[Music] okay good day and good morning everyone um it is indeed with a great pleasure and that i'm able to see so many people joining here today when i created this this session or this webinar i had no idea just how many people would actually join in the end and in matter of fact today we have over i think 500 people almost signed up on the linkedin event itself so that is absolutely fantastic and of course today's topic is pretty straightforward it is about servicenow discovery and it's a very very simple question which is is it worth the money um and before i jump into the actual topic itself i'm just gonna set the stage a little bit because i have made an observation so first of all there is a lot of questions around servicenow item when it comes to the value like i went out to google on reddit and so forth and i've done this many many times and when you start searching for the information about the special roi the value does it pay off or not you find so many people asking questions about how to quantify it what are some tips does it really pay off in the end and so forth and so on yet there is very little material to be found specifically on this topic of course servicenow has their calculators you also have some reports from forester and so forth but really to find this kind of tangible material is quite difficult and that is kind of the basis why i selected to create this this webinar here today and we are able to see this through the fact that i mentioned that almost over 500 people when i checked today have signed up on the linkedin event and that is absolutely mind-blowing i had 1 500 visitors to the linkedin event so that also tells me that there is a huge interest in this and finally i've had over 30 pre-submitted questions so clearly there is a lot to be discussed and unpacked here today and i'm going to do my best to do so and you might now be wondering who am i who are speaking well my name is alexander jungstrom and i am the founder of nrn partners which is a firm that is only focusing on one thing and one thing only which is servicenow item and the strategy around that essentially and you might sometimes hear the the phrase aiops being used in place of item and throughout my career i have now worked approximately eight years with the servicenow item product i have 12 years if not a bit more of experience in item as a general topic and a matter of fact i used to work directly at servicenow in their item team quite a few years ago and i am very very passionate about item and kind of how it truly can be used within an organization and how it can provide value so that is the things that i am essentially pondering every day about um now i was supposed to have a moderator with me here today that is unfortunately not possible which means that i am running this webinar completely by myself today so i will have to try and do my best in order to multitask and really kind of um yeah do my best to keep keep track of the chat and try to moderate this as i go i just want to set a few expectations some good information to be aware of before we get started for example i just received a question with every webinar will be recorded or not and absolutely it will be recorded you will be able to find it on the anal and partners youtube channel and more importantly you will also get the recording sent to you also if you have any questions during the webinar there should be a chat and there should be a q a box where you can write to me obviously it might be difficult for me to reply to each and every question live during the webinar but because this is such an interesting topic i will personally get back to you if i don't have time to do so however i am not a sales representative so this is a little bit sensitive when we speak about return on investment measuring roi financial data values etc i just want to be very clear that i come from a technical background i am very very enthusiastic and passionate about the value and roi but i am not a sales representative from servicenow so obviously you should take today's webinar as the opinions are on my own and based on the observations that i have done and also to set expectations today's webinar is not about licensing or pricing as always this information is best fetched directly through your servicenow and sales representative so i will not touch upon licensing or pricing of the product or go down that rabbit hole because yeah then we would be sitting here all day and finally this is not me you know trying to convince you today that discovery is the absolute kick-ass product that you have to invest to so you will not be sitting through a sales session here today but really what we'll be doing is looking at data based on real experience that i personally have gathered together with me and my team and in my previous jobs so just to to set this in perspective i think i have guided and led right now over 30 discovery rollouts on a global scale um so yeah obviously um i have been able to see patterns i have been able to see commonalities and that is what i want to share with you here today and for those of you who are in a bit of a time crunch i usually don't like always when there are questions and then you have to wait all until the end to get answer to the question so i'm just going to drop that off right now is servicenow discovery worth the money yeah probably but there are exceptions to it and that is what i want to discuss and touch upon today among other things um and speaking of what i will touch upon then today um i will try to structure it in the following way then so we have right now approximately 50 minutes left and i will start speaking about the value because it's really important for me that in order to understand the roi and how you kind of measure cost reductions the financial impacts etc you also need to fundamentally understand what value are you looking for when you start with servicenow discovery so i will speak about that different type of iot organizations and i will see you a value matrix where i will show you a value matrix that we have developed and i think it's a really easy one that you can use yourself but then of course more importantly is the measurements so measuring roi some of the impact factors when we're speaking about roi and also then when it comes to implementation so when the project is actually kicking off how do we ensure that we don't cannibalize on our future roi through having a disastrous project for example and then of course benchmarks as i mentioned we have done many many discovery rollouts and based on these rollouts we want to share with you here actually first time ever some of these discovery benchmarks yet again it's not about the cost it's not about the pricing benchmarks but it's more around how long time does it take to implement discovery what are some common challenges timelines etc and hopefully this can help you or your clients um plan their discovery and coordinate it a little bit better and as i mentioned feel free to ask any questions as we go here so there should be a chat and the same if you hear that maybe my sound quality drops or something like that just let me know because yeah i'm kind of having a monologue with myself right now okay excellent let's get down to it then so as i mentioned the value is super important and when we look at value it's very easy to get caught up in this kind of salesy hype bubble or oh it's the best ever it provides you know great values etc etc and you know big inflated numbers and i want to take that down a notch and really start looking at how we can estimate in value in a realistic way and before we start estimating the value we first need to understand where in the maturity scale our i2 organization is so here is a scale that i have developed and it starts then from old school let's say it organizations so the old-school iit organizations they tend to have their own data centers a majority is internal i.t they tend to have quite a lot of legacy they might not even have a cmdb perhaps it is scattered around in excel sheets could be spread out in different tools maybe you have network equipment in solarwind and you have sccm for your clients and servers and whatever but then fundamentally what we're looking at tends to be a low process maturity and rather fragmented iot operations and you might think are people still there today and yes believe me i am often meeting organizations who are still kind of you know i'm calling it old school but they are still on this side of the maturity scale and then we have what i call the hybrid and let's say i2 organization when it comes to maturity so this is where you start seeing a mixture of internal i.t with some outsourced iot there might be some cloud activities going on perhaps you're using azure perhaps you're using aws etc they tend to also already use itil and itsn in one capacity or another so the usual incident changed etc but nonetheless the struggles then are often focused on still legacy systems still a lot of you know old tools in the i.t landscape there might be a large sprawl and when it comes to cloud resources outsourced resources etc and then finally governance and when we speak about automation it tends to also be rather low now cloud native iit organizations then and they tend to be heavily outsourced and when i say outsource it's not necessarily that i mean that you know they they have everything with ibm or something like that but pretty much that their own internal i.t and on-prem infrastructure etc tends to be fairly low and these type of organizations also have often in one capacitor another a cmdb in place then and i often also see here devops initiatives and active cloud projects and this organization tend to run a sort of hybrid way of itil so there are some parts of the the organization which is using itil practices there are other parts who are using devops practices and then there are maybe some some small isolated let's say islands within the iot organization that are very mature and they are looking at machine learning site reliability engineering that type of thing and then finally we have what i call the cutting edge iit organization so this is an iot organization who truly embraces data they live and breathe data and they have their devops cloud ops all of these things are in full swing and in full operations they also tend to be very skeptical about itil in my experience because they think that is a thing of the past and that is not a thing of the future which is a different topic to discuss and i also often see this monolith monolithic infrastructure is gone um but rather they're using a lot of different infrastructure as code everything as a service that type of thing and interesting enough i often see that the fte count is fairly low for these type of iot organizations so they are able to do a lot with few amount of people and in general the iit organizations tend to be more flat so why am i mentioning these things it's not necessarily black or white that you are one or the other if you are a global company it is very possible that you have a mixture of this depending on where you look but it is important to ask yourself this question and assess where you are because historically seen up until approximately two years ago then i would say that when you hit this kind of cutting edge side um of the iit organization here that is kind of where service now discovery were not able to keep up and what i mean with that is two three years ago servicenow discovery would not be able to do all the cool devops thing to integrate with kubernetes docker microservices etc etc um but that has changed and today i think it's fair to say that servicenow discovery as a as a product and platform um is able to cover the entire range of spectrum here um so this is good to be aware of and it's really good as i mentioned before to ask yourself this question where is your i.t organization here um on this scale and as i said it could be regional differences and so forth depending on the size because this will determine what type of values you can expect from discovery all right and that is what i want to start off with speaking about now so this is a value matrix for servicenow discovery you will not be able to find this somewhere else and i am a little bit unorthodox when it comes to how i look at things in life and especially let's say i t and servicenow item so this might not fully resonate with everyone and i fully respect that but this is something that i like to employ and use in my daily work because i like to simplify things and i like to take something very very complex like discovery and looking at the values and simplify them so we have two axes here the first one is measurable roi so how tangible can we actually measure things when it comes to implementing discovery so if we do a before and after snapshot how tangible is the roi how easy is it to actually measure and then on the other side on the left side we have governance and i'm going to come to that in a bit so when something is easy to measure in roi it means that it typically pays off in numbers so this is where you have direct cost reductions all right so it's very easy to measure and there are methodologies of doing so on the other hand in the top here is where we see things pace off in a culture and this for me is hugely interesting because we see more and more let's say in that area today the more we modernize the less tangible the roi actually becomes because the differences from a technological perspective is not always huge there are small incremental steps but the differences in cultural aspects for the iot organizations can be enormous um and this is what i like to call indirect cost reductions so it has a secondary effect and now you might be wondering all right but how how can we place things here well i'm going to show you an example and this is very very easy to keep in mind so when we speak about things which are low in measurable roi so things are not always very tangible they tend to be the first level data ownership so if you today don't have good data ownership discovery tends to help there a lot so essentially you know who owns what within the iot organization or within the cloud or whatever it is and as you can see here the governance that that is what you least should expect from a governance level ownership but at the same time just because you get ownership i mean you cannot measure that in direct numbers always how does that help us right and then we have data quality now we're starting to get higher up on the governance scale here so when you have good data quality which by the way servicenow discovery obviously brings a standardized way of representing the data and the infrastructure then that is a huge payoff in culture and then finally we have data context so that is different from data ownership because that is who owns what but data context is basically how is it used and for those of you who are a bit well versed within itom you might now think but hey alex isn't that service mapping that's not necessarily what i mean um data context can also be discovery for example bringing in tags you know if you do cloud discovery you get a lot of tags and based on these tags you know okay is this used for a development environment is it used for a production environment what application is it used for whatever it is and it could also be other type of data context like relationships etc etc and this data context in combination with data ownership is often used then for internal billing for example so i hope that makes sense and i hope you can agree upon that even though you get these things it's difficult to directly measure roi obviously it has a value and you know that but in straight numbers it's it's a difficult not to crack now here pace of the numbers what do we have there then uh consolidation absolutely a huge thing very easy to measure in numbers if you can kill off a few tools then obviously um that is very very tangible right and then finally here we have process improvements these five factors whenever you ask yourself about value of discovery whatever that might be whatever use case you have they will fall in one of these five factors or five areas so process improvements consolidation data ownership quality or context i promise you you will be have a difficult time finding another exception and if we're looking here on process improvements it has a huge value in culture so indirect cost reductions and it also is very easy to measure in terms of roi so use this value matrix that is what i do all the time and it tends to work out really well for me at least now let's speak a little bit more tangible about what what does this value matrix mean what could be some examples when we speak about these different areas then so direct cost reductions here we have then the two areas consolidation and process improvements all right and on consolidation like i mentioned before if you phase out existing tools for example then obviously that is very easy to measure in numbers if you can kill off especially if you are a large organization if you can kill off the contracts for a few other tools and replace it with one unified tool then yes that is absolutely a huge benefit and it could also be phase out tools in terms of relying on excel and that brings me to the second point a huge part of organizations today still rely on excel um and it might sound silly but it's true and if you can reduce that that manual maintenance of sea ice manual maintenance of data etc obviously it has a huge impact on the p l and bottom line because people are essentially spending that time in a way way more productive way hopefully driving up the the profits and efficiency and then something else which is more recent is cut out integrations so actually um let's say eight years ago or five years ago when i worked with discovery i would say that 80 to 90 percent of discovery what's the traditional protocols all right so what i mean with that is like ssh snmp these type of things the traditional discovery however these days with cloud with kubernetes you know docker etc etc discovery includes a lot of api based discovery so in practice what this means is that if you invest in discovery you can hopefully cut out a few integrations that you otherwise would need to rely on and this is especially true in my experience if you today or have plans to let's say create your own integration somewhere so custom-made integrations over rest my experience tells me that when you have more than three data sources over rest that tends to be more expensive to maintain over time than discovery this is though true for mid-sized companies keep that in mind so i'm not speaking here like a csv import or something like that but if you go down the rabbit hole and create your own integrations trust me it's an expensive story and you might now be thinking well isn't there this new thing called service graph where there is plug and play integrations from different vendors directly to the servicenow cmdb that is true but keep in mind you have to pay the discovery licenses for that so you might as well just go for discovery then all right um process improvements then we have the usual suspects here mttr root cause analysis faster onboarding time if you don't have you know five six different tools but you have one tool to look in it goes way faster to onboard people but then we have the single pane of glass as well this is a huge value benefit that instead of having this spread out tools um you essentially just look at everything through a single pane of glass and i bet you've heard that story before i mean that's that's one of the main philosophies of servicenow i don't but we have something else and this is what i call smash smash the stack and i'm going to speak about that more in a little while but um it's basically you know when you try to find a needle in a haystack of an issue that is what i call smash the stack and it comes from that netseq or network security and yeah i'm going to speak about that in a little bit so those were the direct cost reductions but then we comes to those indirect cost reductions then so how do you quantify culture in i.t um that is more difficult this is typically where we speak about things like i.t workforce perception like if you have a good discovery tool people can rely on the data things are up to date they don't feel you know frustrated etc and then yeah obviously the perception is way way way improved the company reputation i see a lot of different companies who employ people straight out of university and these university you know students or whatever you want to call them they are used to using very modern things in their daily life and then they start at some company and things are ancient and that's simply not good for the reputation so it has a huge benefit there and then finally conformity when things are done the same way across geographical differences across different teams etc you get a degree of conformity and that sounds a little bit dystopian maybe but that's actually a good thing standardization and consolidation and also in culture how you work with things because that allows for interoperability between teams so if you don't have too many different tools it's easier to just work together pretty much um and i just want to mention here that i'm not going to speak about how you measure these things because that's an entirely different beast to tackle there is this one thing called happy signals which do this um they measure a lot of like employee happiness etc i'm by the way not affiliated with them at all neither is a rm partners i just know that it's it's a decent product so um you might want to check that out all right so that was so far about the values then um now we're gonna come in to the measurements all right and this is where it gets a little bit um let's say trickier because how do you measure these values and i think this is the the area where a lot of people struggle essentially and what i want to start off to speak about is the discovery project so when we are looking at a discovery project there are two main components if we're looking at the financial side of it so we have the implementation phase and then we have the continuous adaption so meaning when you're actually using the product then during the implementation phase that is typically where we see the capital expenditure or the investment into discovery um and this phase and i will speak more about this later it usually takes something like three to six months and a lot of people here think that in order to save money and get good roi we need to go for the absolute cheapest implementation um you know save money on consultants etc but that is actually not true in my experience during the implementation stage consultants is not the the expensive part in matter of fact a discovery project even the larger sizes they you know from an effort perspective from a consultancy perspective they're quite low comparing to other products but the huge thing which eats the cost in in the implementation phase is the internal time spent and a lot of people don't think about this and that is what you want to optimize within your organization how do you spend that internal time for discovery continuous adaption then so that is where you slowly start adapting servicenow discovery and that is obviously where you have most of the opex or operational expenditure and mostly in terms of licensing costs here but there could also be some other opecs like for example having a discovery admin that you have employed or people maintaining the tool upgrades etc but that's fairly neglectable in my experience eighty percent of the opecs for discovery if not more comes from the the licensing but this is also the part where the cost reductions actually can take place during this phase and this has a direct correlation to how you actually use the data of discovery and this sounds silly but trust me i have seen many organizations who buy discovery but in the end of the day they don't use the data because discovery by itself doesn't do anything it's how you leverage that data it's how you leverage that tool or process um that will dictate how much money or cost reductions you can realize and i've also seen many examples of organizations who buy let's say licenses but they wait three years before using it and i get that sometimes you might get a sweet deal you know and you might just throw on you know a couple of licenses here and there but that is obviously something which tends to eat the opecs and it makes cost reductions more difficult and when we speak about the implementation here's a philosophy of mine which is that preparation is an art form so during the implementation stage of discovery there are there are quite some people who think that oh we should just get started let's try it out let's you know let's let's gradually um roll it out and see what happens and you know there's no real structure around it and i often see this and i am the absolute opposite here a matter of fact i've had situations where customers were almost like we don't want alex anymore because we don't see anything he's only talking and maybe you know that's that feedback to myself but the reason for this is simply because preparation is key and this is a good example here that i like to to use as a comparison there was um in japan 1200 engineers who essentially switched as you can see and hear the article they switched a track in a subway station in three hours all right twelve hundred people three hours they switched an entire track in a subway how did they do this well they prepared for two years it was a carefully orchestrated project and they succeeded and i am of the same philosophy in discovery so really do the preparation phase well because then i have seen successful discoveries been rolled out in a week or less and i'm speaking here global large organizations all right so preparation is really really important which brings me to the next topic here when we're looking at a cost efficient discovery implementation what are some of the important attributes and elements there there are three main pillars in my opinion not only in my opinion based on what i've seen before the first one security if you have followed me before you know i have spoken a lot about security but trust me when i say that the security aspect of servicenow discovery implementation is huge and it tends to yeah create a lot of discussions and addressing the security is super important coordination discovery is one of those things where there is a project and you are essentially speaking to the entire itu organization from infrastructure to database to network teams to cloud teams to devops teams literally you're speaking to the entire right organization because you're trying to fetch all the data right so if you don't know how to coordinate that well it's going to add a huge amount to the internal time spent and then more importantly political buy-in and i'm not necessarily referring here to the political buy-in from the executive management team or something like that but as you do the coordination as you start speaking to different teams you will also notice there is resistance to discovery because people have their own tools they might feel threatened by this new fancy tool there might be a guy who have maintained an excel list for 10 years and it's his little baby whatever it is you will find resistance during the project so to have a methodology for addressing that resistance and really ensuring that political buy-in within the organization that will also ensure adaption later on meaning that people actually use the product um and i should mention here that the cost reduction potential it scales fairly linear to the iot organization's size so if you are a huge it organization there is a huge potential for cost reduction through discovery but also be aware of that there is a risk profile and the risk also increases the larger the organization is for the reasons i'm mentioning here in front of you and interestingly enough here as you can see this has nothing to do with technical aspects almost it's all the human side and the human element of rolling out discovery and that's where the typically the majority of the risks are and i should just mention here that we are now going to have the first time ever by the way a um a boot camp for how to roll out discovery successfully um this is a a little bit let's say shameless self advertisement so to speak but this is a bootcamp for three days it's going to be in october and you can find it if you go to another partner slash boot camp and we will essentially or i personally will teach for three days approximately 15 hours how you can succeed with rolling out discoveries so if you're curious about knowing more about the implementation side of things then you might want to consider this one or for your team or whatever it might be all right continuing um one other important aspect is that people tend to lack data when they base their decisions so whenever it starts with a discovery project how do you actually know if it's going to be successful and how do you know if it has been successful um so there's a different difference there so do you do the discovery project because you are sure of its success and then when you have done the project can you verify that it has been successful in terms of cost reductions and so forth that's why it's really important to measure um kpis before you start a discovery project so you have something tangible to show that like okay this actually paid off in these and these areas and remember the value matrix i showed before you can then measure things in different processes you can measure things in consolidation measure things in culture like employee happiness whatever it is um so measuring is super important and i'm going to give you an example right now a study case of how we in the past measured this and hopefully this can serve as a little bit of inspiration for you guys if you were curious on taking benchmarks then so what we did was that we tried to quantify toil and you might wonder what is toil it's basically repetitive boring work that engineers are doing every day that makes them hate their job so decreasing employee frustration meanwhile reducing costs that's a sweet sentence for sure um but the driver here then as i mentioned is that for this customer that we were working with they essentially saw a burnout of engineers because they had to do so many dead tasks every day um to set the stage here this was a global eye to organization they had approximately 120 it up staff which sounds a lot but what i mean with that is they had 120 people on a daily basis who in one way or another were working with infrastructure so it could be the network team it could be the database team it could be the help desk you name it right um site reliability engineers etc so these engineers or itops they spent an insane amount of time on digging for data related to basic infrastructure information and you might not think so but if you start measuring how much time if you don't have a cmdb and you don't have discovery and you start measuring how much time people actually spend on just trying to find information it is staggering like imagine how many emails you go through just to find some old information about a server for example or maybe if you have five different tools you log into five different tools in a daily basis or whatever it might be um when you don't have a cmdb and discovery then finding that information is very very cumbersome and time consuming and this is what i called before smash the stack then um so what we did here was that we focused in a pilot group before the let's say before the the discovery rollout was gonna take place because we wanted to have good data for basing our decision should we continue with discovery on a more global scale um so what we did was we actually went down i went down and did real interviews with people i sat next to them and asked them show me your daily work how do you work and i started just looking through the process and how they were working with the it infrastructure and we started doing benchmarks and tracking and we also looked at current statistics so for example how long time did it take to resolve an incident because if it takes very long time to resolve an incident and you spend a lot of time digging for data it's probably an indicator that you can optimize it somewhere so what were the results then well in average all of these engineers or itops they spend approximately half an hour per day just digging for you know basic infrastructure related data this might sound like a lot it's not because if you're working with it especially in infrastructure your main job is literally to sit and go to different servers check his history check whatever it is right so 33 minutes per day in average based on this pilot group right and what we find out after testing then discovery and doing the same benchmarks is that we reduced it down to 14 minutes so approximately by half and yet again it might not sound like a lot like yeah we save you know 15 minutes per engineer per day is that really a lot well trust me it adds up so we did the project we did a measurement afterward and we looked at what did it actually save them so 19 minutes per fte and day all right that translates to 73 hours per fte and year globally for this organization that was eight thousand seven hundred and sixty hours across the i.t organization in days that is one thousand ninety days saved let's say per year which translated to roughly four um full ftes then and oops that at least if we're looking at the european standard and prices of how much an engineer cost or an it operations then that is roughly 240 000 euros per year then which they unlocked in more productive time but there is a truth with modification here because when we started with discovery actually we needed a full-time resource to maintain discovery so part of the cost savings went to paying for that resource but nonetheless what i want to illustrate here is yet again not to inflate numbers in your face or something like that but it's rather to show you that this was from one simple use case and this is what it was saying then and if you do this for different use cases beforehand then you really start basing things on data and that is super important to do because otherwise it would be very difficult to determine the success um i hope that makes sense so what i want to do now um is to speak a little bit about the benchmarks then so these are based on approximately 30 implementations um and we have now roughly 15 minutes left so if you have any questions now would be a good time to ask those questions because hopefully i will have maybe five or ten minutes where i can address the questions and if there are no any questions well then we'll end a little bit earlier so all right implementation benchmarks then so this is for servicenow discovery and this right here is the size versus duration and what do i mean with size 10 um so the size here refers to a combination of factors so more specifically node count and what i mean with that is the amount of infrastructure in scope for the discovery project we also have the i.t organization size so how many employees are there in the iit organization is it a global one is it geographically spread out etc we have the network size so how large are the networks the subnets and then we also have here operations partners and what i mean with that is outsourced because it's very common of course that if you are a company who outsource a lot of your it infrastructure you might be wondering is there really any value for us to utilizing discovery but that's more than on the cultural aspect for example data ownership data governance you are the owner of your data so we wanted to include also organizations here who really had a lot of outsourced i.t then um so on the bottom side we have duration and then we have the size then all right and by the way this is based on only implementations that we've done so here we have a timeline of 3 to 12 months and here we have the size from small to significant to massive and you might be wondering what dictates what here the scope here we have different colors we have below 5 000 ips so when i'm speaking below 5 000 ips i mean reachable ips here not the size of the subnets but ips that respond and that you want to include in the cmdb and discovery and then we have over 10 000 ips and over 25 000 ips so these are the different colors all right and here is some of these benchmarks um now there's a lot on the screen and you might be looking everywhere right now um keep in mind this will be shared later so you will get access to these benchmarks so you can investigate them yourself but i would like to remove a few of these benchmarks to make a few observations so here let's look at four data points to start with um what is interesting here for me to see is for example these two data points so here we have something of a very similar size but it takes double the amount of time and then in my experience the primary reason for this yet again was coordination all right so we're seeing here that if the coordination is poor during a discovery project if the feedback loops are long and what i mean with long feedback loops is that if it takes you a long long time to get firewall opens if there is a very very in-depth in detail security discussions or clearance or whatever it might be and that tends to add a lot to the time like that and the same here is very interesting for me that we see a correlation here that a project which was rather massive in size so over 25 000 um reachable ieps then took almost the same amount of time and i remember these two products and they took almost exactly the same amount of time like i'm speaking down to the day here almost um and what does this tell me and why am i mentioning these data points then well the reason i'm saying this is because this is a clear indication that it's not the size of the infrastructure necessarily that dictates how long time the discovery project is going to take in matter of fact it's a neglectable part but it really is about those other factors that i mentioned before so as i said um i have seen discovery being rolled out building the cmdbs of tens and thousands of of sea ice in the span of a few days if the preparation is done right um i should mention of course there might be some bias here in this data because and i want to acknowledge that that we are looking for certain type of responses just because we are humans so the way we ask questions might add to a little bit of bias to the data but nonetheless i think the data adds a lot of value to put things in perspective that when it comes to the timelines it's not the size of the network etc but there are other factors that are in play then i'm now going to look at another type of benchmarks which is then related to the primary challenges so we reached out to 12 organizations that we in one capacity or another have worked with discovery with and they had discovery running it was optimized etc and then we asked them questions what were the primary challenges during the implementation phase of discovery and we had four categories we had security we had coordination we had technical and then we had other so things that couldn't be captured in these three categories then um and the selection you see here is the first choice which is the very dark purple color the second choice which is the slightly less um purple and then finally the third almost pink one there is a margin of error where approximately five percent here by the way so keep that in mind um nonetheless we can clearly see here that based on these 12 rollouts the majority if we spoke about the first the primary challenge that they had was related to security so yet again this for me illustrates and confirms what i have seen based on my experience that security is still a huge factor to consider when it comes to servicenow discovery then the other part was indeed related to coordination same there a lot of people use that as a first choice then the second choice and also the you see a very few people had that as the third choice um and then finally the technical aspects um i think it was only one who had um the technical aspects as a first choice of their primary challenges and then there was a few i think one or two who had um it has a second choice but nonetheless you see here that when we speak about servicenow discovery it is not so much the technical factors which is the difficulty um but it is rather the the other constraints and then for the others um yeah it it as i mentioned it's something which didn't fall under these categories here then um so these were two benchmarks um that that we have gathered for this specifically for this webinar only if you are curious about more benchmarks or have more questions around these type of data and timelines and you can always reach out to me and also you can find contact information to us on ino and partners website all right to finalize this webinar as we only have approximately eight minutes left here there are some key points that i would like to summarize and for people to consider when it comes to cost optimization and roi the first one as i mentioned in the beginning categorize the let's say value drivers in culture versus numbers because that way you you really know what to expect and what to look for especially if you're going to explain that to management and so forth number two define and prioritize where the primary benefits are to set those expectations so are we doing discovery if we're looking if you remember this this maturity scale of iit organizations if you are very far on that maturity scale then you for sure have different motivations than if you are at the very beginning for example so define and prioritize these primary benefits and that will dictate how you measure than the the roi then realize that most capex is consumed by internal time not consultants necessarily yet again i'm emphasizing it coordination here reduces the capex um successful cost reduction so once you start using discovery it has a direct correlation then to the usage of data so how do you use that discovery data how much can you consolidate and what is the actual adaption rate of discovery so don't buy discovery and never use it is pretty much what i'm saying it sounds obvious but trust me um as i mentioned also eighty percent more than eighty percent of any opecs comes from the discovery licensing cost pretty much and a difficult political landscape with long feedback loops it entails a higher risk keep in mind there's also higher reward there if you succeed because most likely if you have a difficult political landscape and there are long feedback loops you don't have very intuitive tools within your organization because it has never been allowed to grow so maybe this would be an opportunity to take a jab at it um scope and network size so scope of the network size amount of infra etc that is actually a neglectable variable when it comes to how long time it takes to roll out discovery but it is rather the other factors and then last but not least here point number eight remember to measure data points before versus after rollout and trust me don't ignore this one spend a few days to really do those investigations because then you base things way way more on data essentially um so with that being said this was what i had in terms of the webinar and what i'm now going to do is i'm going to stop the screen sharing and see if i've received any questions here so i got a good question here directly based on your experience what do you think are the specific security or technical drawbacks of servicenow discovery when compared with other discovery products um well my answer to that question is that it used to be the fact that you need the credentials stored in service now which always were a reason of concern um you also needed local admin that is no longer the case and there is no agent-based discovery and more importantly you also have stuff such as just enough administration and privilege access management etc so right now when we speak about the product year 2021 i don't think there are any that large um specific security constraints and for technical drawbacks as such they have caught up pretty good in my opinion um i've also received a question here from ted who is asking in your experience alexander where are most companies in their development from legacy to cutting edge when looking at discovery in my experience most people who look at discovery or either one or the other they're either quite far back and so they don't have a same db at all pretty much or they're very further down the line and they're very modern with devops and so forth it's rare that i see the middle it exists but it tends to be rather polarized there in my experience um alright guys i see there is more questions but i don't think i have time to answer those because they are quite in-depth and um complex so i will get back specifically to you guys about those questions um for the rest of you who have been here on the webinar here today i'd like to sincerely thank you for your time it's a great pleasure to see so many people interested and i hope that you keep up to date please subscribe to the analm partners linkedin page where i will share and we will share a lot of item related content trust me it's never going to be sales things but just pure good content similar to the benchmarks and so forth um so have a great day and if you are curious about discovery don't hesitate to reach out to me um alexander aynor.partners and if you're a consultant curious about really mastering that you know that discovery rollout and how you do this from a practical perspective don't forget about the boot camp coming up it's the first time ever by interim partners and it will be me personally who teaches for three days so it's gonna be super fun and i'm looking forward to that and if you use um the the code webinar when you sign up for the bootcamp i will give you 20 off so have a good day everyone and then see around in the item space [Music]