okay we're back this is Dave vellante I'm with Wikibon organ this is silicon angles the cube the cube is a live mobile studio we come into events we're here at knowledge service now's big customer event we're here at the aria hotel in Las Vegas and we've got wall-to-wall coverage today tomorrow and part of thursday as many of you know we were at sapphire now the big SI p customer show were simulcasting that on SiliconANGLE too but we're here in Las Vegas the ServiceNow conference is all about transformation transforming from no to now we've kind of got a double whammy segment here virtually every industry is transforming and certainly Big Pharma is transforming quite dramatically as well as the IT components of many industries Ross rexer is here he's the managing director at kpmg the global consultancy and T Juan Lumpkin who's an IT practitioner for Eli Lilly gentlemen welcome to the cube okay thanks for you so Ross let's start with you at a high level what's happening in the pharmaceutical industry in general Big Pharma how is the industry itself transforming and then we'll get into the I TPS sure so many of the Big Pharma's find themselves today in a situation that is unique to their their business industry and market where a lot of blocked blockbuster drugs which have been significant sources of revenue over the years are starting to come on with that it brings competition and a loss of revenue so the big farmers are all in a very coordinated methodical process right now to resize their business and at the same time enable the R&D function to bring new drugs to market focusing on patient outcomes that will happen in different ways in them they probably ever done before so the business model itself has changed and along with it all the support functions like ITA of course too so in that so it's all about the pipeline right and and the challenge if I understand it is that historically you got the big pharma companies they would you know go do about go about their thing and develop these drugs and they get a blockbuster and it was a relative today a relatively slow paced environment that's that's changing if I understand it correctly what's driving that change so the the innovation around medicines today is much different than it has been over the last 10 20 years in that composition around in the use of different biotech components to create a to create medicines is now being sourced in different ways historically Pharma built itself and really invested and was really a research and development company almost entirely in-house right so all the support systems and everything the way that the business was run was around that nowadays these the farmers are collaborating with smaller providers many of them in ways that again they just historically have never done everything was done in house to build to bring drugs to market and now it's it's shifted absolute to the opposite side where big farmers are relying on these providers these third-party providers for all stages of R&D and ultimately FDA and the release of these so t1 I introduced you as an IT practitioner and Lily so talk about more specifically about your role there you focused on infrastructure I teach em a list or more about them yeah so my rules are about service integration think about those services that we deliver to our internal customers within lily and how do we do that across our complex ecosystem where you have multiple different IT departments you have multiple suppliers who have different rigs and complexities in that space and so our job is how do we minimize that complexity for our internal business partners and making sure that the way we build variety is seamless for our internal customers okay so we heard Ross talking about the the pressures in the in the industry from a from an IT practitioner standpoint what how does that change change your life what are the drivers and what's the business asking you to do but just like anyone we need more volume but we also have to do that under under constraint and so for us how do we get more fishing so you think about this basically gone under you can only do so much outsourcing you only do so much change and so you have to see how do I start running my business more efficiently and I think that's the big shift and I tias you're moving from a from an internal infrastructure towers are truly looking at how do we deliver IT services and part of living IT services and making sure that we're a value-added partner and also being assured that we're competitive with other sources of our businesses have to get services from an IT perspective yeah so 10 years ago we used to talk a lot about demand management and to me it's that's why i love this from now from know to now because demand management is actually ended up just being no we just can't handle the the volume so you mentioned constraints you've got constraints you've got to be more efficient so so talk a little bit about what you did to get more efficient for us it was all about standardization so how do we how do we build standardization across our IT infrastructure nikka system within our IT partners empower external partners what that does it gives us flexibility so that we can deliver our systems and be more agile they think about our internal space we had a lot of complexity we had multiple procedures multiple processes different business units operating or delivering IT services in a consistent manner what we've been able to do it being able to streamline that we've been able to be more consistent internally in a line on the comments that are processes and how we deliver those ikea services to our customers so Ross you're talking about the sort of changing dynamic of what I would call sort of the pharmaceutical ecosystem right so so that's that sounds like it's relatively new in pharma it used to be sort of a go-it-alone the big guys hey we're multi-billion dollar companies we don't need these little guys you see all these startups coming out there really innovative there faster so take us through sort of how that's evolving how companies are dealing with the ecosystem and what kind of pressures that puts on IT what are you seeing out there so as t1 was was mentioning as well this was pushing to IT service integration as a kind of one of the next frontiers of now right being able to have the single pane of glass single system of record of IT and our ability to bring standardized services up and down in a coordinating consistent way has allowed for the bigger more monolithic type companies in be able to interact with with these smaller more agile more tech-savvy appeal partners and be able to not overburden them so the little provider who has maybe less less overhead of IT infrastructure and their processes would find it hard to be able to collaborate electronically with a big pharma if we had to adopt the big pharma's old-style processes so service integration is all about allowing for the the easy plug-and-play of these providers and establishing the reference set of processes and the supporting data that's needed to govern those transactions or the length of the of the outsourcing arrangement with with that provider in a way that doesn't get overburden them but provides the company Big Pharma the ability to have transparency ability to see risks before they're happening and to enter manage the cause so talk about your practice a little bit how do you what's role do you play it's obviously you've got this increasingly complex ecosystem evolving they've definitely got different infrastructures um how do you sort of mediate all this so Kim G what are our go-to-market offering and our solution set is based around a set of leading practices that that we have established over the past 17 years for example that we've been in the IT service management consulting and advisory business so we have these accelerators that we can we bring to a project and engagement like like the one we're at Eli Lilly where we can quickly faster than ever establish a common ground for those processes the operational processes first and foremost that would don't require years and years of consultancy process engineering 20 years ago type of thing so our role in that is to provide the basis for the are the operating model that's going to go forward and allow the core customer as well as these other providers to get there fast to get operating faster so t1 we've been hearing a similar pattern from the customers that we've talked to a lot of stovepipes a lot of legacy you know tools a lot of uncoordinated sort of activities going going on is that what what Lily with you would you describe that as an accurate depiction of the pasture i think i think that i think you're being kind yeah I'm sure we kind on the cheer we don't like to feed our guests up what I think it not to over use the ERP for IT term but this is something I t we've done for our business partners over the years we haven't done for our so if you think about the essay peas of the world where you get your CI CFO a one-click look at the the financial assets of the company you think about from a CRM perspectively doing that for our sales force we've done that from an HR perspective but we haven't taken the time to look at from an IT perspective and how do i give the cio that same visibility across our portfolio services so that he can ask those same questions you can have that same visibility so i want to add a little color to this whole erp for IT though of course on the one hand you know the sort of single system of record that's a positive but when you think of erp i say we were at SI p sapphire there's a lot of complexity in erp and with that type of complexity you'd never succeed but so what's your experience been thus far with regard to you know the complexity in my senses it's not this big monolithic system it's a cloud-based SAS based system talk about that a little bit well for us it's getting to a set of standards it actually helped reduce the complexity where you have complexity when you have multiple business procedures across the organization delivering services and so to get to that single source that single record it is actually help to reduce a lot of complexity on our part help it make it easier for us to deliver customer service for customers the other piece of that to which is the the singularity of vision of how we deliver I team so right now within our business we're depending on what area in you may get IT servers that delivers slightly differently from each area we've been able to streamline that and say this is how you're going to receive IT services and make it a more predictable experience for our internal users I saw Rus I want to talk about this notion of a single system of record before I ask you why it's so important what are we talking about here because today you've got a single system of record for your transactions you might have a single system of record for your your data warehouse all these single systems are at a record so what do you mean by a single system of record so when we're talking with service now and specifically in the IT Service Management domain what we're talking about is having integrated the capability to see data across the different data domains if you like so operational data performance data service level data with that coupled with the IT finance data as well as a zesty one put 360-degree vision of your assets as well so linking all those sources of data together in a way that can be used for analytics maybe for the first time ever so we we we use the analogy of IT intelligence right so what we've given our business partners and business intelligence over the years mmm it's-- never had that so the ability to provide IT intelligence that allows for the leadership due to to have data have information that they can take decisions and then ultimately become predicted with that right so be able have the knowledge to know what we're doing to make the right choices and in the future be able to do some predictive analysis again back to the point about the demands really never got one hundred percent right over the years we've talked about a lot but having the ability to understand the consumption and have the levers to influence demand and see it grow I want to go back to this business process discussion you were sort of reference the 20 years ago the whole VPO of movement and you know business process reorganization it seems to me that what what occurred was you had let's say a database or some kind of system and maybe there was a module and then you build a business process around that and so you had relatively inflexible business process they were hard to change is are you seeing that change it we at the cusp of the dawn of a new era where I can actually create whatever business process I want to around that single system of record is that truly a vision that's coming to fruition we believe it is and our experience it is it is starting to happen and I think service now with their platform is one of the emerging leaders in this space that's allowing for that to happen percent of the day so you have you have a concise platform that allows you enough flexibility to build new processes but has the common data structure has the common user interface as the common workflow set in a and all wrapped in and easy to maintain type of platform is what I think 20 years ago we wished we had and we tried to build in many different ways and ended up mostly cobbling things together but we really believe that and again our starting to see success out there David the platform question is solved and that we're now able to get to the prosecutor historically we you know delivered value plenty of value the problem is so much of that value was sucked by the infrastructure and and and not enough went into the innovation around it do you want my question to you is so people don't like change naturally now maybe it's different and nit maybe they want change in IT but did you see initial resistance I'll know we have this way of doing it we don't want to change or are people enthusiastic about change talk about that a little quite you hit it spot-on and absolutely the technology is the easy part of it it's really the change part that that's the most difficult piece of it and I would say we've done to a lot of work just a line organization and we've had a lot of support for from not only our internal IT people but also our senior leadership team so we've gotten support we've seen a lot of buy-in not saying still them not going to be easy not gonna be easy but I feel that we've got the right momentum now to make this type of change to get the business volume part of its been able to articulate the value that we're going to receive from from from this initiative so it's early days for for Lily and you guys should just get started on this journey not yesterday but you know you you're in an inference perience to give some advice to your fellow practitioners so my ask you guys both start with t1 what advice would you give to fellow practitioners that are looking to move in this direction great I would say first of all you have to have the business alignment so I need to make sure that you can clearly articulate the value of the change of the company so I can I can talk not in terms of process but in terms of outcomes that we're going to drive for our business partners once you're able to describe those outcomes then you can have the conversation on what's the work it's going to take to get there it's not an easy journey to be able to paint that picture accurately for for our teams and also talk about how we're going to support them through the process and so we're going to talk about the value we're going to we're going to paint the picture the journey we're not going to tell you how I want to support you throughout that process okay Ross you're talking to CIOs what's your what's your main point of advice for CIOs in this regard is look at the transformation as transformational right so it's it's it can be a set of tactical projects and tactical wins based on outcomes that you're looking for however to in order to truly change the way your IT functions runs as a business do all these these great things that we're talking we're talking about today is you have to have the vision and understand that it is there are series of building blocks that we will get you incremental value along the way but this is not a quick you know product slam then again maybe 20 years ago was about let's swap this software for that software and we're going to be good it's not about that and that's not going to get you the transformation so it's about transformation it's about the metrics to be able to prove that you are transforming and continuous improvement Ross do you want thanks very much for coming on the cube and sharing your story we could go on forever we're getting the hook but really appreciate you guys coming up thanks thanks for having right thanks for watching everybody we right back with our next guest Chris Pope is here who's the director of product management for service now so we're going to double-click on the platform and share with you some greater information about that this is the cube I'm Dave vellante we're right back

>>Welcome to this exclusive discussion. IBM, the next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond. My name is Dave Volante from the Cuban today, we're going to take a deep dive into some of the most important trends impacting the life sciences industry in the next 60 minutes. Yeah, of course. We're going to hear how IBM is utilizing Watson and some really important in life impacting ways, but we'll also bring in real world perspectives from industry and the independent analyst view to better understand how technology and data are changing the nature of precision medicine. Now, the pandemic has created a new reality for everyone, but especially for life sciences companies, one where digital transformation is no longer an option, but a necessity. Now the upside is the events of the past 22 months have presented an accelerated opportunity for innovation technology and real world data are coming together and being applied to support life science, industry trends and improve drug discovery, clinical development, and treatment commercialization throughout the product life cycle cycle. Now I'd like to introduce our esteemed panel. Let me first introduce Lorraine Marshawn, who is general manager of life sciences at IBM Watson health. Lorraine leads the organization dedicated to improving clinical development research, showing greater treatment value in getting treatments to patients faster with differentiated solutions. Welcome Lorraine. Great to see you. >>Dr. Namita LeMay is the research vice-president of IDC, where she leads the life sciences R and D strategy and technology program, which provides research based advisory and consulting services as well as market analysis. The loan to meta thanks for joining us today. And our third panelist is Greg Cunningham. Who's the director of the RWE center of excellence at Eli Lilly and company. Welcome, Greg, you guys are doing some great work. Thanks for being here. Thanks >>Dave. >>Now today's panelists are very passionate about their work. If you'd like to ask them a question, please add it to the chat box located near the bottom of your screen, and we'll do our best to answer them all at the end of the panel. Let's get started. Okay, Greg, and then Lorraine and meta feel free to chime in after one of the game-changers that you're seeing, which are advancing precision medicine. And how do you see this evolving in 2022 and into the next decade? >>I'll give my answer from a life science research perspective. The game changer I see in advancing precision medicine is moving from doing research using kind of a single gene mutation or kind of a single to look at to doing this research using combinations of genes and the potential that this brings is to bring better drug targets forward, but also get the best product to a patient faster. Um, I can give, uh, an example how I see it playing out in the last decade. Non-oncology real-world evidence. We've seen an evolution in precision medicine as we've built out the patient record. Um, as we've done that, uh, the marketplace has evolved rapidly, uh, with, particularly for electronic medical record data and genomic data. And we were pretty happy to get our hands on electronic medical record data in the early days. And then later the genetic test results were combined with this data and we could do research looking at a single mutation leading to better patient outcomes. But I think where we're going to evolve in 2022 and beyond is with genetic testing, growing and oncology, providing us more data about that patient. More genes to look at, uh, researchers can look at groups of genes to analyze, to look at that complex combination of gene mutations. And I think it'll open the door for things like using artificial intelligence to help researchers plow through the complex number of permutations. When you think about all those genes you can look at in combination, right? Lorraine yes. Data and machine intelligence coming together, anything you would add. >>Yeah. Thank you very much. Well, I think that Greg's response really sets us up nicely, particularly when we think about the ability to utilize real-world data in the farm industry across a number of use cases from discovery to development to commercial, and, you know, in particular, I think with real world data and the comments that Greg just made about clinical EMR data linked with genetic or genomic data, a real area of interest in one that, uh, Watson health in particular is focused on the idea of being able to create a data exchange so that we can bring together claims clinical EMR data, genomics data, increasingly wearables and data directly from patients in order to create a digital health record that we like to call an intelligent patient health record that basically gives us the digital equivalent of a real life patient. And these can be used in use cases in randomized controlled clinical trials for synthetic control arms or natural history. They can be used in order to track patients' response to drugs and look at outcomes after they've been on various therapies as, as Greg is speaking to. And so I think that, you know, the promise of data and technology, the AI that we can apply on that is really helping us advance, getting therapies to market faster, with better information, lower sample sizes, and just a much more efficient way to do drug development and to track and monitor outcomes in patients. >>Great. Thank you for that now to meta, when I joined IDC many, many years ago, I really didn't know much about the industry that I was covering, but it's great to see you as a former practitioner now bringing in your views. What do you see as the big game-changers? >>So, um, I would, I would agree with what both Lorraine and Greg said. Um, but one thing that I'd just like to call out is that, you know, everyone's talking about big data, the volume of data is growing. It's growing exponentially actually about, I think 30% of data that exists today is healthcare data. And it's growing at a rate of 36%. That's huge, but then it's not just about the big, it's also about the broad, I think, um, you know, I think great points that, uh, Lorraine and Greg brought out that it's, it's not just specifically genomic data, it's multi omic data. And it's also about things like medical history, social determinants of health, behavioral data. Um, and why, because when you're talking about precision medicine and we know that we moved away from the, the terminology of personalized to position, because you want to talk about disease stratification and you can, it's really about convergence. >>Um, if you look at a recent JAMA paper in 2021, only 1% of EHS actually included genomic data. So you really need to have that ability to look at data holistically and IDC prediction is seeing that investments in AI to fuel in silico, silicone drug discovery will double by 20, 24, but how are you actually going to integrate all the different types of data? Just look at, for example, diabetes, you're on type two diabetes, 40 to 70% of it is genetically inherited and you have over 500 different, uh, genetic low side, which could be involved in playing into causing diabetes. So the earlier strategy, when you are looking at, you know, genetic risk scoring was really single trait. Now it's transitioning to multi rate. And when you say multi trade, you really need to get that integrated view that converging for you to, to be able to drive a precision medicine strategy. So to me, it's a very interesting contrast on one side, you're really trying to make it specific and focused towards an individual. And on the other side, you really have to go wider and bigger as well. >>Uh, great. I mean, the technology is enabling that convergence and the conditions are almost mandating it. Let's talk about some more about data that the data exchange and building an intelligent health record, as it relates to precision medicine, how will the interoperability of real-world data, you know, create that more cohesive picture for the, for the patient maybe Greg, you want to start, or anybody else wants to chime in? >>I think, um, the, the exciting thing from, from my perspective is the potential to gain access to data. You may be weren't aware of an exchange in implies that, uh, some kind of cataloging, so I can see, uh, maybe things that might, I just had no idea and, uh, bringing my own data and maybe linking data. These are concepts that I think are starting to take off in our field, but it, it really opens up those avenues to when you, you were talking about data, the robustness and richness volume isn't, uh, the only thing is Namita said, I think really getting to a rich high-quality data and, and an exchange offers a far bigger, uh, range for all of us to, to use, to get our work done. >>Yeah. And I think, um, just to chime, chime into that, uh, response from Greg, you know, what we hear increasingly, and it's pretty pervasive across the industry right now, because this ability to create an exchange or the intelligent, uh, patient health record, these are new ideas, you know, they're still rather nascent and it always is the operating model. Uh, that, that is the, uh, the difficult challenge here. And certainly that is the case. So we do have data in various silos. Uh, they're in patient claims, they're in electronic medical records, they might be in labs, images, genetic files on your smartphone. And so one of the challenges with this interoperability is being able to tap into these various sources of data, trying to identify quality data, as Greg has said, and the meta is underscoring as well. Uh, we've gotta be able to get to the depth of data that's really meaningful to us, but then we have to have technology that allows us to pull this data together. >>First of all, it has to be de-identified because of security and patient related needs. And then we've gotta be able to link it so that you can create that likeness in terms of the record, it has to be what we call cleaned or curated so that you get the noise and all the missing this out of it, that's a big step. And then it needs to be enriched, which means that the various components that are going to be meaningful, you know, again, are brought together so that you can create that cohort of patients, that individual patient record that now is useful in so many instances across farm, again, from development, all the way through commercial. So the idea of this exchange is to enable that exact process that I just described to have a, a place, a platform where various entities can bring their data in order to have it linked and integrated and cleaned and enriched so that they get something that is a package like a data package that they can actually use. >>And it's easy to plug into their, into their studies or into their use cases. And I think a really important component of this is that it's gotta be a place where various third parties can feel comfortable bringing their data together in order to match it with other third parties. That is a, a real value, uh, that the industry is increasingly saying would be important to them is, is the ability to bring in those third-party data sets and be able to link them and create these, these various data products. So that's really the idea of the data exchange is that you can benefit from accessing data, as Greg mentioned in catalogs that maybe are across these various silos so that you can do the kind of work that you need. And that we take a lot of the hard work out of it. I like to give an example. >>We spoke with one of our clients at one of the large pharma companies. And, uh, I think he expressed it very well. He said, what I'd like to do is have like a complete dataset of lupus. Lupus is an autoimmune condition. And I've just like to have like the quintessential lupus dataset that I can use to run any number of use cases across it. You know, whether it's looking at my phase one trial, whether it's selecting patients and enriching for later stage trials, whether it's understanding patient responses to different therapies as I designed my studies. And so, you know, this idea of adding in therapeutic area indication, specific data sets and being able to create that for the industry in the meta mentioned, being able to do that, for example, in diabetes, that's how pharma clients need to have their needs met is through taking the hard workout, bringing the data together, having it very therapeutically enriched so that they can use it very easily. >>Thank you for that detail and the meta. I mean, you can't do this with humans at scale in technology of all the things that Lorraine was talking about, the enrichment, the provenance, the quality, and of course, it's got to be governed. You've got to protect the privacy privacy humans just can't do all that at massive scale. Can it really tech that's where technology comes in? Doesn't it and automation. >>Absolutely. >>I, couldn't more, I think the biggest, you know, whether you talk about precision medicine or you talk about decentralized trials, I think there's been a lot of hype around these terms, but what is really important to remember is technology is the game changer and bringing all that data together is really going to be the key enabler. So multimodal data integration, looking at things like security or federated learning, or also when you're talking about leveraging AI, you're not talking about things like bias or other aspects around that are, are critical components that need to be addressed. I think the industry is, uh, it's partly, still trying to figure out the right use cases. So it's one part is getting together the data, but also getting together the right data. Um, I think data interoperability is going to be the absolute game changer for enabling this. Uh, but yes, um, absolutely. I can, I can really couldn't agree more with what Lorraine just said, that it's bringing all those different aspects of data together to really drive that precision medicine strategy. >>Excellent. Hey Greg, let's talk about protocols decentralized clinical trials. You know, they're not new to life silences, but, but the adoption of DCTs is of course sped up due to the pandemic we've had to make trade-offs obviously, and the risk is clearly worth it, but you're going to continue to be a primary approach as we enter 2022. What are the opportunities that you see to improve? How DCTs are designed and executed? >>I see a couple opportunities to improve in this area. The first is, uh, back to technology. The infrastructure around clinical trials has, has evolved over the years. Uh, but now you're talking about moving away from kind of site focus to the patient focus. Uh, so with that, you have to build out a new set of tools that would help. So for example, one would be novel trial, recruitment, and screening, you know, how do you, how do you find patients and how do you screen them to see if are they, are they really a fit for, for this protocol? Another example, uh, very important documents that we have to get is, uh, you know, the e-consent that someone's says, yes, I'm, well, I understand this study and I'm willing to do it, have to do that in a more remote way than, than we've done in the past. >>Um, the exciting area, I think, is the use of, uh, eco, uh, E-Pro where we capture data from the patient using apps, devices, sensors. And I think all of these capabilities will bring a new way of, of getting data faster, uh, in, in this kind of model. But the exciting thing from, uh, our perspective at Lily is it's going to bring more data about the patient from the patient, not just from the healthcare provider side, it's going to bring real data from these apps, devices and sensors. The second thing I think is using real-world data to identify patients, to also improve protocols. We run scenarios today, looking at what's the impact. If you change a cut point on a, a lab or a biomarker to see how that would affect, uh, potential enrollment of patients. So it, it definitely the real-world data can be used to, to make decisions, you know, how you improve these protocols. >>But the thing that we've been at the challenge we've been after that this probably offers the biggest is using real-world data to identify patients as we move away from large academic centers that we've used for years as our sites. Um, you can maybe get more patients who are from the rural areas of our countries or not near these large, uh, uh, academic centers. And we think it'll bring a little more diversity to the population, uh, who who's, uh, eligible, but also we have their data, so we can see if they really fit the criteria and the probability they are a fit for the trial is much higher than >>Right. Lorraine. I mean, your clients must be really pushing you to help them improve DCTs what are you seeing in the field? >>Yes, in fact, we just attended the inaugural meeting of the de-central trials research Alliance in, uh, in Boston about two weeks ago where, uh, all of the industry came together, pharma companies, uh, consulting vendors, just everyone who's been in this industry working to help define de-central trials and, um, think through what its potential is. Think through various models in order to enable it, because again, a nascent concept that I think COVID has spurred into action. Um, but it is important to take a look at the definition of DCT. I think there are those entities that describe it as accessing data directly from the patient. I think that is a component of it, but I think it's much broader than that. To me, it's about really looking at workflows and processes of bringing data in from various remote locations and enabling the whole ecosystem to work much more effectively along the data continuum. >>So a DCT is all around being able to make a site more effective, whether it's being able to administer a tele visit or the way that they're getting data into the electronic data captures. So I think we have to take a look at the, the workflows and the operating models for enabling de-central trials and a lot of what we're doing with our own technology. Greg mentioned the idea of electronic consent of being able to do electronic patient reported outcomes, other collection of data directly from the patient wearables tele-health. So these are all data acquisition, methodologies, and technologies that, that we are enabling in order to get the best of the data into the electronic data capture system. So edit can be put together and processed and submitted to the FDA for regulatory use for clinical trial type submission. So we're working on that. I think the other thing that's happening is the ability to be much more flexible and be able to have more cloud-based storage allows you to be much more inter-operable to allow API APIs in order to bring in the various types of data. >>So we're really looking at technology that can make us much more fluid and flexible and accommodating to all the ways that people live and work and manage their health, because we have to reflect that in the way we collect those data types. So that's a lot of what we're, what we're focused on. And in talking with our clients, we spend also a lot of time trying to understand along the, let's say de-central clinical trials continuum, you know, w where are they? And I know Namita is going to talk a little bit about research that they've done in terms of that adoption curve, but because COVID sort of forced us into being able to collect data in more remote fashion in order to allow some of these clinical trials to continue during COVID when a lot of them had to stop. What we want to make sure is that we understand and can codify some of those best practices and that we can help our clients enable that because the worst thing that would happen would be to have made some of that progress in that direction. >>But then when COVID is over to go back to the old ways of doing things and not bring some of those best practices forward, and we actually hear from some of our clients in the pharma industry, that they worry about that as well, because we don't yet have a system for operationalizing a de-central trial. And so we really have to think about the protocol it's designed, the indication, the types of patients, what makes sense to decentralize, what makes sense to still continue to collect data in a more traditional fashion. So we're spending a lot of time advising and consulting with our patients, as well as, I mean, with our clients, as well as CRS, um, on what the best model is in terms of their, their portfolio of studies. And I think that's a really important aspect of trying to accelerate the adoption is making sure that what we're doing is fit for purpose, just because you can use technology doesn't mean you should, it really still does require human beings to think about the problem and solve them in a very practical way. >>Great, thank you for that. Lorraine. I want to pick up on some things that Lorraine was just saying. And then back to what Greg was saying about, uh, uh, DCTs becoming more patient centric, you had a prediction or IDC, did I presume your fingerprints were on it? Uh, that by 20 25, 70 5% of trials will be patient-centric decentralized clinical trials, 90% will be hybrid. So maybe you could help us understand that relationship and what types of innovations are going to be needed to support that evolution of DCT. >>Thanks, Dave. Yeah. Um, you know, sorry, I, I certainly believe that, uh, you know, uh, Lorraine was pointing out of bringing up a very important point. It's about being able to continue what you have learned in over the past two years, I feel this, you know, it was not really a digital revolution. It was an attitude. The revolution that this industry underwent, um, technology existed just as clinical trials exist as drugs exist, but there was a proof of concept that technology works that this model is working. So I think that what, for example, telehealth, um, did for, for healthcare, you know, transition from, from care, anywhere care, anytime, anywhere, and even becoming predictive. That's what the decentralized clinical trials model is doing for clinical trials today. Great points again, that you have to really look at where it's being applied. You just can't randomly apply it across clinical trials. >>And this is where the industry is maturing the complexity. Um, you know, some people think decentralized trials are very simple. You just go and implement these centralized clinical trials, but it's not that simple as it it's being able to define, which are the right technologies for that specific, um, therapeutic area for that specific phase of the study. It's being also a very important point is bringing in the patient's voice into the process. Hey, I had my first telehealth visit sometime last year and I was absolutely thrilled about it. I said, no time wasted. I mean, everything's done in half an hour, but not all patients want that. Some want to consider going back and you, again, need to customize your de-centralized trials model to, to the, to the type of patient population, the demographics that you're dealing with. So there are multiple factors. Um, also stepping back, you know, Lorraine mentioned they're consulting with, uh, with their clients, advising them. >>And I think a lot of, um, a lot of companies are still evolving in their maturity in DCTs though. There's a lot of boys about it. Not everyone is very mature in it. So it's, I think it, one thing everyone's kind of agreeing with is yes, we want to do it, but it's really about how do we go about it? How do we make this a flexible and scalable modern model? How do we integrate the patient's voice into the process? What are the KPIs that we define the key performance indicators that we define? Do we have a playbook to implement this model to make it a scalable model? And, you know, finally, I think what organizations really need to look at is kind of developing a de-centralized mature maturity scoring model, so that I assess where I am today and use that playbook to define, how am I going to move down the line to me reach the next level of maturity. Those were some of my thoughts. Right? >>Excellent. And now remember you, if you have any questions, use the chat box below to submit those questions. We have some questions coming in from the audience. >>At one point to that, I think one common thread between the earlier discussion around precision medicine and around decentralized trials really is data interoperability. It is going to be a big game changer to, to enable both of these pieces. Sorry. Thanks, Dave. >>Yeah. Thank you. Yeah. So again, put your questions in the chat box. I'm actually going to go to one of the questions from the audience. I get some other questions as well, but when you think about all the new data types that are coming in from social media, omics wearables. So the question is with greater access to these new types of data, what trends are you seeing from pharma device as far as developing capabilities to effectively manage and analyze these novel data types? Is there anything that you guys are seeing, um, that you can share in terms of best practice or advice >>I'll offer up? One thing, I think the interoperability isn't quite there today. So, so what's that mean you can take some of those data sources. You mentioned, uh, some Omix data with, uh, some health claims data and it's the, we spend too much time and in our space putting data to gather the behind the scenes, I think the stat is 80% of the time is assembling the data 20% analyzing. And we've had conversations here at Lilly about how do we get to 80% of the time is doing analysis. And it really requires us to think, take a step back and think about when you create a, uh, a health record, you really have to be, have the same plugins so that, you know, data can be put together very easily, like Lorraine mentioned earlier. And that comes back to investing in as an industry and standards so that, you know, you have some of data standard, we all can agree upon. And then those plugs get a lot easier and we can spend our time figuring out how to make, uh, people's lives better with healthcare analysis versus putting data together, which is not a lot of fun behind the scenes. >>Other thoughts on, um, on, on how to take advantage of sort of novel data coming from things like devices in the nose that you guys are seeing. >>I could jump in there on your end. Did you want to go ahead? Okay. So, uh, I mean, I think there's huge value that's being seen, uh, in leveraging those multiple data types. I think one area you're seeing is the growth of prescription digital therapeutics and, um, using those to support, uh, you know, things like behavioral health issues and a lot of other critical conditions it's really taking you again, it is interlinking real-world data cause it's really taking you to the patient's home. Um, and it's, it's, there's a lot of patients in the city out here cause you can really monitor the patient real-time um, without the patient having coming, you know, coming and doing a site visit once in say four weeks or six weeks. So, um, I, and, uh, for example, uh, suicidal behavior and just to take an example, if you can predict well in advance, based on those behavioral parameters, that this is likely to trigger that, uh, the value of it is enormous. Um, again, I think, uh, Greg made a valid point about the industry still trying to deal with resolving the data interoperability issue. And there are so many players that are coming in the industry right now. There are really few that have the maturity and the capability to address these challenges and provide intelligence solutions. >>Yeah. Maybe I'll just, uh, go ahead and, uh, and chime into Nikita's last comment there. I think that's what we're seeing as well. And it's very common, you know, from an innovation standpoint that you have, uh, a nascent industry or a nascent innovation sort of situation that we have right now where it's very fragmented. You have a lot of small players, you have some larger entrenched players that have the capability, um, to help to solve the interoperability challenge, the standards challenge. I mean, I think IBM Watson health is certainly one of the entities that has that ability and is taking a stand in the industry, uh, in order to, to help lead in that way. Others are too. And, uh, but with, with all of the small companies that are trying to find interesting and creative ways to gather that data, it does create a very fragmented, uh, type of environment and ecosystem that we're in. >>And I think as we mature, as we do come forward with the KPIs, the operating models, um, because you know, the devil's in the detail in terms of the operating models, it's really exciting to talk these trends and think about the future state. But as Greg pointed out, if you're spending 80% of your time just under the hood, you know, trying to get the engine, all the spark plugs to line up, um, that's, that's just hard grunt work that has to be done. So I think that's where we need to be focused. And I think bringing all the data in from these disparate tools, you know, that's fine, we need, uh, a platform or the API APIs that can enable that. But I think as we, as we progress, we'll see more consolidation, uh, more standards coming into play, solving the interoperability types of challenges. >>And, um, so I think that's where we should, we should focus on what it's going to take and in three years to really codify this and make it, so it's a, it's a well hum humming machine. And, you know, I do know having also been in pharma that, uh, there's a very pilot oriented approach to this thing, which I think is really healthy. I think large pharma companies tend to place a lot of bets with different programs on different tools and technologies, to some extent to see what's gonna stick and, you know, kind of with an innovation mindset. And I think that's good. I think that's kind of part of the process of figuring out what is going to work and, and helping us when we get to that point of consolidating our model and the technologies going forward. So I think all of the efforts today are definitely driving us to something that feels much more codified in the next three to five years. >>Excellent. We have another question from the audience it's sort of related to the theme of this discussion, given the FDA's recent guidance on using claims and electronic health records, data to support regulatory decision-making what advancements do you think we can expect with regards to regulatory use of real-world data in the coming years? It's kind of a two-parter so maybe you guys can collaborate on this one. What role that, and then what role do you think industry plays in influencing innovation within the regulatory space? >>All right. Well, it looks like you've stumped the panel there. Uh, Dave, >>It's okay to take some time to think about it, right? You want me to repeat it? You guys, >>I, you know, I I'm sure that the group is going to chime into this. I, so the FDA has issued a guidance. Um, it's just, it's, it's exactly that the FDA issues guidances and says that, you know, it's aware and supportive of the fact that we need to be using real-world data. We need to create the interoperability, the standards, the ways to make sure that we can include it in regulatory submissions and the like, um, and, and I sort of think about it akin to the critical path initiative, probably, I don't know, 10 or 12 years ago in pharma, uh, when the FDA also embrace this idea of the critical path and being able to allow more in silico modeling of clinical trial, design and development. And it really took the industry a good 10 years, um, you know, before they were able to actually adopt and apply and take that sort of guidance or openness from the FDA and actually apply it in a way that started to influence the way clinical trials were designed or the in silico modeling. >>So I think the second part of the question is really important because while I think the FDA is saying, yes, we recognize it's important. Uh, we want to be able to encourage and support it. You know, when you look for example, at synthetic control arms, right? The use of real-world data in regulatory submissions over the last five or six years, all of the use cases have been in oncology. I think there've been about maybe somewhere between eight to 10 submissions. And I think only one actually was a successful submission, uh, in all those situations, the real-world data arm of that oncology trial that synthetic control arm was actually rejected by the FDA because of lack of completeness or, you know, equalness in terms of the data. So the FDA is not going to tell us how to do this. So I think the second part of the question, which is what's the role of industry, it's absolutely on industry in order to figure out exactly what we're talking about, how do we figure out the interoperability, how do we apply the standards? >>How do we ensure good quality data? How do we enrich it and create the cohort that is going to be equivalent to the patient in the real world, uh, in the end that would otherwise be in the clinical trial and how do we create something that the FDA can agree with? And we'll certainly we'll want to work with the FDA in order to figure out this model. And I think companies are already doing that, but I think that the onus is going to be on industry in order to figure out how you actually operationalize this and make it real. >>Excellent. Thank you. Um, question on what's the most common misconception that clinical research stakeholders with sites or participants, et cetera might have about DCTs? >>Um, I could jump in there. Right. So, sure. So, um, I think in terms of misconceptions, um, I think the communist misconceptions that sites are going away forever, which I do not think is really happening today. Then the second, second part of it is that, um, I think also the perspective that patients are potentially neglected because they're moving away. So we'll pay when I, when I, what I mean by that neglected, perhaps it was not the appropriate term, but the fact that, uh, will patients will, will, will patient engagement continue, will retention be strong since the patients are not interacting in person with the investigator quite as much. Um, so site retention and patient retention or engagement from both perspectives, I think remains a concern. Um, but actually if you look at, uh, look at, uh, assessments that have been done, I think patients are more than happy. >>Majority of the patients have been really happy about, about the new model. And in fact, sites are, seem to increase, have increased investments in technology by 50% to support this kind of a model. So, and the last thing is that, you know, decentralized trials is a great model and it can be applied to every possible clinical trial. And in another couple of weeks, the whole industry will be implementing only decentralized trials. I think we are far away from that. It's just not something that you would implement across every trial. And we discussed that already. So you have to find the right use cases for that. So I think those were some of the key misconceptions I'd say in the industry right now. Yeah. >>Yeah. And I would add that the misconception I hear the most about is, uh, the, the similar to what Namita said about the sites and healthcare professionals, not being involved to the level that they are today. Uh, when I mentioned earlier in our conversation about being excited about capturing more data, uh, from the patient that was always in context of, in addition to, you know, healthcare professional opinion, because I think both of them bring that enrichment and a broader perspective of that patient experience, whatever disease they're faced with. So I, I think some people think is just an all internet trial with just someone, uh, putting out there their own perspective. And, and it's, it's a combination of both to, to deliver a robust data set. >>Yeah. Maybe I'll just comment on, it reminds me of probably 10 or 15 years ago, maybe even more when, um, really remote monitoring was enabled, right? So you didn't have to have the study coordinator traveled to the investigative site in order to check the temperature of the freezer and make sure that patient records were being completed appropriately because they could have a remote visit and they could, they could send the data in a via electronic data and do the monitoring visit, you know, in real time, just the way we're having this kind of communication here. And there was just so much fear that you were going to replace or supplant the personal relationship between the sites between the study coordinators that you were going to, you know, have to supplant the role of the monitor, which was always a very important role in clinical trials. >>And I think people that really want to do embrace the technology and the advantages that it provided quickly saw that what it allowed was the monitor to do higher value work, you know, instead of going in and checking the temperature on a freezer, when they did have their visit, they were able to sit and have a quality discussion for example, about how patient recruitment was going or what was coming up in terms of the consent. And so it created a much more high touch, high quality type of interaction between the monitor and the investigative site. And I think we should be looking for the same advantages from DCT. We shouldn't fear it. We shouldn't think that it's going to supplant the site or the investigator or the relationship. It's our job to figure out where the technology fits and clinical sciences always got to be high touch combined with high-tech, but the high touch has to lead. And so getting that balance right? And so that's going to happen here as well. We will figure out other high value work, meaningful work for the site staff to do while they let the technology take care of the lower quality work, if you will, or the lower value work, >>That's not an, or it's an, and, and you're talking about the higher value work. And it, it leads me to something that Greg said earlier about the 80, 20, 80% is assembly. 20% is actually doing the analysis and that's not unique to, to, to life sciences, but, but sort of question is it's an organizational question in terms of how we think about data and how we approach data in the future. So Bamyan historically big data in life sciences in any industry really is required highly centralized and specialized teams to do things that the rain was talking about, the enrichment, the provenance, the data quality, the governance, the PR highly hyper specialized teams to do that. And they serve different constituencies. You know, not necessarily with that, with, with context, they're just kind of data people. Um, so they have responsibility for doing all those things. Greg, for instance, within literally, are you seeing a move to, to, to democratize data access? We've talked about data interoperability, part of that state of sharing, um, that kind of breaks that centralized hold, or is that just too far in the future? It's too risky in this industry? >>Uh, it's actually happening now. Uh, it's a great point. We, we try to classify what people can do. And, uh, the example would be you give someone who's less analytically qualified, uh, give them a dashboard, let them interact with the data, let them better understand, uh, what, what we're seeing out in the real world. Uh, there's a middle user, someone who you could give them, they can do some analysis with the tool. And the nice thing with that is you have some guardrails around that and you keep them in their lane, but it allows them to do some of their work without having to go ask those centralized experts that, that you mentioned their precious resources. And that's the third group is those, uh, highly analytical folks that can, can really deliver, uh, just value beyond. But when they're doing all those other things, uh, it really hinders them from doing what we've been talking about is the high value stuff. So we've, we've kind of split into those. We look at people using data in one of those three lanes and it, and it has helped I think, uh, us better not try to make a one fit solution for, for how we deliver data and analytic tools for people. Right. >>Okay. I mean, DCT hot topic with the, the, the audience here. Another question, um, what capabilities do sponsors and CRS need to develop in-house to pivot toward DCT? >>Should I jump in here? Yeah, I mean, um, I think, you know, when, when we speak about DCTs and when I speak with, uh, folks around in the industry, I, it takes me back to the days of risk-based monitoring. When it was first being implemented, it was a huge organizational change from the conventional monitoring models to centralize monitoring and risk-based monitoring, it needs a mental reset. It needs as Lorraine had pointed out a little while ago, restructuring workflows, re redefining processes. And I think that is one big piece. That is, I think the first piece, when, you know, when you're implementing a new model, I think organizational change management is a big piece of it because you are disturbing existing structures, existing methods. So getting that buy-in across the organization towards the new model, seeing what the value add in it. And where do you personally fit into that story? >>How do your workflows change, or how was your role impacted? I think without that this industry will struggle. So I see organizations, I think, first trying to work on that piece to build that in. And then of course, I also want to step back for the second to the, uh, to the point that you brought out about data democratization. And I think Greg Greg gave an excellent point, uh, input about how it's happening in the industry. But I would also say that the data democratization really empowerment of, of, of the stakeholders also includes the sites, the investigators. So what is the level of access to data that you know, that they have now, and is it, uh, as well as patients? So see increasingly more and more companies trying to provide access to patients finally, it's their data. So why shouldn't they have some insights to it, right. So access to patients and, uh, you know, the 80, 20 part of it. Uh, yes, he's absolutely right that, uh, we want to see that flip from, uh, 20%, um, you know, focusing on, on actually integrating the data 80% of analytics, but the real future will be coming in when actually the 20 and 18 has gone. And you actually have analysts the insights out on a silver platter. That's kind of wishful thinking, some of the industries is getting there in small pieces, but yeah, then that's just why I should, why we share >>Great points. >>And I think that we're, we're there in terms that like, I really appreciate the point around democratizing the data and giving the patient access ownership and control over their own data. I mean, you know, we see the health portals that are now available for patients to view their own records, images, and labs, and claims and EMR. We have blockchain technology, which is really critical here in terms of the patient, being able to pull all of their own data together, you know, in the blockchain and immutable record that they can own and control if they want to use that to transact clinical trial types of opportunities based on their data, they can, or other real world scenarios. But if they want to just manage their own data because they're traveling and if they're in a risky health situation, they've got their own record of their health, their health history, uh, which can avoid, you know, medical errors occurring. So, you know, even going beyond life sciences, I think this idea of democratizing data is just good for health. It's just good for people. And we definitely have the technology that can make it a reality. Now >>You're here. We have just about 10 minutes left and now of course, now all the questions are rolling in like crazy from the crowd. Would it be curious to know if there would be any comments from the panel on cost comparison analysis between traditional clinical trials in DCTs and how could the outcome effect the implementation of DCTs any sort of high-level framework you can share? >>I would say these are still early days to, to drive that analysis because I think many companies are, um, are still in the early stages of implementation. They've done a couple of trials. The other part of it that's important to keep in mind is, um, is for organizations it's, they're at a stage of, uh, of being on the learning curve. So when you're, you're calculating the cost efficiencies, if ideally you should have had two stakeholders involved, you could have potentially 20 stakeholders involved because everyone's trying to learn the process and see how it's going to be implemented. So, um, I don't think, and the third part of it, I think is organizations are still defining their KPIs. How do you measure it? What do you measure? So, um, and even still plugging in the pieces of technology that they need to fit in, who are they partnering with? >>What are the pieces of technology they're implementing? So I don't think there is a clear cut as answered at this stage. I think as you scale this model, the efficiencies will be seen. It's like any new technology or any new solution that's implemented in the first stages. It's always a little more complex and in fact sometimes costs extra. But as, as you start scaling it, as you establish your workflows, as you streamline it, the cost efficiencies will start becoming evident. That's why the industry is moving there. And I think that's how it turned out on the long run. >>Yeah. Just make it maybe out a comment. If you don't mind, the clinical trials are, have traditionally been costed are budgeted is on a per patient basis. And so, you know, based on the difficulty of the therapeutic area to recruit a rare oncology or neuromuscular disease, there's an average that it costs in order to find that patient and then execute the various procedures throughout the clinical trial on that patient. And so the difficulty of reaching the patient and then the complexity of the trial has led to what we might call a per patient stipend, which is just the metric that we use to sort of figure out what the average cost of a trial will be. So I think to point, we're going to have to see where the ability to adjust workflows, get to patients faster, collect data more easily in order to make the burden on the site, less onerous. I think once we start to see that work eases up because of technology, then I think we'll start to see those cost equations change. But I think right now the system isn't designed in order to really measure the economic benefit of de-central models. And I think we're going to have to sort of figure out what that looks like as we go along and since it's patient oriented right now, we'll have to say, well, you know, how does that work, ease up? And to those costs actually come down and then >>Just scale, it's going to be more, more clear as the media was saying, next question from the audiences, it's kind of a best fit question. You all have touched on this, but let me just ask it is what examples in which, in which phases suit DCT in its current form, be it fully DCT or hybrid models, none of our horses for courses question. >>Well, I think it's kind of, uh, it's, it's it's has its efficiencies, obviously on the later phases, then the absolute early phase trials, those are not the ideal models for DCTs I would say so. And again, the logic is also the fact that, you know, when you're, you're going into the later phase trials, the volume of number of patients is increasing considerably to the point that Lorraine brought up about access to the patients about patient selection. The fact, I think what one should look at is really the advantages that it brings in, in terms of, you know, patient access in terms of patient diversity, which is a big piece that, um, the cities are enabling. So, um, if you, if, if you, if you look at the spectrum of, of these advantages and, and just to step back for a moment, if you, if you're looking at costs, like you're looking at things like remote site monitoring, um, is, is a big, big plus, right? >>I mean, uh, site monitoring alone accounts for around a third of the trial costs. So there are so many pieces that fall in together. The challenge actually that comes when you're in defining DCTs and there are, as Rick pointed out multiple definitions of DCTs that are existing, uh, you know, in the industry right now, whether you're talking of what Detroit is doing, or you're talking about acro or Citi or others. But the point is it's a continuum, it's a continuum of different pieces that have been woven together. And so how do you decide which pieces you're plugging in and how does that impact the total cost or the solution that you're implementing? >>Great, thank you. Last question we have in the audience, excuse me. What changes have you seen? Are there others that you can share from the FDA EU APAC, regulators and supporting DCTs precision medicine for approval processes, anything you guys would highlight that we should be aware of? >>Um, I could quickly just add that. I think, um, I'm just publishing a report on de-centralized clinical trials should be published shortly, uh, perspective on that. But I would say that right now, um, there, there was a, in the FDA agenda, there was a plan for a decentralized clinical trials guidance, as far as I'm aware, one has not yet been published. There have been significant guidances that have been published both by email and by, uh, the FDA that, um, you know, around the implementation of clinical trials during the COVID pandemic, which incorporate various technology pieces, which support the DCD model. Um, but I, and again, I think one of the reasons why it's not easy to publish a well-defined guidance on that is because there are so many moving pieces in it. I think it's the Danish, uh, regulatory agency, which has per se published a guidance and revised it as well on decentralized clinical trials. >>Right. Okay. Uh, we're pretty much out of time, but I, I wonder Lorraine, if you could give us some, some final thoughts and bring us home things that we should be watching or how you see the future. >>Well, I think first of all, let me, let me thank the panel. Uh, we really appreciate Greg from Lily and the meta from IDC bringing their perspectives to this conversation. And, uh, I hope that the audience has enjoyed the, uh, the discussion that we've had around the future state of real world data as, as well as DCT. And I think, you know, some of the themes that we've talked about, number one, I think we have a vision and I think we have the right strategies in terms of the future promise of real-world data in any number of different applications. We certainly have talked about the promise of DCT to be more efficient, to get us closer to the patient. I think that what we have to focus on is how we come together as an industry to really work through these very vexing operational issues, because those are always the things that hang us up and whether it's clinical research or whether it's later stage, uh, applications of data. >>We, the healthcare system is still very fragmented, particularly in the us. Um, it's still very, state-based, uh, you know, different states can have different kinds of, uh, of, of cultures and geographic, uh, delineations. And so I think that, you know, figuring out a way that we can sort of harmonize and bring all of the data together, bring some of the models together. I think that's what you need to look to us to do both industry consulting organizations, such as IBM Watson health. And we are, you know, through DTRA and, and other, uh, consortia and different bodies. I think we're all identifying what the challenges are in terms of making this a reality and working systematically on those. >>It's always a pleasure to work with such great panelists. Thank you, Lorraine Marshawn, Dr. Namita LeMay, and Greg Cunningham really appreciate your participation today and your insights. The next three years of life sciences, innovation, precision medicine, advanced clinical data management and beyond has been brought to you by IBM in the cube. You're a global leader in high tech coverage. And while this discussion has concluded, the conversation continues. So please take a moment to answer a few questions about today's panel on behalf of the entire IBM life sciences team and the cube decks for your time and your feedback. And we'll see you next time.

>>Hi, Brian, this is Dave Volante. Welcome to this cube conversation with Thomas Hazel was the founder and CTO of chaos surgeon. I'm also joined by ed Walsh. Who's the CEO Thomas. Good to see you. >>Great to be here. >>Explain Jason. First of all, what >>Jason, Jason has a powerful data representation, a data source. Uh, but let's just say that we try to drive value out of it. It gets complicated. Uh, I can search. We activate customers, data lakes. So, you know, customers stream their Jason data to this, uh, cloud stores that we activate. Now, the trick is the complexity of a Jason data structure. You can do all these complexity of representation. Now here's the problem putting that representation into a elastic search database or relational databases, very problematic. So what people choose to do is they pick and choose what they want and or they just stored as a blob. And so I said, what if, what if we create a new index technology that could store it as a full representation, but dynamically in a, we call our data refinery published access to all the permutations that you may want, where if you do a full on flatten, your flattening of its Jason, one row theoretically could be put into a million rows and relational data sort of explode, >>But then it gets really expensive. But so, but everybody says they have Jason support, every database vendor that I talked to, it's a big announcement. We now support Jason. What's the deal. >>Exactly. So you take your relational database with all those relational constructs and you have a proprietary Jason API to pick and choose. So instead of picking, choosing upfront, now you're picking, choosing in the backend where you really want us the power of the relational analysis of that Jaison data. And that's where chaos comes in, where we expand those data streams we do in a relational way. So all that tooling you've been built to know and love. Now you can access to it. So if you're doing proprietary APIs or Jason data, you're not using Looker, you're not using Tableau. You're doing some type of proprietary, probably emailing now on the backend. >>Okay. So you say all the tools that you've trained, everybody on you can't really use them. You got to build some custom stuff and okay, so, so, so maybe bring that home then in terms of what what's the money, why do the suits care about this stuff? >>The reason this is so important is think about anything, cloud native Kubernetes, your different applications. What you're doing in Mongo is all Jason is it's very powerful but painful, but if you're not keeping the data, what people are doing a data scientist is, or they're just doing leveling, they're saying I'm going to only keep the first four things. So think about it's Kubernetes, it's your app logs. They're trying to figure out for black Friday, what happens? It's Lilly saying, Hey, every minute they'll cut a new log. You're able to say, listen, these are the users that were in that system for an hour. And here's a different things. They do. The fact of the matter is if you cut it off, you lose all that fidelity, all that data. So it's really important that to have. So if you're trying to figure out either what happened for security, what happened for on a performance, or if you're trying to figure out, Hey, I'm VP of product or growth, how do I cross sell things? >>You need to know what everyone's doing. If you're not handling Jason natively, like we're doing either your, it keeps on expanding on black Friday. All of a sudden the logs get huge. And the next day it's not, but it's really powerful data that you need to harness for business values. It's, what's going to drive growth. It's what's going to do the digital transformation. So without the technology, you're kind of blind. And to be honest, you don't know. Cause a data scientist is kind of deleted the data on you. So this is big for the business and digital transformation, but also it was such a pain. The data scientists in DBS were forced to just basically make it simple. So it didn't blow up their system. We allow them to keep it simple, but yes, >>Both power. It reminds me if you like, go on vacation, you got your video camera. Somebody breaks into your house. You go back to Lucas and see who and that the data's gone. The video's gone because it didn't, you didn't, you weren't able to save it cause it's too >>Expensive. Well, it's funny. This is the first day source. That's driving the design of the database because of all the value we should be designed the database around the information. It stores not the structure and how it's been organized. And so our viewpoint is you get to choose your structure yet contain all that content. So if a vendor >>It says to kind of, I'm a customer then says, Hey, we got Jason support. What questions should I ask to really peel the onion? >>Well, particularly relational. Is it a relational access to that data? Now you could say, oh, I've ETL does Jason into it. But chances are the explosion of Jason permutations of one row to a million. They're probably not doing the full representation. So from our viewpoint is either you're doing a blob type access to proprietary Jason APIs or you're picking and choosing those, the choices say that is the market thought. However, what if you could take all the vegetation and design your schema based on how you want to consume it versus how you could store it. And that's a big difference with, >>So I should be asking how, how do I consume this data? Are you ETL? Bring it in how much data explosion is going to occur. Once I do this, and you're saying for chaos, search the answer to those questions. >>The answer is, again, our philosophy simply stream your data into your cloud object, storage, your data lake and with our index technology and our data refinery. You get to create views, dynamic the incident, whether it's a terabyte or petabyte, and describe how you want your data because consumed in a relational way or an elastic search way, both are consumable through our data refinery, which is >>For us. The refinery gives you the view. So what happens if someone wants a different view, I want to actually unpack different columns or different matrices. You able to do that in a virtual view, it's available immediately over petabytes of data. You don't have that episode where you come back, look at the video camera. There's no data there left. So that's, >>We do appreciate the time and the explanation on really understanding Jason. Thank you. All right. And thank you for watching this cube conversation. This is Dave Volante. We'll see you next time.

[Music] hi I'm Peter Burris and welcome to another Cube conversation from the cube studios here in beautiful Palo Alto California today we're going to talk about some new things that are happening in the security world obviously this is one of the most important domains within the technology industry and increasingly because of digital business in business overall now to do that we've asked Eric manki to come back Derick is the chief of security insights and global threat alliances at Fort Net Derek welcome back to the cube absolutely the same feel the same way Derek okay so we're going to get into some some predictions about what the bad guys are doing and some predictions about what the defenses are doing how we're going to see them defense opportunities improve but let's set the stage because predictions always are made on some platforms some understanding of where we are and that has also changed pretty dramatically so what's the current state in the overall security world Derek yeah so what we saw this year in 2019 a lot is a big increase on automation and I'm talking from an attackers point of view I think we talked about this a little bit earlier in the year so what we've been seeing is the use of frameworks to enhance sort of the day-to-day cycles that cyber criminals and attackers are using to make their you know criminal operations is that much more efficient sort of a well-oiled machine so we're seeing toolkits that are taking you know things within the attack cycle and attack change such as reconnaissance penetration you know exploitation getting into systems and just making that that much quicker so that that window to attack the time to breach has been shrinking thanks to a lot of these crime kits and services that are offered out there now one other comment on this or another question that I might have on this is that so speed is becoming an issue but also the risk as digital business takes on a larger four portion of overall business activities that ultimately the risks and costs of doing things wrong is also going up if I got the right yeah absolutely for sure and you know it's one of those things that it's the longer that a cybercriminal has a foothold in your system or has the opportunity to move laterally and gain access to other systems maybe it's your I o T or you know other other platforms the higher the risk right like the deeper down they are within an attack cycle the higher the risk and because of these automated toolkits are allowing allowing them to facilitate that it's a catalyst really right they can get into the system they can actually get out that much quicker the risk is a much higher and we're talking about risk we're talking about things like intellectual property exfiltration client information this sort of stuff that can be quite damaging to organizations so with the new foundation of speed is becoming an increasingly important feature probably think about security and the risks are becoming greater because digital assets are being recognized as more valuable why do you take us through some of the four Donets predictions on some of the new threats or the threat landscape how's the threat landscape changing yeah so as I said we've already seen this shift in automation so what I would call the basics I mean knowing the target trying to break into that target right when it comes to breaking into the target cyber criminals right now they're following the path of least resistance right they're finding easy ways that they can get into IOT devices I into other systems in our world when we talk about penetration or breaking into systems it's through zero days right so the idea of a zero day is essentially a cyber weapon there's movies and Hollywood that have been made off of this you look at attacks like Stuxnet in the past they all use zero day vulnerabilities to get into systems all right so the idea of one of the predictions we're seeing is that cyber criminals are gonna start to use artificial intelligence right so we talk about machine learning models and artificial intelligence to actually find these zero days for them so in the world of an attacker to find a zero day they have to do a practice called fuzzing and fuzzing is basically trying to trick up computer code right so you're throwing unverified parameters out at your turn T of throwing and unanticipated sequences into code parameters and and input validation and so forth to the point that the code crashes and that's from an attackers point of view that's when you take control of that code this how you know finding weapons into system cyber weapons in this systems work it typically takes a lot of a lot of resource it takes a lot of cycles it takes a lot of intelligence that takes a lot of time to discovery we can be talking on month for longer it's one of the predictions that we're hitting on is that you know cyber criminals are gonna start to use artificial intelligence fuzzing or AI F as I call it to be able to use AI to do all of that you know intelligent work for them so you know basically having a system that will find these gateways if you will these these you know new vulnerabilities into systems so sustained use of AI F to corrupt models so that they can find vulnerabilities that can then be exploited yeah absolutely and you know when it comes to the world of hacking and fuzzing it's one of the toughest things to do it is the reason that zero days are worth so much money you know they can suffer hundreds of thousands of dollars on darknet and in the cyber criminal you know economy so it's because they're talk talk to finally take a lot of resources a lot of intelligence and a lot of effort to be able to not only find the vulnerability but then actively attack it and exploit it right there's two phases to that yeah so the idea is by using part of the power of artificial intelligence that cyber criminals will start to leverage that and harness it in a bad way to be able to not only discover you know these vulnerabilities but also create that weapon right create the exploit so that they can find more you know more holes if you will or more angles to be able to get into systems now another one is that virtualization is happening in you know what the good guys as we virtualized resources but is it also being exploited or does it have the potential be exploited by the bad guys as well especially in a swarming approach yeah virtualization for sure absolutely so the thing about virtualization too is you often have a lot of virtualization being centralizes especially when we talk about cloud right so you have a lot of potential digital assets you know valuable digital assets that could be physically located in one area so when it comes to using things like artificial intelligence fuzzing not only can it be used to find different vulnerabilities or ways into systems it can also be combined with something like I know we've talked about the const that's warm before so using you know multiple intelligence infected pieces of code that can actually try to break into other virtual resources as well so virtualization asked definitely it because of in some cases close proximity if you will between hypervisors and things like this it's also something of concern for sure now there is a difference between AI fai fuzzing and machine learning talk to us a little bit about some of the trends or some of the predictions that pertain to the advancement of machine learning and how bad guys are going to exploit that sure so machine learning is a core element that is used by artificial intelligence right if you think of artificial intelligence it's a larger term it can be used to do intelligent things but it can only make those decisions based off of a knowledge base right and that's where machine learning comes into place machine learning is it's data it's processing and it's time right so there's various machine learning learning models that are put in place it can be used from everything from autonomous vehicles to speech recognition to certainly cybersecurity and defense that we can talk about but you know the other part that we're talking about in terms of reductions is that it can be used like any tool by the bad guys so the idea is that machine learning can be used to actually study code you know from from a black hat attacker point of view to studying weaknesses in code and that's the idea of artificial intelligence fuzzing is that machine learning is used to find software flaws it finds the weak spots in code and then it actually takes those sweet spots and it starts probing starts trying to attack a crisis you know to make the code crash and then when it actually finds that it can crash the code and that it can try to take advantage of that that's where the artificial intelligence comes in right so the AI engine says hey I learned that this piece of software or this attack target has these weak pieces of code in it that's for the AI model so the I fuzzy comes into place to say how can I actually take advantage how can i exploit this right so that's where the AI trussing comes into play so we've got some predictions about how black hats and bad guys are going to use AI and related technologies to find new vulnerabilities new ways of exploiting things and interacting new types of value out of a business what are the white hats got going for them what are their some of the predictions on some of the new classes of defense that we're going to be able to put to counter some of these new classes of attacks yeah so that's that's you know that's honestly some of the good news I believe you know it's always been an armor an arms race between the bad guys and the good guys that's been going on for decades in terms of cybersecurity often you know the the bad guys are in a favorable position because they can do a million things wrong and they don't care right from the good guys standpoint we can do a million things right one thing wrong and that's an issue so we have to be extra diligent and careful with what we do but with that said you know as an example of 49 we've deployed our forty guard AI right so this is six years in the making six years using machine learning using you know precise models to get higher accuracy low false positives to deploy this at reduction so you know when it comes to the defensive mechanism I really think that we're in the drivers position quite frankly we have better technology than the Wild West that they have out on the bad guys side you know from an organization point of view how do you start combating this sort of onslaught of automation in AI from from the bad guys side well you gotta fight fire with fire right and what I mean by that is you have to have an intelligent security system you know perimeter based firewalls and gateways they don't cut it anymore right you need threat intelligence you need systems that are able to orchestrate and automate together so in different security products and in your security stack or a security fabric that can talk to each other you know share intelligence and then actually automate that so I'm talking about things like creating automated security policies based off of you know threat intelligence finding that a potential threat is trying to get into your network that sort of speed through that integration on the defensive side that intelligence speed is is is the key for it I mean without that any organization is gonna be losing the arms race and I think one of the things that is also happening is we're seeing a greater willingness perhaps not to share data but to share information about the bad things that are happening and I know that fort and it's been something at the vanguard of ensuring that there's even better clearing for this information and then driving that back into code that actually further automates how customers respond to things if I got that right yeah you hit a dead-on absolutely you know that is one of the key things that were focused on is that we realized we can't win this war alone right nobody can on a single point of view so we're doing things like interoperating with security partners we have a fabric ready program as an example we're doing a lot of work in the industry working with as an example Interpol and law enforcement to try to do attribution but though the whole endgame what we're trying to do is to the strategy is to try to make it more expensive for cyber criminals to operate so we obviously do that as a vendor you know through good technology our security fabric I integrated holistic security fabric and approach to be able to make it tougher you know for attackers to get into systems but at the same time you know we're working with law enforcement to find out who these guys are to go after attribution prosecution cut off the head of the snake as I call it right to try to hit cyber criminal organizations where it hurts we're also doing things across vendor in the industry like cyber threat Alliance so you know forty knots a founding member of the cyber threat Alliance we're working with other security vendors to actually share real time information is that speed you know message that we're talking about earlier to share real time information so that each member can take that information and put it into you something actionable right in our case when we get intelligence from other vendors in the cyber threat Alliance as an example we're putting that into our security fabric to protect our customers in new real-time so in sum we're talking about a greater value from being attacked being met with a greater and more cooperative use of technology and process to counter those attacks all right yeah absolutely so open collaboration unified collaboration is is definitely key when it comes to that as well you know the other thing like I said is is it's the is the technology piece you know having integration another thing from the defensive side too which is becoming more of a topic recently is deception deception techniques this is a fascinating area to me right because the idea of deception is the way it sounds instead of to deceive criminals when they're coming knocking on your door into your network so it's really what I call like the the house of a thousand mirrors right so they get into your network and they think they're going to your data store but is it really your data store right it's like it's there's one right target and a thousand wrong targets it's it's a it's a defensive strategy that organizations can play to try to trip up cyber criminals right it makes them slower it makes them more inaccurate it makes them go on the defensive and back to the drawing board which is something absolutely I think we have to do so it's very interesting promising you know technology moving forward in 2019 to essentially fight back against the cyber criminals and to make it more expensive to get access to whatever it is that they want Derek max Lilly yeah Derrick McKey chief of security insights and global threat Alliance this is for net thanks once again for being on the cube it's a pleasure anytime look forward to the next chat and from Peter Burroughs and all of us here at the cube in Palo Alto thank you very much for watching this cube conversation until next time you

this one minute I'm here with my co-host Jeff Frick who we just fresh off of the AWS summit the Amazon event Jeff and I covered that and we're here at knowledge 13 now this conference is all about the notion of going from IT as a service organization changing high teas mantra from no to now that really is the theme of this conference and we're here with Frank's luton who's the president and CEO of service now Frank welcome back to the cube thanks good to be here that's good to see you again we had you on that vm world is great story when we first introduced service now to our community you just fresh off the keynote fantastic keynote by the way thank you you had strong themes i mentioned the from no to now you talked about itu gave a little little tongue-in-cheek joke about the line outside the the rmv the Registry of Motor Vehicles and that's sort of the the idea is you guys are transforming IT from an organization that is trying to manage demand push off demand saying no we'll get it in six months it'll cost you five million dollars to one that really is redesigning IT processes around the globe so first of all welcome back congratulations how do you feel after that keynote I have to work a lot of energy in that room and it was electrifying it was awesome well one of the one of the guys in the panel stopped when you had asking the question I think was the guy from NY yes he said even stop you looked at the audience said i love this crowd that was a great crowd we gave a little goop out to the audience so talk about from know to now how'd you come up with that theme and you know give us a little color behind you know it's it's actually not easy for for us to communicate about service now desk to to lay people in sight unless you have lived in sight I t you just most people don't even know what I t really does on the day-to-day basis right so we've lived a fairly insular existence because you know everybody knows what sales people do and to some degree about HR doesn't finance people but I t it's a bit of a you know a bit of a mystery to what most folks do right but most people do know however is that the service experience with IT has been and challenging what's all we say I mean it's been you know sort of a service experience where if you have to ask the answer was going to be no right because IT organizations have been super preoccupied with infrastructure rapid change in the infrastructure for the last 30 40 years nothing ever set still long enough for us to really master the architecture and the platforms are really stabilizing mature our systems and they have to keep moving so you get pretty cranky it's back to your organization having to live that kind of life so their their their reputation for service has not been stellar and I love making the joke during the keynote their ranking right down there with legal in the basement you know of the corporate enterprise you know so well so talk a little bit about sort of how you guys you know go into an organism's you start with the IT organization right in helping them sort of automated processes connect all these different processes but you've been through your platform expanding out to other parts of the organization the irony is that I T which is the most technology savvy organization in the price as the least management sophistication in terms of managing their own activity which you know I duck to the CIO of a very large consumer gets company he said where does she make her son it's inexcusable right here here we are running milk that going in dollar budgets and staffs with tens of thousands of people and we're running it on spreadsheets email excel project management tools this is ridiculous right we don't have real information in near real time and show that we can drive our business as opposed to being driven by it right i key executives have a tendency to run from one crisis to another with their hair on fire and that's sort of the mental model and a note of now message is about out of a get these people out of this you know reactive crisis mode to where they become full-blown business partners and they start you know bring your guide to enterprise and in a very transformative way or they become the people that bring innovation to the enterprise you know here's so much Frank about shadow I teach my colleague Jeff Frick and I were at the AWS some of the few weeks and you see a lot of these cloud companies you mentioned your keynote Salesforce the salespeople workday talk to HR people they sort n run IT certainly amazon is the poster child for shadow IT but you know Jeff we have that sort of notion where IT people are not the center of the new cloud universe but that's different for service now yes it's very different but the other thing brought up amazon your keynote and how they've kind of fine what kind of a user expectation experiences with an application on the web a level of service a level of delivery and then you've got AWS its kind of the girl child of shadow IT but you guys are coming in really as the enabler to let the internal IT guys actually have the tools to compete with with guys trying to go around it really exact with delivery platform I mean we're trying to turn the tables here right because the entire history of IT is one big end around righty the many computer was an end-around of the glasshouse client-server was really pcs you know dribbling into departmental environments suffer as a service was an incredible end around people in there didn't realize it was seeping into the enterprise right now things like 80 lbs now infrastructure right is actually finding its way so we're saying look you know worthy Enterprise IT cloud company right we are going to empower and enable IT to be driving rather than just being driven and being taken over and run over by by events because that's what's been happening here's the goodness IT can start withdrawing and getting out of the business of infrastructure which is what they've been doing forever infrastructure is very challenging pretty soon that's going to be somebody else's problem right infrastructure goes behind the cooking all you have to do is in network connection so that means that the role of IT is moving from you know keeping the lights on to you know we're going to be the people who are experts at defining structuring and automating service relationships and so does relationship management I mean at this and I make a joke about you know your hole in the inbox of email you know it's full of basically service relationships that are unstructured and unlimited and undefined right right and there is this incredible opportunity to go aptet with record-keeping workflow systems and that's what we want to enable and empower IT to do right we had to give you a quick example actually very interesting we talked to our one of our very large retail customers and the supply chain office unbeknownst to us went to IT and said hey we want to build this app what should we use and Ikey said no you should try and do that on service now what's the app a supply chain office in a retail environment what they do is they take requests all day long stores distribution centers suppliers and they're rebalancing you know product right place right time right right product and they were doing that everybody running spreadsheets and emails and people constantly calling what's the update on my request and they decide no we're going to go to a record-keeping workflow system and from the moment you know they started using that system all of a sudden they had full visibility to a what the volume was of issues that was coming in but the nature of the volume was how well they were doing on their SOS relative to their storage and distribution centers and they were able to structurally go after you know the things that were a constant them grief because they just didn't know right so very simply in very short period of time you know they transformed themselves from the supply chain all those Devils running around like a chicken with his head cut off the people that were actually driving to supply chain now now supply chain management in the retail organization it's super mission-critical right because their results are directly impacted by having right product right time right place simple example where we moving from email and Excel to a record-keeping workflow system any impact with literally within 30 40 days is enormous yeah you hear that a lot of people just using Excel using email we talked to we talking some customers last night we talked to some perspective customers that were in so to check it out and they were big Lotus no shop and is describing sort of the difficulties and challenges of it you will sign them up I can almost see it but the other thing so so this notion of your customer base is very powerful in fact I tweeted out I said the service now has a sick logo basis and we said is that a typo said no sick like that sick touchdown catch it isn't good yeah sick is it good but I mean which I we hear from land o lakes Red Hat metropcs KPM nor Brent I mean just on and on and on at Facebook Intel google or customers what are some other favorite customer stories you hear a lot of the same themes Frank you know we used to use spreadsheets with using email or reliant on all these disparate processes bringing them all together getting some some other you know favorite stories of yours for customers I I relayed a bunch of him on stage this morning right beasties it's just extraordinary to me the the corporate America I mean you mentioned some of them but you know the people we had on stage you know AIG you know coca-cola company's general electric demand this is United States Army right and they owe is yeah New York Stock Exchange eli lilly big pharmaceuticals bristol-myers squibb they all have the same set of issues they have a completely fractured fragmented sprawled acti environment right and here's the interesting history we have not had CIOs that long you know I T used to report into a division next sag or a regional exact and there really wasn't one person that was responsible for running IT throughout the global enterprise because it was just a decentralized function by the way example when you in Europe yeah I ray mighty and I certainly wasn't IT guy stuff and by the way it wasn't my priority either you know it was just by the way that's for some of the history you know comes from so CIO comes in and they are now charged with you're going to run this thing they're not running anything they're being run by it right so until you get to global IT processes I mean City another you know big name they set to as rogue global bank that we don't have global IT right it is the inefficiency and the lack of ability to drive and manage is unacceptable for these very sophisticated large institutions it's embarrassing really you know yeah I mean you really can't go global as a come you can't scale your business not having all these surprises so to me it's about global scaling and it's about the business value of both having ITB accountable but also have the metrics and the visibility to be able to demonstrate the value to the organization you see i SAT with our executive sponsor from bristol-myers squibb last night and she said i got data and i got it in real time and i know it's good so I'm not putting my service providers on their heels you know before they were you know everything was you know in the realm of you know interpretation and fuzzy fuzzy right and now it's like I have data and I'm driving and I'm changing behavior right so the empowering effective it has mighty organizations it's just stomach right I thought that empowering note that came up in your keynote was interesting how the IT organizations themselves and their presentation now to their internal customers are looking more like a company you know they're they're being cute there yeah I'm taking branding they're there they're not just button pushers in and as you said you know infrastructure operators they are trying to be contributors to the business and keeping some this automobile shade of nail them to it's even stronger than now yes they want to be contributors to the business but they want to be the playmakers they wanted me to go to guys give me the ball you know that that's where we want to you know take itt there that people that really understand how to change how work gets done the enterprise I thought you characterize the dwelling experience in IT people have been running from crisis to crisis and they need to be more proactive so talk about how your system allows them to be more proactive well it's all about going from a message oriented environment to a system or an a message or environment is the one way l know it's email it's text you know it's voice right that doesn't work because you know we're just talking right systems have the ability to drive behavior because you know every time you send an email you should think to yourself could i create a service request instead right because a service request has a defined data ship it goes into a database it gets assigned you know in a workflow operation it has metrics around it if it doesn't get responded to a certain amount of time it gets accelerated to the escalator to the next level or management right so the process is defined structure to automate it is going to run its course right whether you know people are participating in it or not with this great example one of our customers equinix delilah or Brian Lily's here actually is a CIO and he said they will sell funny you know we have a system that all my life cycle application where our developers check-in fixes and enhancement to a particular software release for an application and he says because they know to work flows is completely structured an automated everybody knows that they don't get their fixes enhancement in by a certain time poof the dashboards pop the higher-ups see you know who's behind and who's not and that the threat alone of the transparency and visibility that the process introduces causes everybody there run harder right so people won't have to run around with the whip like where are you you know the process is driving is like a hamster on a treadmill you know so Freki used amazon as an example of the user experience that you know you covet as a CEO of this company and you believe you're your customer base desires at the back end also when you talk about companies like Amazon and Facebook and Google they are super highly automated you also talked about lights out automation yeah now normally IT organizations are managed now they're managed by humans they're not highly automated are you are you seeing your customers able to get to that sort of vision that you're talking about that lights-out automation almost like the hyperscale guys you know it's a super important custody I said during the cleanup or were overstaffed and under automated NIT we have reams of people on staff any large financial institutions have tens of thousands of people on staff they're bigger than any technology company right why is that it's because things are very laborious laborious and manual right the processes that they run require so many touch points I mean one of the things that we always tell our customers when you can reimplement these processes do not take your legacy forward because your legacy is very manual you remember the inbox in the outbox when we have physical in boxes and other boxes and now we know we have our laptop why do we have an inbox and outbox right does this message really this cross why are you even involved in this process right so we have to invert the process it's not like wouldn't it be nice for you to be involved in this process there'd better be a very good reason for you to touch this process because the moment you touch it you know we're going from the speed of light to you know the speed of the dirt road that Franco so service now is really in a rocket ship right now and you've demonstrated you've got a track record of being able to be sometimes call jump three myself throwing gasoline on the fire you look very good at that you got 1,600 customers you're growing like crazy but you're under penetrated in your target which is the global 2000 you're only fourteen percent penetrated in the global 2000 so get a long way to go in this journey we're very excited to be you know covering this event really appreciate you guys having us here Frank's loot Minh will give you the last word and then we'll wrap you know this is actually one of the great things that we are so on the front hood and they're penetrated because our investors are like wow you've got a lot of runway you know considering the size company that we we already are and you know the rate of monetization of our business is is extraordinarily I in other words the share of wallet that service now represents and the enterprise is so much larger than people had ever considered or thought because it was not an existing category that was fully metastasized and visible it's new it's emergent it is really transforming how people you know look at technology and process automation and so on now we're gonna be here all week covering knowledge we've got it we're going to double-click on so how is it that service now is able to deliver this cloud functionality the secret is in the single system of record the CMDB and that is not a trivial thing to do we didn't talk about that with Frankie could talk about it but we don't want to steal you know the name of thunder yeah fred muddies going to be on RNA Justin who's the CTO we're going to go deep into sort of how service now actually accomplishes this architecture Lee what their vision is so Frank thanks very much for spending so much time I know you're busy you got to run but appreciate you coming on terrific thanks for having me alright thanks for watching everybody keep it right there we'll be right back with more we're live from Las Vegas ServiceNow knowledge we'll be right back this is the Q cute baby rock and roll