In today’s episode of #O365 Hours, we’re joined by Office Apps & Services MVP Simon Denton to discuss the need for data-driven insights in organizations, how to pursue them, and where Microsoft Viva Topics fits into the mix. Watch our discussion below or read the full transcript at your convenience!
Guest: Simon Denton, Technical Product Manager at Mott MacDonald (visit his website here)
- Your team was an early participant in piloting Microsoft Viva Topics. What is the background for the need for data driven insights within your organization?
- Now that you’ve had Topics underway, what are some of your biggest takeaways about how your organization captures, tracks, and utilizes your content and data?
- What are your organization’s next steps for data driven insights, and for the Microsoft Viva solutions?
Christian Buckley: Hello and welcome to another Office 365 Hours. My name is Christian Buckley, and I’m the Microsoft Go-To-Market Director at AvePoint and a Microsoft MVP and Regional Director. I’m joined today by Simon Denton, a fellow Office Apps and Services MVP, and a Technical Product Manager at McDonald’s in the United Kingdom. Good morning, Simon. Or should I say, good afternoon!
Today we’re discussing the topic of data insights and Microsoft Viva Topics. So it’s interesting topic. I know that you have been involved with the pilot around Viva since it really started. So let’s jump right into that. Your, team was an early member of the pilot program of Microsoft Viva Topics. What is the background there? Tell us some of how you got involved in the interest of your organization and participating.
Simon Denton: We got involved through a chance encounter in the hallway. I mean, who remembers physical in-person conferences? I think the last physical in-person conference I went to, I literally bumped into a member of product group. We’ve got something for you. Anyway, a few months later we had a conversation and the thing they had for us was Project Cortex, which is the forerunner to Topics. And I guess the reason why they picked us out is quite independent of Microsoft and probably like many other organizations. We’re starting to work on joining people to their content and content that people based using taxonomy thinking, well, I’m a bridges engineer. I’m interested in bridges, bring me all the product-related content. So classify them, connect me to a group of people who are interested in bridges. I’m trying to solve a problem about bridges, bringing all that together. So we started to build our own sort of, I like to call it our Viva V zero which would then bring people together. So people to elect based on taxonomy, they’re interested in something that will join the content they’re working on to younger groups. So we’ve built that network within our office 365 environment, and that was working quite well. So once we have that chance encounter in the hallway, and then someone said, “We’ve got something for you,” and that’s the background. That’s the kind of reason why they came to us. And so, yeah, that’s how it all started.
CB: You know, it’s interesting. You would look at kind of the path that brought us to where we are and there’s, I know it can be confusing for people out there. Like suddenly there’s all these new brands that are out there, these new product names and how did the pieces come together? And I think like looking back at, you know, Microsoft’s path into this area, it really started by, you know, creating this, this cloud environment in making it possible to capture all this data. And remember both of us come from the SharePoint background and that side of things, and we’re fans of Yammer and all the social knowledge that’s captured there. But then you start to see things like Delve, which was, again, a way of surfacing and intelligently linking people and technology. You know, what did I recently work on?
What did Simon share with me that I recently worked on? And you could start to get some intelligence around the content. So I’m not going in wondering, where was I, where was I starting? What do I need to work on here? But the system could start to like, here’s the things that you were recently working on. Here’s the most urgent, here’s the new project that you just started and it’s relevant. Here’s the meeting that you’re getting ready to go into. And here’s all the notes from the last time that you all met together. And so that you can kick things off. And here’s a task that you had that you may not have completed in time for the status meeting. So it’s really about building intelligence around our data.
SD: So it’s all about building intelligence on the graph. Yeah. To, to use the expression say, yeah, Dell, Dell very much. I think it was in hindsight sort of pre vivre for Microsoft, right? That was, that was their first sort of people’s content, content people moment and the glue. I mean, don’t delve works really well, but the glue is to quite a sort of a simplistic level. And so yeah, it, it thinks those around you are most likely the people you’re going to work with next. And based on that, it thinks the content you need to see is this. And if you need to see others, you need to drill in and find it. Whereas kind of Veeva flipped that concept on its head and says, actually, this is the content you’re working on. I think you need to be near this group of people.
CB: But that’s always been kind of like that. If you think of Search, I remember you know, people that are really focused on search as a category and would talk about this. Like it’s, it’s not about what I’m asking the system to show me it’s for the system to understand, yes, you’re asking for this, but what you really want is this other thing, that’s the level of intelligence that we, that we want. It’s not just about going to the browser and using Google to search for something where you’re then limited based on, did I use the right words? Do I, do I currently have the right access or no, the people are interacting with the social connection within the graph. Do I, am I connected to the right people so that it’s going to surface that in the first 10 pages of my results or will it intelligently find its way through all those things to get me to where I really need to go.
SD: And I think that’s why I think we saw that shortcoming with our visa that we built. So we had people we built snappers and SharePoint. And what you could do is based on a taxonomy that as an organization that we thought would be the right taxonomy, you could, I don’t like this phrase. You could classify yourself about the things that you’re interested in the subjects you’re interested in, and then we’d have some automation that joined you to the right groups and in the right communities. But also then it will serve you just in time knowledge, based on auto classified content that people suggested. This could be a good bit of knowledge to share. And what we saw with that and when the data-driven insight came in was that of the nearly 700 terabytes of content we’ve got, there was some way 0.0, zero 1% of that content was actually shared and consumed through that process.
It was really, really small. So we thought, well, this, this the level of effort we put in to make these connections and actually the knowledge isn’t flowing, what haven’t we quite got right here? We’ve got the people to people, bet we started forming much better groups around knowledge and expertise. And Yammer was a great catalyst to that. But somebody not quite with that mind will knowledge. And as our SharePoint storage and content went up and up and up to give you an idea, our old DMS, we burned five terabytes per month, six years ago, we now burn 25 terabytes per month using SharePoint. So, their mind, their content growth, their guarantee is going up and up and up and up, but actually not many people were sharing and reusing knowledge. So, there was something awry there and there’s something we want to look at. And then we started looking at the numbers and that’s where Viva topics sort of stepped in to fill that for the piece.
CB: One of the number one complaints from people is that they would go and turn it on. And then there was nothing there; there’s no value there. And part of the learning, you know, the education Microsoft had to do around that was, well, you have to be in there actively using it. It has to be able to learn from that. You need to be able to, you have to go in and curate aspects of that. And then the system, the AI goes and drives the machine learning actually picks on those patterns and then gets better over time. And most people like didn’t give it enough time. And one of the things that I saw right away with Microsoft doing this limited pilot.
So, I, you know, there was just maybe, maybe a dozen companies globally that were part of that first effort for Microsoft learning. So, what was that setup process like, because I remember talking to you like a year and a half ago about your involvement within that. And you kind of made, I don’t want to put words in your mouth, but you, you kind of indicated that it took time to get up and moving it. And it made me think about, maybe we can kind of get into question number two around, like, what are your takeaways? What have you learned from this? But it makes me think all of this, of a conversation I had with a fellow and, you know, MVP, John White talking about you know, power BI and how people think, oh, it’s so easy. Let’s go build the report. Let’s build a thing. Like we’ll take this raw data. And John’s just like, people don’t understand the level of data cleanup and alignment that has to be done to get it. So that, that, oh, so easy tool on the front end can then provide those beautiful visualizations. So what did you learn about your data and really the process of moving data, raw data to knowledge?
SD: Yeah, you’ve made some really good points that Christian, because the one thing we saw through our, our work preview B the topics was the fallen opt-in versus sort of suggest out sort of process. So all of these things that we put in, we put in place, Microsoft put in place and the things that we built relied on people to opt in to do. And you can cajole from the top and you can drive from the bottom. You could do all that, that great change, but it was still required people to, to do something and actually opt into the process. You know, the opt into the conversations and Yammer opt to share that SharePoint document, or they think is a good bit of knowledge ops to curate that, but that doctrine and do the right things to make sure it’s visible and search opt to interact with Dell.
So it learns a little bit more white papers all or that opt-in. So the one area we learned was we had to find some way of actually suggest out, suggest out here’s a good bit of knowledge to take that burden mental burden away from people to talk to. The other aspect we found with is when we built, when we go to our station, the first time around is that we, we gathered 60 of the leaders, neutralization, thereabouts, and saturate unions come up with 10 terms that describes area of the business. Okay, no problem. We got in there, the 6,000 terms bank, but obviously people can’t do 60 times 10 or whatever. They can’t do that, but we kind of rationalize that down. And we, we ended up with sort of a master list of terms that describe, we felt described the organization and they didn’t have sort of duality.
We didn’t have concrete in one group in the concrete and other concrete, just one thing that appeared in one place. Right. But what we found when we enabled our, our, our sort of version of Viva was that not many of the terms are actually being used. They also classify no matter what rules we built into the process, that the terms weren’t really appearing, be much against documents. There there’s something odd sort of happening there and what actually transpired. And we only really learned this once we turned Veeva topics on and we sat back and we left it for a couple months, was actually the language, the business used to describe the things they do, even in industries that you would expect the language to be common is subtly different to the language that some people may think are actually being used. So when it goes it’s Delta, so we’ve got a Delta of topics coming from Veeva and terms of the term store. And now we’re starting to look right here. Okay. That, that same poor water pressure. Okay. That’s a geotechnical engineer. That’s most important to them, but they’re saying it like this is it with a hand to teach it, teach the models back. So yeah, that was one, one real sort of valuable part of the process, which you didn’t really get in the old system.
CB: But, but again, you talk about organizations that were really heavy into search and making the search was important to how their business internals ran that concept. I mean, when you talk about that, it makes me think of the difference between taxonomy and folks’ autonomy. So taxonomy being that structured the way that here’s the names for our projects, here’s the name for our products. Here’s the, the, our methodology across those things. The way that we describe things outwardly you know, about our business. And then as you described the way that people talk about it internally, you know, a project names, it could be the same project, but it might be like a, you know, a synonymous name for it, or just kind of a jokey, but an internal name for that thing. And the system needs to know that, that funny little quirky name that the team gave themselves relates to this project or set of projects around there. That’s where the folks autonomy it’s that user generated terminology and where you then need to go in and curate as an administrator and map out that, Hey, this is relevant. This is synonymous with ask questions. What is this other thing? Is it something separate? But that is, again, that is something that’s to a level that organizations had to learn to go and do some understood that inherently, but the majority of organizations, that’s a new net new activity.
SD: And to be honest, I’d rather be the net new organization I T I, and I, and that’s the reason why I say why is that? I feel that actually the, the AI approach has to be taken certain to topic generation is, is I think more accurate than the human generated descriptors at the moment that we’ve been creating. And as a result, I think you can, you can invest a lot of time and effort into actually, how, how do I describe my organization? How is it working? What you can to put all that time and effort, or you can let you turn something on and leave it for a month. And it will give you a pretty good description of your organization back. And I think other organizations could lead them the slightly more mature KM organizations as a result, because they’ll get to the clearer descriptors of their organization, much quicker through the process. And to your question, you made a point around how BI and data and all that unclean up the last thing, right? We’re all orders at the end of the day. And so, you know, I come from an organization of porters and I told you, 25 terabytes a month we put on now, because there’s just the drive.
CB: I just remember in the mid-nineties where we were, it was a very conscious effort of like, okay, we’re running out of space and this and hardware was expensive and took time to move in. And so we would actually make decisions about how do we want to split up data so that we could store it in different places. Do we want to purge cleanup and purge data? What do we actually want to store long-term we had to make decisions about that. Now the costs are like, it’s nothing less. And so let’s, we don’t know if we’ll need that later. So we can be in some ways lazy about what are we capture. Let’s just capture everything. Let’s hoard it, you know, let’s build it. But what is, I guess, the, the, the kind of the question around us, you know, is like, when does data become knowledge? What needs to happen for it to move from? I’m just capturing raw data out there until it’s actually knowledge.
SD: It’s normally, it’s a moment it’s used. Right? It’s whether it’s reusable knowledge or not. That’s knowledge that you’ve gained. Now, if someone then wanted to ask you a question of the data that you’ve got, how many horseshoe bats in boxes have I got across the organization? Well, that’s reusable knowledge that you’re kind of surfacing up. And I think that’s where I think that’s where the steps of knowledge get to. I think that’s where we’re kind of getting too, can you get a load of active boy term active knowledge, things that you need just in time to allow you to get things done?
And we want people to act if you share that with people, but because we’re all hoarders we don’t know what knowledge we’re going to need. So it’s, we’re going to keep a load of that. And so we’re now moving to Nero where actually let’s archive the knowledge that we’ve got. It puts it somewhere safe. So, we don’t even know the questions we’re going to ask about it, but we’re going to ask some questions of it in the future. And at that point it would come reuse or knowledge. So the next part of the journey we’re on is actually, how do we separate out what’s active people using what they need just in time to act to knowledge that actually we don’t want to press the delete key on, we don’t know the question we can ask here, but we need to keep it somewhere safe and have some descriptors around it. So help people find it again. So where we’re at, where are we gaining out? No, it’s, it’s building those archives. What’s in SharePoint, unfortunately. But you know, we’re putting this archives where you see storage is relatively cheap. There is a tipping point.
CB: Sure. Right. There’s always the, the gap between, you know, the, the there’s dumb storage and intelligent storage. Yeah.
SD: So we’re building, we’re building. What we feel is a slightly intelligent storage because we’re moving vast amounts of knowledge out of SharePoint into Azure, into Azure blob storage. We’re running cognitive search over it. We’re building up some descriptors and we’re aligning that back with the script is using the organization. That’s discovered by topics, your topics and the taxonomy organization. So we’re getting that glue back. So we get that 360 process, but, but we say it’s limited knowledge because we’re only indexing it to a degree because you want to keep the cost down because anyone who’s index content in their shoe and knows the cost, do that very quickly. And so we’re kind of working quite hard on that. And also, we don’t know the question that’s going to be asked today. People ask questions of knowledge in a very Bing, Google way. Tell me how many bridges, Warren trust, bridge, Google search go answer. Right. But what, how are people going to ask the questions in the future? And we don’t quite know that. So at least with storing the data in a way that people can answer, ask questions in a future sense. Yeah.
CB: So again, it’s, it’s just so funny. I go back to, like, my early in my career in the early nineties was, you know, a data warehouse management. And I owned the front-end applications if you remember, like business objects and other tools on the front-end micro strategy and you know, SAS and kind of all these other tools to go in there and do you know pattern recognition and knowledge management around this, this raw data, these massive libraries of content. And so I was, I was always in those conversations, it’d be like, okay, what is the business trying to ask? What is the business trying to understand out of this? And where is the data? Where does the data sit to be able to answer that question or allow that question to be answered? And so doing data joins between those and, and of course that was a much bigger deal, you know, back in the day.
CB: And I always joke that, like I managed this one consumer data warehouse that was massive, and it was 800 gigs and it was massive. And at the end of the process, when we manipulated, we added our GIS information, we added all this third party acquired demographic psychographic information about customer data with all of the location-based data added together. And we were 1.2 terabytes. And we were just like, wow, this is just massive. And now I was showed about it. Like, I have an eight terabyte external drive that’s just music.
SD: We’ve got cupboards. And you point to the cupboards and there’s petabytes in it. And, and I think, yeah, when, when, when sort of question that people, when we started to look at the data insight is around the cost optimization or project projects. And also there could be another downturn. So what, how can we ask questions of the knowledge and data that we’ve got to maybe predict the next sort of downtime the organization, how the organization reacted. You can’t really do that with data joins and SQL and a bit taxonomy that’s quantum territory, that’s content modeling and trying to work out how it all is there. So that’s what we’ve put in some of our content in the, in these show to allow us to do, to ask the questions, if we don’t quite know how to ask it.
CB: What I read from this is that we’re like, we’re still in the hoarding stage and we’re okay with that healthy, healthy hoarding. There should be a hashtag right there. Hopefully hoarding is part of slash you know, Eva topics. What are your organization’s next steps? Like where, where are you going from this? I mean, obviously not getting into, you know, deep into what you guys are doing specifically, but in general, what are the next steps for organizations that have been piloting it out, or you, as we get close to, to GA with all things Veeva and organizations around the world, it starts to slip into the mainstream kind of what are the, where’s it going from here? What is your focus?
SD: Yeah, so our focus at the moment is around looking at terms versus topics and topics with terms up that curation piece. I think that’s really important about bringing the quality of the knowledge that you have up to a level. So that’s one area we’re definitely looking at archiving and making sure that the in SharePoint is relevant. The content in our archives is hoard it’s suitably for remiss.
CB: So with both of those activities are very traditionally it pro centric roles. It sounds like what you’re saying is that it pro role remains important. Yeah.
SD: Some, some leader, Microsoft say data, every company is going to be an it company. Right. So yeah, if yeah, it’s going to be, it’s important. The reviewing of the tax on, that’s not an IC pro thing, you know, we do have now people who own those chunks of the taxonomy within the business and that aspect is going to fall to them. So they will look at that. We’ll just enact what they, what they ask us to do if they can’t drive the technology themselves. The archiving, yes, we’re leading the way with that only because we’ve got a slight cost driver that we want to want to manage first. But again, it’s a very user centric design user first, so, or consumer first time use, but it’s more consumer driven. So kind of find things, kind of reuse things. So these are the two things we’re sort of pivoting on is organization.
I think what we’re doing is really preparing the groundwork for, I guess, what is Microsoft 365 V next in probably about two years’ time when ITV was no longer an add-on hoping hope on hopes. You know, this is not a marketing thing. I have no idea what their plans are. If you want to empower every organization person to achieve more, this kind of technology needs to be core to the stack, ultimately. And I think when a two year two to three year cycle where they’re sharpening their pencils, making sure it all works and then maybe, maybe it becomes the heartbeat of multiple 3, 6, 5.
CB: And I can see that that happens again. I go, and you think about like the fast search acquisition and how it was this separate, very expensive, you know, additional server the solution. And then they slowly over the next several versions, integrating that technology out there, there still is, you know, search solution that you can go and purchase. There’s a skew that’s out there, but so much of it has just been, you know, added into morphed into an improving on, you know, the SharePoint stack that is within Microsoft 365. Like, we don’t even know that it’s there, we’re not talking about it as actively. I can see that about VBA, about somebody of these things, which should become ubiquitous. And just part of the, the way that the platform works and captures, or at least a lot of things like you talk about you know the curation process, the administration activities around all those things.
A lot of that at the very least, it may be that you’re, you still have to go and pay for an additional license to get these other advanced experiences, these endures the way that you’re consuming the data, but a lot of the inputs into an integrations with the core platform, that part needs to be ubiquitous. There shouldn’t be a lot of the extra work done. It should just intelligently be picking up this data, doing a lot of this chunking up of the data and integrating that in with the rest of the platform, benefiting from that, but then enabling a lot of those advanced features.
SD: Clear you can see that the curation is very human-driven still. And I kind of alluded to at the beginning of this conversation that’s probably where it goes a little bit awry, right? Yeah. Cause you’ve got to opt in to do it. Right. I kind of feeling, there’s probably some ML coming down the line there, kind of what I would imagine behaviors are being watched. Right. And then what, what AML, we run across this machine, learning out to you and help your curation. I think this is quite relevant. Do you agree type curation versus here’s 18,000 things, stop hitting the like button? I think that that could well be the next exciting bit. And I think that will really then start accelerating the relevance you that the script is because you still would’ve taken, you’ve taken a big burden and the job away you’re taking that off another chunk of the opt-in way and people will be running it through and you’ll be, you’ll be getting using the data driven insight, which is behind the scenes driving all this to, to, to educate the process or be through.
CB: Right. Well, I always like to use the 80/20 rule, you know, and if it’s doing 80% of the work and so that you can focus really on the 20%, that’s unique to your business. That’s unique to the patterns of doing, but, but that’s a moving target. Like the it’s constantly that 80% that Microsoft does, the AI provides that ML learns and adapts to is changing and evolving and growing. And so just as your, the 20% you’re focusing on continually changes because your, the system is improving and you’re as you move past and you get this you get your data cataloged, you have these topics it’s up and running suddenly the needs of your business, the things that your end users focus on, changes in evolves, the more that they get from the system, the better insights they have about the data that you have and what you do for a living. And then they start asking completely other questions that they had the inability to ask previously, because they just didn’t add the data. They just hadn’t. I know that this sounds so fluffy, but, you know, evolved in a way to get to the place where they get asked those questions.
SD: Like it’s a place quicker, cause it’s a concentrator, right, right. You and your history SIS loop. And you’re going to, it’s going to get tighter and faster and faster and faster and faster, right. It just, it’s going to really reach that point of sort of knowledge, singularity much, much quicker unsafe.
CB: That’s the question we need to ask is Simon of all of this, how close are we to the singularity? Will the terminators arrive?
SD: It’s funny though, when you see silence. Yeah. I hear some internal names. There’s some internal products. I mean, it’s, I get a project a while ago, which is with a broadcast company and we looked to the numbers that TV marks and they would share them to mobile phone companies to put their panels on. So you speak to your 3g, 4g 5g panels. They bring the space up. So we did this huge analysis, all the structures, what they could take, and then they would rent them out. And because the system was so good, we just used to joke that as long as was running yeah. Just you can walk up to the lights off as long as it’s running. It’s fine. It’s intelligent enough. It was intelligent enough to do the whole thing, work out space, put the, tell the engineer waits for the panel and the whole process. Are we there for other things we’re getting close. We’re definitely close with some of the things. Knowledge. Yeah. Engineering design, definitely parametric design is definitely a point where it’s starting to actually make suggestions.
CB: It’d be nervous when you come in and you, you ask this system to do something, some regular task, and it’s when the system says, I’m sorry, Dave, I cannot do that. They can hear the door lock of the automatic door into your office.
SD: Right. Queen wakes up. Yes, that’s right.
CB: Yeah. Little things. I think we’ve got a five, seven years before any of that happens before the singularity. So I think we’re good. Yeah. Well, Simon, thanks so much again, this has been great discussion. It’s always interesting to learn more, a deeper insights into, you know, especially companies and individuals that have been involved with some of the new products and the early pilots to kind of learn from that. It, I mean, I look at this as, you know, a validation of some of what I’ve read and heard about and learned about, but again, so much of this, it, it it’s, I can see the progression from where we were my experience with data warehousing in the nineties and into collaboration technology, the end of the nineties and early two thousands and the early versions of Microsoft graph and what that empowered for what the cloud opened up and enabled us to do at scale. I mean, all of these things that had to happen in this order to get to where we are today. And so it really is exciting. I feel like we’re in like a golden era of knowledge management again, where we’re so many things that we had talked about doing 10, 15, 20 years ago, we’re now just scratching the surface and able to go in and do it’s an exciting place. Yeah.
SD: Yeah. Most definitely. I mean, for me, I got into it like 20, 20, 15, 20 16. And for me the system to put those ignite slides in context, when, you know, Satya stands up on stage. And so here’s the graph, you know, empower everyone and you can get, how on earth are you going to do that? Right. And you sit, you see that. And actually, ah, I can see how this is coming together. There’s a substrate, there’s a knowledge graph.
CB: Recommend people go take a look at it. You know, Microsoft used to do these kind of home of the future office of the future. And they would show these videos that were very slickly produced and great music. And, and show this like this. I, I used to joke that this, the guy wakes up and he has this, just this piece of glass. There’s nothing on it, touches it. And it lights up with controls that he’s flicking things to the wall in his house and his bathroom, and then his car going to work and all this stuff. And I learned that lesson, that one in the future buy stock in Windex because everything’s glass and somebody they’re wiping things down. But you know, and we laugh about a lot of what we saw. It’s amazing to go back and look at some of those early videos; how much they delivered on what’s there today is amazing.
SD: How does it know that the, that you’re sewing in front of the fridge and the thing the front of you that you need in front of you on your pane of glass is your shopping list. It is, it knows that from taxonomy, right? Fridge food, all the time, someone say your foods, you need somewhere to categorize it. You say what he wants you to curate that list, right?
CB: Every time you open the fridge, it’s scanning your facial expression. It knows if you look disappointed, it knows that you’re out of something important, if there’s something to add to the list, to go and order it from Amazon, etc.
SD: Yeah, yeah. To mention the, a word, I guess we’re allowed to that the whole, the whole premise of Amazon is taxonomy. If you want to see anyone who’s been successful with knowledge management, with classification of content, for understanding the relationship between people and the content or the products they want to consume, spend 10 minutes on the animals and right.
CB: Yeah. So there’s so, so many lessons that we can learn from the competitive sphere that’s out there. And there’s things certainly that Microsoft and Amazon and Google and apple, and you know, all of the, the big well-known tech companies are doing some really interesting things, you know, but one thing is I, you know, being within the Microsoft ecosystem, when you talk about, you know, the relevance to the enterprise and that’s where Microsoft really stands apart and looking at those different things. So there’s always things that we can go and learn from those, those other places. But it’s how it, you could actually use that in your day to day within the business. That is a huge advantage.
SD: I agree. Absolutely agree.
CB: So, yeah, just bring it back around for two MVPs talking to that where we have to give props to Microsoft. Yeah. That’s not it at all. It’s like, I am here, not because, you know, I’m a shell for the Microsoft sphere, but because I have seen really positive things that have done that I’m passionate about, you know, the Microsoft space for the value that’s being driven. And I, you know, I’m excited about getting, it sounds like I’m Schilling some more here, but for around Veeva I’m you know, so I’ve been kind of a skeptic on it because of all the promises made by other companies and throughout the history around this, I’m very excited to see the progress that’s been made in this space. And I know that there’s a lot more to come.
SD: That’s it. And I think you should be just be patient with it when we, when you were definitely, I think that’s one of the things that, the new thing aren’t, you just turn it on it’s can work, you know it’s, you know, I think be patient be along with the ride or recommend anyone to get on the journey soon. That’s not sales pitch, I’m not under commission or anything like get in early. Cause I think you sort of understand that the journey they’re on and also, I think they’ll help you probably shape if you don’t have a cam strategy or idea, it might help your next two to three years strategies. He is sort of starting to line yourself up. So I know KMS, like a slow moving supertanker, right. Takes a little while to turn, so gather, get that process set up and running through. So yeah, I, I do have great hopes for it. I am pretty, pretty positive. In fact, that’s my calls in the next seven minutes. Isn’t the next insight into what’s happening.
CB: Yeah, there’s a few things going on. Well, Simon, so thanks again for joining me on the call and thanks for everybody who watched this episode, remember to please subscribe to office hours and get notifications of the upcoming shows. So there they run on the first and third, Wednesday morning of every month at 11:00 AM Eastern. So thanks everybody for watching. And thanks again, Simon.
SD: Thanks very much.