about the Social Capital Compendium that you're publishing every month on LinkedIn as well. 

[0:35:08] Michael Arena: Yeah.  So, LinkedIn's probably the easiest place, just because that's where most of the work is synthesised.  We do this monthly, I try to do it monthly, a compendium where we're trying to really just -- I mean, I'm jazzed up about all the people starting to work in this space, and try to elevate all the great insights from others.  And then, I publish in places like HR Exchange and Process Improvement, kind of on a monthly basis, some of the research.  So, those are all great places.  But LinkedIn and Connected Commons are kind of the two central sites.   

[0:35:46] David Green: Well, Michael, thank you so much for being a guest on the Digital HR Leaders podcast again. 

[0:35:50] Michael Arena: Super-appreciated, David.  And thank you for all that you've done for the field.  It's just been remarkable being on this journey of the last ten-plus years of what's happened with people science as a whole.  And you've certainly been a core catalyst for that, so thank you.