>> Live, from Boston, it's theCUBE, covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back everyone to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host Rebecca Knight, along with my co-host Paul Gillin. We're joined by Dr. Prakriteswar Santikary known as Dr Santi. He is the Vice President and Global Chief Data Officer at eResearch Technology. Thank you so much for coming back on theCUBE. >> Yeah, thank you for inviting me. >> So Dr Santi tell our viewers a little bit about eResearch Technology. You're based in Marlborough... >> Yeah, so we're in Boston, but ERT has been around since 1977 and we are a data and technology company that minimizes risks and uncertainties within clinical trial space and our customers are pharmaceutical companies, biotechnology companies, medical device companies, and where they really trust us in terms of running their clinical trials on our platform. So we have been around over 40 years, so we have seen a thing or two in the space. It's a very complex domain a very highly regulated as you know, because it's dealing with patients lives. So we take huge pride in what we do. >> We know how involved clinical trials can be long, very expensive, how are the new tools, big data impacting the cost? >> Well, that has been an age old problem within the clinical trials, usually a drug takes about eight to 12 years and costs about $2 billion from start to commercialization. So it's a very lengthy, manual and arduous process. So there are lots going on in this clinical trial domain that's tries to shorten the timeline and employing of big data technologies, modern data platform to expedite data processing, data collection from mobile devices and health technologies and all these. Artificial intelligence is playing a big role in terms of disrupting some of these domains, particularly if you see the protocol development down to patient selection, down to study design, then study monitoring. So you need to do all those things and each takes long long long time, so AI with the big data technologies is they're really making a difference. >> In what ways? >> For example, patient selection is one of the huge pin points in any clinical trial, because without patients there are no clinical trials. Particularly when you try to launch a drug, you will have to identify the patients, select the patients and not only select the patients, you have to make sure those patients stay with the clinical trials throughout the duration of the trial. So patient engagement is also a big deal. So with these big data technologies, like now you can see all this mobile health devices that patients are wearing using which you can monitor them. You can remind, send them a reminder, take your drug or you can send a text saying that there will be a clinical visit at that site come at seven o'clock, don't come at nine o'clock. So these kind of encouragement and constant feedback loop is really helping patients stay engaged. That is critical. Then matching patients with the given clinical trials is a very manual and arduous process, so that's where the algorithms is helping. So they are just cranking up real world evidence data for example claims data, prescription data and other type of genomic data and they're matching patients and the clinical trial needs. Instead of just fishing around in a big pond and find out, okay I need three patients. So go and fish around the world to get the three patients. That's why current process is very manual and these AI techniques and behind technologies and big data technologies are really disrupting this industry. >> So are the pharmaceutical companies finding that clinical trials are better today because patients are more engaged and they are getting as you said this constant reminder, take your drug, stay with us. Do you think that they are, in fact, giving them better insights into the efficacy of the drug? >> Yes because you will see their compliance rate is increasing, so because remember when they have to fill out all these diaries, like morning diaries evening diaries, when they are taking which medicine, when they are not taking. It used to be all manual paper driven, so they would forget and particularly think about a terminally ill patient, each day is so critical for them. So they don't have patience, nor do they have time to really maintain a manual diary. >> Nor do their caregivers have the time. Right. >> So this kind of automation is really helping and that is also encouraging them as well, that yeah somebody is really caring about me. We are not just a number, patient is not a number that somebody is really relating to them. So patient engagement, we have a product that specifically focuses around patient engagement. So we do all these phase one through phase four trials, one, two, three, four and then forced marketing, obviously, but through the entire process, we also do patient engagement, so that we help our customers like pharmaceutical companies and biotechnology companies so that they can run their trials with confidence. >> How about analyzing the data that you collect from the trials, are you using new techniques to gain insights more quickly? >> Yes, we are. We just recently launched a modern data platform, a data lake while we are consolidating all the data and anonymizing it and then really applying AI techniques on top of it and also it is giving us real time information for study monitoring. Like which side is not complying, with patients or not complying, so if the data quality is a big deal in clinical trials, because if the quality is good, then FDA approval, there is a chance that FDA may approve, but if the data quality is bad, forget about it, so that's why I think the quality of the data and monitoring of that trial real time to minimize any risks before they become risks. So you have to be preempted, so that's why this predictive algorithms are really helping, so that you can monitor the site, you can monitor individual patient through mHealth devices and all these and really pinpoint that, hey, your clinical trials are not going to end on time nor on budget. Because here you see the actual situation here, so, do something instead of waiting 10 years to find that out. So huge cost saving and efficiency gain. >> I want to ask about data in healthcare in general because one of the big tensions that we've talked about today is sort of what the data is saying versus what people's gut is saying and then in industry, it's the business person's gut but in healthcare it is the doctor, the caregivers' gut. So how are you, how have you seen data or how is data perceived and is that changing in terms of what the data shows that the physician about the patient's condition and what the patient needs right then and there, versus what the doctors gut is telling him that the patient needs? >> Yeah and that's where that augmentation and complementary nature, right? So AI and doctors, they're like complementing each other, So predictive algorithm is not replacing doctors the expertise, so you still need that. What AI and predictive algorithm is playing a big role is in expediting that process, so instead of sifting through manual document so sifting through this much amount of document, they would only need to do this much of document. So then that way it's minimizing that time horizon. It's all about efficiency again, so AI is not going to be replacing doctors anytime soon. We still need doctors, because remember a site is run by a primary investigator and primary investigator owns that site. That's the doctor, that's not a machine. That's not an AI algorithm, so his or her approval is the final approval. But it's all about efficiency cost cutting and bringing the drugs to the market faster. If you can cut down these 12 years by half, think about that not only are you saving lots of money, you are also helping patients because those drugs are going to get to the market six year earlier. So you're saving lots of patients in that regard as well. >> One thing that technologies like Watson can do is sort through, read millions of documents lab reports and medical journals and derive insights from them, is that helping in the process of perhaps avoiding some clinical trials or anticipating outputs earlier? >> Yes, because if you see Watson run a clinical study with Cleveland Clinic recently or Mayo Clinic I think or maybe both. While they reduce the patient recruitment time by 80%, 80%. >> How so? >> Because they sweep through all those documents, EMR results, claims data, all this data they combined-- >> Filter down-- >> Filter down and then say, for this clinical trial, here are the 10 patients you need. It's not going to recommend to who those 10 patients are but it will just tell you that, the goal is the average locations, this that, so that you just focus on getting those 10 patients quickly instead of wasting nine months to research on those 10 patients and that's a huge, huge deal. >> And how can you trust that, that is right? I mean I think that's another question that we have here, it's a big challenge. >> It is a challenge because AI is all about math and algorithm, right? So when you, so it's like, input black box, output. So that output may be more accurate than what you perceive it to be. >> But that black box is what is tripping me up here. >> So what is happening is sometimes, oftentimes, if it is a deep learning technique, so that kind of lower level AI techniques. It's very hard to interpret that results, so people will keep coming back to you and say, how did you arrive at that results? And that's where most of the, there are techniques like Machine Learning techniques that are easily interpretable. So you can convince FDA folks or other folks that here is how we've got to it, but there are a deep learning techniques that Watson uses for example, people will come and, how did you, how did you arrive at that? And it's very hard because those neural networks are multi-layers and all about math, but as I said, output may be way more accurate, but it's very hard to decipher. >> Right, exactly. >> That's the challenge. So that's a trust issue in that regard. >> Right, well, Dr. Santi, thank you so much for coming on theCUBE. It was great talking to you. >> Okay, thank you very much. Thanks for inviting. >> I'm Rebecca Knight for Paul Gillin we will have more from the IBM CDO Summit in just a little bit. (upbeat music)

>> Live from the MIT campus in Cambridge, Massachusetts, it's the Cube, covering the 12th Annual MIT Chief Data Officer and Information Quality Symposium. Brought to you by SiliconANGLE Media. >> Welcome back to the Cube's coverage of MITCDOIQ here in Cambridge, Massachusetts. I'm your host, Rebecca Knight, along with my co-host, Peter Burris. We're joined by Dr. Santikary, he is the vice-president and chief data officer at ERT. Thanks so much for coming on the show. >> Thanks for inviting me. >> We're going to call you Santi, that's what you go by. So, start by telling our viewers a little bit about ERT. What you do, and what kind of products you deliver to clients. >> I'll be happy to do that. The ERT is a clinical trial small company and we are a global data and technology company that minimizes risks and uncertainties within clinical trials for our customers. Our customers are top pharma companies, biotechnologic companies, medical device companies and they trust us to run their clinical trials so that they can bring their life-saving drugs to the market on time and every time. So we have a huge responsibility in that regard, because they put their trust in us, so we serve as their custodians of data and the processes, and the therapeutic experience that you bring to the table as well as compliance-related expertise that we have. So not only do we provide data and technology expertise, we also provide science expertise, regulatory expertise, so that's one of the reasons they trust us. And we also have been around since 1977, so it's almost over 50 years, so we have this collective wisdom that we have gathered over the years. And we have really earned trust in this past and because we deal with safety and efficacy of drugs and these are the two big components that help MDA, or any regulatory authority for that matter, to approve the drugs. So we have a huge responsibility in this regard, as well. In terms of product, as I said, we are in the safety and efficacy side of the clinical trial process, and as part of that, we have multiple product lines. We have respiratory product lines, we have cardiac safety product lines, we have imaging. As you know, imaging is becoming more and more so important for every clinical trial and particularly on oncology space for sure. To measure the growth of the tumor and that kind of things. So we have a business that focuses exclusively on the imaging side. And then we have data and analytics side of the house, because we provide real-time information about the trial itself, so that our customers can really measure risks and uncertainties before they become a problem. >> At this symposium, you're going to be giving a talk about clinical trials and the problems of, the missteps that can happen when the data is not accurate. Lay out the problem for our viewers, and then we're going to talk about the best practices that have emerged. >> I think that clinical trial space is very complex by its own nature, and the process itself is very lengthy. If you know one of the statistics, for example, it takes about 10 to 15 years to really develop and commercialize a drug. And it usually costs about $2.5 to 3 billion. Per drug. So think about the enormity of this. So the challenges are too many. One is data collection itself. Your clinical trials are becoming more and more complex. Becoming more and more global. Getting patients to the sites is another problem. Patient selection and retention, another one. Regulatory guidelines is another big issue because not every regulated authority follows the same sets of rules and regulations. And cost. Cost is a big imperative to the whole thing, because the development life-cycle of a drug is so lengthy. And as I said, it takes about $3 billion to commercialize a drug and that cost comes down to the consumers. That means patients. So the cost of the health care is growing, is sky-rocketing. And in terms of data collection, there are lots of devices in the field, as you know. Wearables, mobile helds, so the data volume is a tremendous problem. And the vendors. Each pharmaceutical companies use so many vendors to run their trials. CRO's. The Clinical Research Organizations. They have EDC systems, they can have labs. You name it. So they outsource all these to different vendors. Now, how do you coordinate and how do you make them to collaborate? And that's where the data plays a big role because now the data is everywhere across different systems, and those systems don't talk to each other. So how do you really make real-time decisioning when you don't know where your data is? And data is the primary ingredient that you use to make decisions? So that's where data and analytics, and bringing that data in real-time, is a very, very critical service that we provide to our customers. >> When you look at medicine, obviously, the whole notion of evidence-based medicine has been around for 15 years now, and it's becoming a seminal feature of how we think about the process of delivering medical services and ultimately paying it forward to everything else, and partly that's because doctors are scientists and they have an affinity for data. But if we think about going forward, it seems to me as though learning more about the genome and genomics is catalyzing additional need and additional understanding of the role that drugs play in the human body and it almost becomes an information problem, where the drug, I don't want to say that a drug is software, but a drug is delivering something that, ultimately, is going to get known at a genomic level. So does that catalyze additional need for data? is that changing the way we think about clinical trials? Especially when we think about, as you said, it's getting more complex because we have to make sure that a drug has the desired effect with men and women, with people from here, people from there. Are we going to push the data envelope even harder over the next few years? >> Oh, you bet. And that's where the real world evidence is playing a big role. So, instead of patients coming to the clinical trials, clinical trial is going to the patient. It is becoming more and more patient-centric. >> Interesting. >> And the early part of protocol design, for example, the study design, that is step one. So more and more the real world evidence data is being used to design the protocol. The very first stage of the clinical trial. Another thing that is pushing the envelope is artificial intelligence and other data mining techniques and now people can be used to really mine that data, the MAR data, prescription data, claims data. Those are real evidence data coming from the real patients. So now you can use these artificial intelligence and mission learning techniques to mine that data then to really design the protocol and the study design instead of flipping through the year MAR data manually. So patient collection, for example, is no patients, no trials, right? So gathering patients, and the right set of patients, is one of the big problems. It takes a lot of that time to bring those patients and even more troublesome is to retain those patients over time. These, too, are big, big things that take a long time and site selection, as well. Which site is going to really be able to bring the right patients for the right trials? >> So, two quick comments on that. One of the things, when you say the patients, when someone has a chronic problem, a chronic disease, when they start to feel better as a consequence of taking the drug, they tend to not take the drug anymore. And that creates this ongoing cycle. But going back to what you're saying, does it also mean that clinical trial processes, because we can gather data more successfully over time, it used to be really segmented. We did the clinical trial and it stopped. Then the drug went into production and maybe we caught some data. But now because we can do a better job with data, the clinical trial concept can be sustained a little bit more. That data becomes even more valuable over time and we can add additional volumes of data back in, to improve the process. >> Is that shortening clinical trials? Tell us a little bit about that. >> Yes, as I said, it takes 10 to 15 years if we follow the current process, like Phase One, Phase Two, Phase Three. And then post-marketing, that is Phase Four. I'm not taking the pre-clinical side of these trials in the the picture. That's about 10 to 15 years, about $3 billion kind of thing. So when you use these kind of AI techniques and the real world evidence data and all this, the projection is that it will reduce the cycle by 60 to 70%. >> Wow. >> The whole study, beginning to end time. >> So from 15 down to four or five? >> Exactly. So think about, there are two advantages. One is obviously, you are creating efficiency within the system, and this drug industry and drug discovery industry is rife for disruption. Because it has been using that same process over and over for a long time. It's like, it is working, so why fix it? But unfortunately, it's not working. Because the health care cost has sky-rocketed. So these inefficiencies are going to get solved when we employ real world evidencing into the mixture. Real-time decision making. Risks analysis before they become risks. Instead of spending one year to recruit patients, you use AI techniques to get to the right patients in minutes, so think about the efficiency again. And also, the home monitoring, or mHealth type of program, where the patients don't need to come to the sites, the clinical sites, for check-up anymore. You can wear wearables that are MDA regulated and approved and then, they're going to do all the work from within the comfort of their home. So think about that. And the other thing is, very, terminally sick patients, for example. They don't have time, nor do they have the energy, to come to the clinical site for check-up. Because every day is important to them. So, this is the paradigm shift that is going on. Instead of patients coming to the clinical trials, clinical trials are coming to the patients. And that shift, that's a paradigm shift and that is happening because of these AI techniques. Blockchain. Precision Medicine is another one. You don't run a big clinical trial anymore. You just go micro-trial, you just group small number of patients. You don't run a trial on breast cancer anymore, you just say, breast cancer for these patients, so it's micro-trials. And that needs -- >> Well that can still be aggregated. >> Exactly. It still needs to be aggregated, but you can get the RTD's quickly, so that you can decide whether you need to keep investing in that trial, or not. Instead of waiting 10 years, only to find out that your trial is going to fail. So you are wasting not only your time, but also preventing patients from getting the right medicine on time. So you have that responsibility as a pharmaceutical company, as well. So yes, it is a paradigm shift and this whole industry is rife for disruption and ERT is right at the center. We have not only data and technology experience, but as I said, we have deep domain experience within the clinical domain as well as regulatory and compliance experience. You need all these to navigate through this turbulent water of clinical research. >> Revolutionary changes taking place. >> It is and the satisfaction is, you are really helping the patients. You know? >> And helping the doctor. >> Helping the doctors. >> At the end of the day, the drug company does not supply the drug. >> Exactly. >> The doctor is prescribing, based on knowledge that she has about that patient and that drug and how they're going to work together. >> And out of the good statistics, in 2017, just last year, 60% of the MDA approved drugs were supported through our platform. 60 percent. So there were, I think, 60 drugs got approved? I think 30 or 35 of them used our platform to run their clinical trial, so think about the satisfaction that we have. >> A job well done. >> Exactly. >> Well, thank you for coming on the show Santi, it's been really great having you on. >> Thank you very much. >> Yes. >> Thank you. >> I'm Rebecca Knight. For Peter Burris, we will have more from MITCDOIQ, and the Cube's coverage of it. just after this. (techno music)

>> Live from the MIT campus in Cambridge, Massachusetts, it's the Cube covering the 12th annual MIT Chief Data Officer and Information Quality Symposium. Brought to you by SiliconANGLE Media. >> Welcome back to the Cube's coverage of MIT CDOIQ here in Cambridge, Massachusetts. I'm your host, Rebecca Knight along with my co-host, Peter Burris. We're welcoming back Dr. Santikary who is the Vice President and Chief Data Officer of ERT, thanks for coming back on the program. >> Thank you very much. >> So, in our first interview, we talked about the why and the what and now we're really going to focus on the how. How, what are the kinds of imperatives that ERT needs to build into its platform to accomplish the goals that we talked about earlier? >> Yeah, it's a great question. So, that's where our data and technology pieces come in. As we were talking about, you know, the frustration that the complexity of clinical trials. So, in our platform like we are just drowning in data, because the data is coming from everywhere. They are like real-time data, there is unstructured data, there is binary data such as image data, and they normally don't fit in one data store. They are like different types of data. So, what we have come up with is a unique way to really gather the data real-time in a data lake and we implemented that platform on Amazon Web Services Cloud and that has the ability to ingest as well as integrate data of any volume of any type coming to us at any velocity. So, it's a unique platform and it is already live. Press release came out early part of June and we are very excited about that and it is commercial right now, so yeah. >> But, you're more than just a platform. The product and services on top of that platform, one might say that the services in many respects are what you're really providing to the customers. The services that the platform provides, have I got that right? >> Yes, yes. So, platform like in a uBuild different kinds of services, we call it data products on top of that platform. So, one of the data products is business intelligence where you do real-time decisioning and the product is RBM, Risk Based Monitoring, where you come up with all the risks that a clinical trial may be facing and really expose those risks preemptively. >> So, give us an examples. >> Examples will be like patient visit, for example. A patient may be noncompliant with the protocol, so if that happens, then FDA is not going to like it. So, before they get there, our platform almost warns the sponsors that hey, there is something going on, can you take preemptive actions? Instead of just waiting for the 11th hour and only to find out that you have really missed out on some major things. It's just one example, another could be data quality issues, right? So, let's say there's a gap in data, and/or inconsistent data, or the data is not statistically significant, so you raise some of these with the sponsors so that they can start gathering data that makes sense. Because at the end of the day, data quality is vital for the approval of the drug. If that quality of the data that you are collecting is not good, then what good is the drug? >> So, that also suggests a data governance is gotta be a major feature of some of the services associated with the platform. >> Yes, data governance is key, because that's where you get to know who owns which data, how do you really maintain the quality of data overtime? So, we use both tools, technologies, and processes to really govern the data. And as I was telling you in our session one, that we are the custodian of this data, so we have fiduciary responsibility in some sense to really make sure that the data is ingested properly, gathered properly, integrated properly. And then, we make it available real-time for our real-time decision making, so that our customers can really make the right decisions based on the right information. So, data governance is key. >> One of the things that I believe about medical profession is that it's always been at the vanguard of ethics, social ethics, and increasingly, well, there's always been a correspondence within social ethics and business ethics. I mean ideally, they're very closely aligned. Are you finding that the medical ethics, social medical ethics of privacy and how you handle data, are starting to inform a broader understanding of the issues of privacy, ethical use of data, and how are you guys pushing that envelope if you think that has an important future? >> Yes, that is a great question like we use all these, but we have like data security in place in our platform, right? And the data security in our case plays at multiple level. We don't co-mingle one sponsor's data with others, so they're always like particularized. We partition the data in technical sense and then we have permissions and roles so they will see what they're supposed to be seeing. Not like interdepending on the roles, so yeah, data security is very critical to what we do. We also de-anonymize the data, we don't really store the PII like personally identifiable information as well like e-mail address, or first name or last name, you know? Or social security number for that matter. We don't, when you do analysis, we de-identify the data. >> Are you working with say, European pharmaceuticals as well, Bayer and others? >> Yeah, we have like as I said -- >> So, you have GDPR issues that you have satisfied? >> We have GDPR issues, we have like HIPAA issues, so you name it, so data privacy, data security, data protection, they're all a part of what we do and that's why technology's one piece that we do very well. Another pieces are the compliance, science, because you need all of those three in order to be really, you know, trustworthy to your ultimate customers and in our case they are pharmaceutical companies, medical device companies, and biotechnology companies. >> Where there are lives at stake. >> Exactly. >> So, I know you have worked, Santi, in a number of different industries, I'd love to get your thoughts on what differentiates ERT from your competitors and then, more broadly, what will separate the winners from the losers in this area? >> Yeah, obviously before joining ERT I was the Head of Engineering at Ebay. >> Who? (panel members laughing) >> So, that's the bidding platform, so obviously we were dealing with consumer data, right? So, we were applying artificial intelligence, machine learning, and predictive analytics, all kinds of things to drive the business. In this case, while we are still doing predictive analytics, but the idea of predictive analytics is very different, because in our case here at ERT, we can't recommend anything because they are all like, we can't say hey, don't take Aspirin, take Tylenol, we can't do that, it needs to be driven by doctors. Whereas at Ebay, we would just talking to the end consumers here and we would just predict. >> Again, different ethical considerations. >> Exactly, but in our domain primarily like ERT, ERT is the best of breed in terms of what we do, driving clinical trials and helping our customers and the things that we do best are those three ideas like data collection, obviously the data custodiancy that includes privacy, security, you name it. Another thing we do very well is real-time decisioning that allow our customers, in this case pharmaceutical companies, who will have this integrated dataset in one place, almost like cockpit, where they can see which data is where, what the risks are, how to mitigate those risks, because remember that this trials are happening globally. So, your sites, some sites are here, some sites are in India, who knows where? >> So, the mission control is so critical. >> Critical, time critical. And as well as, you know, cost effective as well, because if you can mitigate those risks before they become problems, you save not only cost, but you shorten the timeline of the study itself. So, your time to market, you know? You reduce that time to market, so that you can go to market faster. >> And you mentioned that it can be as long, the process can be a $3 billion dollar process, so reducing time to market could be a billion dollars a cost and a few billion dollars of revenue, because you get your product out before anybody else. >> Exactly, plus you're helping your end goals which is to help the ultimate patients, right? Because you can bring the drug five years earlier than what you have ended for, then you would save lots of lives there. >> So, the one question I had is we've talked a lot about these various elements, we haven't once mentioned master data management. >> Yes. >> So, give us a little sense of the role that master data management plays within ERT and how you see it changing, because you used to be a very metadata, technical-oriented thing and it's becoming much more something that is almost a reflection of the degree to which an institution has taken up the role that data plays within decision-making and operations. >> Exactly, a great question. At the master data management has people, process, and technology, all three that they co-mingle each other to drive master data management. It's not just about technology. So, in our case, our master data is for example, site, or customers, or vendors, or study, they're master data because they lead in each system. Now, depenation of those entities and semantics of those entities are different in each system. Now, in our platform, when you bring data together from this pair of systems, somehow we need to harmonize these master entities. That's why master data management comes into play. >> While complying with regulatory and ethical requirements. >> Exactly. So, customers for example aren't worried as once said. Or, pick any other name, can be spared 20 different ways in 20 different systems, but when you are bringing the data together, into a called platform, we want nobody to be spared only one way. So that's how you mental the data quality of those master entities. And then obviously we have the technology side of things, we have master data management tools, we have data governance that is allowing data qualities to be established over time. And then that is also allowing us to really help our ultimate customers, who are also seeing the high-quality data set. That's the end goal, whether they can trust the number. And that's the main purpose of our integrated platform that we have just launched on AWS. >> Trust, it's been such a recurring theme in our conversation. The immense trust that the pharmaceutical companies are putting in you, the trust that the patients are putting in the pharmaceutical companies to build and manufacture these drugs. How do you build trust, particularly in this environment? On the main stage they were talking this morning about, how just this very notion of data as an asset. It really requires buy-in, but also trust in that fact. >> Yeah, trust is a two-way street, because it has always been. So, our customers trust us- we trust them. And the way you build the trust is through showing, not through talking, right? So, as I said, in 2017 alone, 60% of the FDA approval went through our platform, so that says something. So customers are seeing the results, they're seeing their drugs are getting approved, we are helping them with compliance, we're artists with science, obviously with tools and technologies. So that's how you build trust, over time, and we have been around since 1977, that helps as well because it says that true and tried methods, we know the procedures, we know the water as they say, and obviously folks like us, we know the modern tools and technologies to expedite the clinical trials. To really gain efficiency within the process itself. >> I'll just add one thing to that, trust- and test you on this- trust is a social asset. At the end of the day it's a social asset. There are a lot of people in the technology industry continuously forget is that they think trust is about your hardware, or it's about something in your infrastructure, or even your applications. You can say you have a trusted asset, but if your customer says you don't, or a partner says you don't, or some group of your employees say you don't, you don't have a trusted asset. Trust is where the technological, the process, and the people really come together, that's the test of whether or not you've really got something the people want. >> Yes, and your results will show that, right. Because at the end of the day, your ultimate test is the results. Everything hinges on that. And the experience helps, as your experience with tools and technologies, signs, regulatories, because it's a multidimensional venn diagram almost, and we are very good at that, and we have been for the past 50 years. >> Well Santi, thank you so much for coming on the program again, it's really fun talking to you. >> Thank you very much, thank you. >> I'm Rebecca Knight for Peter Burris, we will have more from M.I.T CDOIQ in just a little bit.

>> Live from the MIT campus in Cambridge Massachusetts, it's theCube, covering the 12th annual MIT Chief Data Officer and Information Quality Symposium, brought to you by SiliconANGLE media. >> Welcome back to theCUBE's coverage of MIT CDOIQ here in Cambridge, Massachusetts. I'm your host Rebecca Knight along with my co-host Peter Burris. We're welcoming back Dr. Santikary, who is the Vice President and Chief Data Officer of ERT. Thanks for coming back on the program. >> Thank you very much. >> So in our first interview we talked about the why and the what and now we're really going to focus on how, the how. How, what are the kinds of imperatives that ERT needs to build into its platform to accomplish the goals that we talked about earlier. >> Yeah, it's a great question. So, that's where our data and technology pieces come in. We are as we were talking about in our first session that the complexity of clinical trials. So in our platform like we are just drowning in data because the data is coming from everywhere. There are like real-time data, there is unstructured data, there is binary data such as image data and they normally don't fit in one data store. They are like different types of data. So what we have come up with is a unique way to really gather the data real time, in a data lake, and we implemented that platform on Amazon web services ... Cloud and ... that has the ability to ingest as well as integrate data of any volume, of any type coming to us at any velocity. So it's a unique platform and it is already live, press release came out early part of June and we are very excited about that. And it is commercial right now. So, yeah. >> But you're more than just a platform, you're product and services on top of that platform, one might say that the services in many respects are what you're really providing to the customers, the services that the platform provides. Have I got that right? >> Yes, yes. So platform like you build different kinds of services we call it data products on top of that platform. So one of the data products is business intelligence. Why do you do real time decisioning? Another product is RBM, Risk-Based Monitoring, where you ... come up with all the risks that a clinical trial may be facing and really expose those risks preemptively. >> So give us some examples. >> Examples will be like patient visit for example. Patient may be non-compliant with the protocol. So if that happens then FDA is not going to like it. So before they get there our platform almost warns the sponsor that hey there is something going on can you take preemptive actions? Instead of just waiting for the 11th hour and only to find out that you have really missed out on some major things. It's just one example. Another could be data quality issues, right. So let's say there is a gap in data and/or inconsistent data or the data is not statistically significant. So you've to raise some of these with the sponsors so that they can start gathering data that makes sense because at the end of the day, data quality is vital for the approval of the drug. If the quality of the data that you are collecting is not good, then what good is the trial? >> So that also suggested that data governance is got to be a major feature of some of the services associated with the platform. Have I got that right? >> Yes, data governance is key because that's where you get to know who owns which data. How do you really maintain the quality of data over time? So we use both tools, technologies, and processes to really govern the data and as I was telling you in our session one, that we have the custodian of these data. So we have fiduciary responsibility in some sense to really make sure that the data is ingested properly, gathered properly, integrated properly and then we make it available real time for real time decision making so that our customers can really make the right decisions based on the right information. So data governance is key. >> One of the things that I believe about medical profession is that it's always been at the vanguard of ethics, social ethics and increasingly, well there has always been a correspondence between social ethics and business ethics. I mean, ideally they're very closely aligned. Are you finding that the medical ethics, social medical ethics of privacy and how you handle data are starting to inform a broader understanding of the issues of privacy, ethical use of data, and how are you guys pushing that envelope if you think that that is an important feature? >> Yeah, that's a great question. We use all these, but we have like data security in place in our platform, right? And the data security in our case plays at multiple level. We don't co-mingle one sponsor's data with other's. So they are always like particalized. We partition the data in technical sense and then we have permissions and roles. So they will see what they are supposed to be seeing. Not like, you know depending on the roles. So yeah, data security is very critical to what we do. We also de-anonymize the data. We don't really store the PII like Personally Identifiable Information as well like email address or first name or last name or social security number for that matter. When we do analysis, we de-identify the data. >> Are you working with European pharmaceuticals as well, Bayer and others? >> Yeah, we have like as I said. >> So you have GDPR issues (crosstalk). >> We have GDPR issues. We have like HIPPA issues. So you name it. Data privacy, data security, data protection. They are all a part of what we do and that's why technology is one piece that we do very well. Another pieces are the compliance, science. Because you need all of those three in order to be really trustworthy to your ultimate customers and in our case they are pharmaceutical companies, medical device companies, and biotechnology companies. >> Where there are lives at stake. >> Exactly. >> So I know you have worked Santi in a number of different industries. I'd like to get your thoughts on what differentiates ERT from your competitors and then more broadly, what will separate the winners from the losers in this area. >> Yeah, obviously before joining ERT, I was the head of data engineering at eBay. >> Who? (laughing) >> So that's the bidding platform so obviously we were dealing with consumer data right? So we were applying like artificial intelligence, machine learning and predictive analytics. All kinds of thing to drive the business. In this case, while we are still doing predictive analytics but the ideal predictive analytics is very different because in our case here at ERT we can't recommend anything because they are all like we can't say hey don't take Aspirin, take Tylenol. We can't do that. It's to be driven by doctors. Whereas at eBay, we were just talking to the end consumers here and we would just predict. >> Different ethical considerations. >> Exactly. But in our domain primarily like ERT, ERT is the best of breed in terms of what we do, driving clinical trials and helping our customers and the things that we do best are those three areas like data collection. Obviously the data custodiancy that includes privacy, security, you name it. Another thing we do very well is real time decisioning. So that allow our customers, in this case, pharmaceutical companies who will have this integrated dataset in one place. Almost like a cockpit where they can see which data is where, where the risks are, how to mitigate those risks. Because remember that these trials are happening globally. So some sites are here, some sites are in India. Who knows where? >> So the mission control is so critical. >> Critical, time critical. >> Hmm. >> And as well as you know cost-effective as well because if you can mitigate those risks before they become problems, you save not only cost but you shorten the timeline of the study itself. So your time to market, you know. You reduce that time to market so that you can go to market faster. >> And you mentioned that it can be, they could be, the process could be a 3 billion dollar process. So reducing time to market could be a billion dollars of cost and a few billion dollars of revenue because you get your product out before anybody else. >> Exactly. Plus you are helping your end goals which is to help the ultimate patients, right? >> And that too. >> Because if you can bring the drug five years earlier than what- >> Save lives. >> What you had intended for then you know, you'd save lots of lives there. Definitely. >> So the one question I have is we've talked a lot about these various elements. We haven't once mentioned master data management. >> Yes. >> So give us a little sense of the role that master data management plays within ERT and how you see it changing. Because it used to be a very metadata technical oriented thing and it's becoming much more something that is almost a reflection of the degree to which an institution has taken up the role that data plays within decision making and operation. >> Exactly, a great question. The master data management has like people, process, and technology. All three, they co-mingle each other to drive master data management. So it's not just about technology. So in our case, our master data is for example, site or customers, or vendors or study. They're master data because they live in each system. Now definition of those entities and semantics of those entities are different in each system. Now in our platform when you bring data together from disparate systems, somehow we need to harmonize these master entities. That's why master data management- >> While complying with regulatory and ethical requirements. >> Exactly. So customers for example Novartis let's say, or be it any other name, can be spelled 20 different ways in 20 different systems. But when we are bringing the data together into our core platform, we want Novartis to be spelled only one way. So that's how you maintain the data quality of those master entities. And then obviously we have the technology side of things. We have master data management tools. We have data governance that is allowing data qualities to be established over time and then that is also allowing us to really help our ultimate customers who are also seeing the high quality dataset. That's the end goal, whether they can trust the number. And that's the main purpose of our integrated platform that we have just launched on AWS. >> Trust is just, it's been such a recurring theme in our conversation. The immense trust that the pharmaceutical companies are putting in you, the trust that the patients are putting in the pharmaceutical companies to build and manufacture these drugs. How do you build trust, particularly in this environment? We've talked, on the main stage they were talking this morning about how just this very notion of data as an asset, it really requires buy-in, but also trust in that fact. >> Yeah, yeah. Trust is a two-way street, right? Because it has always been. So our customers trust us, we trust them. And the way you build the trust is through showing not through talking, right? So, as I said, in 2017 alone, 60% of the FDA approval went through our platform. So that says something. So customers are seeing the results. So they are seeing their drugs are getting approved. We are helping them with compliance, with audits, with science, obviously with tools and technologies. So that's how you build trust over time. And we have been around since 1977, that helps as well, because it's a ... true and tried method. We know the procedures. We know the water, as they say. And obviously, folks like us, we know the modern tools and technologies to expedite the clinical trials, to really gain efficiency within the process itself. >> I'll just add one thing to that and test you on this. Trust is a social asset. >> Yeah. >> At the end of the day it's a social asset and I think what a lot of people in the technology industry continuously forget, is that they think the trust is about your hardware, or it's about something in your infrastructure, or even in your applications. You can say you have a trusted asset but if your customer says you don't or a partner says you don't or some group of your employees say you don't, you don't have a trusted asset. >> Exactly. >> Trust is where the technological, the process, and the people really come together. >> And the people come together. >> That's the test of whether or not you've really got something that people want. >> Yes. And your results will show that, right? Because at the end of the day, your ultimate test is the results, right? And because that, everything hinges on that. And then the experience helps as you're experienced with tools and technologies, science, regularities. Because it's a multidimensional Venn diagram almost. And we are very good at that and we have been for the past 50 years. >> Great. Well Santi, thank you so much for coming on the program again. >> Okay, thank you very much. >> It was really fun talking to you. >> Thank you. >> I'm Rebecca Knight for Peter Burris. We will have more from MIT CDOIQ in just a little bit. (upbeat futuristic music)

(upbeat music) >> From theCube Studios in Palo Alto in Boston, bringing you data driven insights from theCube and ETR. This is Breaking Analysis with Dave Vellante. >> While by no means a safe haven, the cybersecurity sector has outpaced the broader tech market by a meaningful margin, that is up until very recently. Cybersecurity remains the number one technology priority for the C-suite, but as we've previously reported the CISO's budget has constraints just like other technology investments. Recent trends show that economic headwinds have elongated sales cycles, pushed deals into future quarters, and just like other tech initiatives, are pacing cybersecurity investments and breaking them into smaller chunks. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis we explain how cybersecurity trends are reverting to the mean and tracking more closely with other technology investments. We'll make a couple of valuation comparisons to show the magnitude of the challenge and which cyber firms are feeling the heat, which aren't. There are some exceptions. We'll then show the latest survey data from ETR to quantify the contraction in spending momentum and close with a glimpse of the landscape of emerging cybersecurity companies, the private companies that could be ripe for acquisition, consolidation, or disruptive to the broader market. First, let's take a look at the recent patterns for cyber stocks relative to the broader tech market as a benchmark, as an indicator. Here's a year to date comparison of the bug ETF, which comprises a basket of cyber security names, and we compare that with the tech heavy NASDAQ composite. Notice that on April 13th of this year the cyber ETF was actually in positive territory while the NAS was down nearly 14%. Now by August 16th, the green turned red for cyber stocks but they still meaningfully outpaced the broader tech market by more than 950 basis points as of December 2nd that Delta had contracted. As you can see, the cyber ETF is now down nearly 25%, year to date, while the NASDAQ is down 27% and change. Now take a look at just how far a few of the high profile cybersecurity names have fallen. Here are six security firms that we've been tracking closely since before the pandemic. We've been, you know, tracking dozens but let's just take a look at this data and the subset. We show for comparison the S&P 500 and the NASDAQ, again, just for reference, they're both up since right before the pandemic. They're up relative to right before the pandemic, and then during the pandemic the S&P shot up more than 40%, relative to its pre pandemic level, around February is what we're using for the pre pandemic level, and the NASDAQ peaked at around 65% higher than that February level. They're now down 85% and 71% of their previous. So they're at 85% and 71% respectively from their pandemic highs. You compare that to these six companies, Splunk, which was and still is working through a transition is well below its pre pandemic market value and 44, it's 44% of its pre pandemic high as of last Friday. Palo Alto Networks is the most interesting here, in that it had been facing challenges prior to the pandemic related to a pivot to the Cloud which we reported on at the time. But as we said at that time we believe the company would sort out its Cloud transition, and its go to market challenges, and sales compensation issues, which it did as you can see. And its valuation jumped from 24 billion prior to Covid to 56 billion, and it's holding 93% of its peak value. Its revenue run rate is now over 6 billion with a healthy growth rate of 24% expected for the next quarter. Similarly, Fortinet has done relatively well holding 71% of its peak Covid value, with a healthy 34% revenue guide for the coming quarter. Now, Okta has been the biggest disappointment, a darling of the pandemic Okta's communication snafu, with what was actually a pretty benign hack combined with difficulty absorbing its 7 billion off zero acquisition, knocked the company off track. Its valuation has dropped by 35 billion since its peak during the pandemic, and that's after a nice beat and bounce back quarter just announced by Okta. Now, in our view Okta remains a viable long-term leader in identity. However, its recent fiscal 24 revenue guide was exceedingly conservative at around 16% growth. So either the company is sandbagging, or has such poor visibility that it wants to be like super cautious or maybe it's actually seeing a dramatic slowdown in its business momentum. After all, this is a company that not long ago was putting up 50% plus revenue growth rates. So it's one that bears close watching. CrowdStrike is another big name that we've been talking about on Breaking Analysis for quite some time. It like Okta has led the industry in a key ETR performance indicator that measures customer spending momentum. Just last week, CrowdStrike announced revenue increased more than 50% but new ARR was soft and the company guided conservatively. Not surprisingly, the stock got absolutely crushed as CrowdStrike blamed tepid demand from smaller and midsize firms. Many analysts believe that competition from Microsoft was one factor along with cautious spending amongst those midsize and smaller customers. Notably, large customers remain active. So we'll see if this is a longer term trend or an anomaly. Zscaler is another company in the space that we've reported having great customer spending momentum from the ETR data. But even though the company beat expectations for its recent quarter, like other companies its Outlook was conservative. So other than Palo Alto, and to a lesser extent Fortinet, these companies and others that we're not showing here are feeling the economic pinch and it shows in the compression of value. CrowdStrike, for example, had a 70 billion valuation at one point during the pandemic Zscaler top 50 billion, Okta 45 billion. Now, having said that Palo Alto Networks, Fortinet, CrowdStrike, and Zscaler are all still trading well above their pre pandemic levels that we tracked back in February of 2020. All right, let's go now back to ETR'S January survey and take a look at how much things have changed since the beginning of the year. Remember, this is obviously pre Ukraine, and pre all the concerns about the economic headwinds but here's an X Y graph that shows a net score, or spending momentum on the y-axis, and market presence on the x-axis. The red dotted line at 40% on the vertical indicates a highly elevated net score. Anything above that we think is, you know, super elevated. Now, we filtered the data here to show only those companies with more than 50 responses in the ETR survey. Still really crowded. Note that there were around 20 companies above that red 40% mark, which is a very, you know, high number. It's a, it's a crowded market, but lots of companies with, you know, positive momentum. Now let's jump ahead to the most recent October survey and take a look at what, what's happening. Same graphic plotting, spending momentum, and market presence, and look at the number of companies above that red line and how it's been squashed. It's really compressing, it's still a crowded market, it's still, you know, plenty of green, but the number of companies above 40% that, that key mark has gone from around 20 firms down to about five or six. And it speaks to that compression and IT spending, and of course the elongated sales cycles pushing deals out, taking them in smaller chunks. I can't tell you how many conversations with customers I had, at last week at Reinvent underscoring this exact same trend. The buyers are getting pressure from their CFOs to slow things down, do more with less and, and, and prioritize projects to those that absolutely are critical to driving revenue or cutting costs. And that's rippling through all sectors, including cyber. Now, let's do a bit more playing around with the ETR data and take a look at those companies with more than a hundred citations in the survey this quarter. So N, greater than or equal to a hundred. Now remember the followers of Breaking Analysis know that each quarter we take a look at those, what we call four star security firms. That is, those are the, that are in, that hit the top 10 for both spending momentum, net score, and the N, the mentions in the survey, the presence, the pervasiveness in the survey, and that's what we show here. The left most chart is sorted by spending momentum or net score, and the right hand chart by shared N, or the number of mentions in the survey, that pervasiveness metric. that solid red line denotes the cutoff point at the top 10. And you'll note we've actually cut it off at 11 to account for Auth 0, which is now part of Okta, and is going through a go to market transition, you know, with the company, they're kind of restructuring sales so they can take advantage of that. So starting on the left with spending momentum, again, net score, Microsoft leads all vendors, typical Microsoft, very prominent, although it hadn't always done so, it, for a while, CrowdStrike and Okta were, were taking the top spot, now it's Microsoft. CrowdStrike, still always near the top, but note that CyberArk and Cloudflare have cracked the top five in Okta, which as I just said was consistently at the top, has dropped well off its previous highs. You'll notice that Palo Alto Network Palo Alto Networks with a 38% net score, just below that magic 40% number, is healthy, especially as you look over to the right hand chart. Take a look at Palo Alto with an N of 395. It is the largest of the independent pure play security firms, and has a very healthy net score, although one caution is that net score has dropped considerably since the beginning of the year, which is the case for most of the top 10 names. The only exception is Fortinet, they're the only ones that saw an increase since January in spending momentum as ETR measures it. Now this brings us to the four star security firms, that is those that hit the top 10 in both net score on the left hand side and market presence on the right hand side. So it's Microsoft, Palo Alto, CrowdStrike, Okta, still there even not accounting for a Auth 0, just Okta on its own. If you put in Auth 0, it's, it's even stronger. Adding then in Fortinet and Zscaler. So Microsoft, Palo Alto, CrowdStrike, Okta, Fortinet, and Zscaler. And as we've mentioned since January, only Fortinet has shown an increase in net score since, since that time, again, since the January survey. Now again, this talks to the compression in spending. Now one of the big themes we hear constantly in cybersecurity is the market is overcrowded. Everybody talks about that, me included. The implication there, is there's a lot of room for consolidation and that consolidation can come in the form of M&A, or it can come in the form of people consolidating onto a single platform, and retiring some other vendors, and getting rid of duplicate vendors. We're hearing that as a big theme as well. Now, as we saw in the previous, previous chart, this is a very crowded market and we've seen lots of consolidation in 2022, in the form of M&A. Literally hundreds of M&A deals, with some of the largest companies going private. SailPoint, KnowBe4, Barracuda, Mandiant, Fedora, these are multi billion dollar acquisitions, or at least billion dollars and up, and many of them multi-billion, for these companies, and hundreds more acquisitions in the cyberspace, now less you think the pond is overfished, here's a chart from ETR of emerging tech companies in the cyber security industry. This data comes from ETR's Emerging Technologies Survey, ETS, which is this diamond in a rough that I found a couple quarters ago, and it's ripe with companies that are candidates for M&A. Many would've liked, many of these companies would've liked to, gotten to the public markets during the pandemic, but they, you know, couldn't get there. They weren't ready. So the graph, you know, similar to the previous one, but different, it shows net sentiment on the vertical axis and that's a measurement of, of, of intent to adopt against a mind share on the X axis, which measures, measures the awareness of the vendor in the community. So this is specifically a survey that ETR goes out and, and, and fields only to track those emerging tech companies that are private companies. Now, some of the standouts in Mindshare, are OneTrust, BeyondTrust, Tanium and Endpoint, Net Scope, which we've talked about in previous Breaking Analysis. 1Password, which has been acquisitive on its own. In identity, the managed security service provider, Arctic Wolf Network, a company we've also covered, we've had their CEO on. We've talked about MSSPs as a real trend, particularly in small and medium sized business, we'll come back to that, Sneek, you know, kind of high flyer in both app security and containers, and you can just see the number of companies in the space this huge and it just keeps growing. Now, just to make it a bit easier on the eyes we filtered the data on these companies with with those, and isolated on those with more than a hundred responses only within the survey. And that's what we show here. Some of the names that we just mentioned are a bit easier to see, but these are the ones that really stand out in ERT, ETS, survey of private companies, OneTrust, BeyondTrust, Taniam, Netscope, which is in Cloud, 1Password, Arctic Wolf, Sneek, BitSight, SecurityScorecard, HackerOne, Code42, and Exabeam, and Sim. All of these hit the ETS survey with more than a hundred responses by, by the IT practitioners. Okay, so these firms, you know, maybe they do some M&A on their own. We've seen that with Sneek, as I said, with 1Password has been inquisitive, as have others. Now these companies with the larger footprint, these private companies, will likely be candidate for both buying companies and eventually going public when the markets settle down a bit. So again, no shortage of players to affect consolidation, both buyers and sellers. Okay, so let's finish with some key questions that we're watching. CrowdStrike in particular on its earnings calls cited softness from smaller buyers. Is that because these smaller buyers have stopped adopting? If so, are they more at risk, or are they tactically moving toward the easy button, aka, Microsoft's good enough approach. What does that mean for the market if smaller company cohorts continue to soften? How about MSSPs? Will companies continue to outsource, or pause on on that, as well as try to free up, to try to free up some budget? Adam Celiski at Reinvent last week said, "If you want to save money the Cloud's the best place to do it." Is the cloud the best place to save money in cyber? Well, it would seem that way from the standpoint of controlling budgets with lots of, lots of optionality. You could dial up and dial down services, you know, or does the Cloud add another layer of complexity that has to be understood and managed by Devs, for example? Now, consolidation should favor the likes of Palo Alto and CrowdStrike, cause they're platform players, and some of the larger players as well, like Cisco, how about IBM and of course Microsoft. Will that happen? And how will economic uncertainty impact the risk equation, a particular concern is increase of tax on vulnerable sectors of the population, like the elderly. How will companies and governments protect them from scams? And finally, how many cybersecurity companies can actually remain independent in the slingshot economy? In so many ways the market is still strong, it's just that expectations got ahead of themselves, and now as earnings forecast come, come, come down and come down to earth, it's going to basically come down to who can execute, generate cash, and keep enough runway to get through the knothole. And the one certainty is nobody really knows how tight that knothole really is. All right, let's call it a wrap. Next week we dive deeper into Palo Alto Networks, and take a look at how and why that company has held up so well and what to expect at Ignite, Palo Alto's big user conference coming up later this month in Las Vegas. We'll be there with theCube. Okay, many thanks to Alex Myerson on production and manages the podcast, Ken Schiffman as well, as our newest edition to our Boston studio. Great to have you Ken. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Silicon Angle. He does some great editing for us. Thank you to all. Remember these episodes are all available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibond.com and siliconangle.com, or you can email me directly David.vellante@siliconangle.com or DM me @DVellante, or comment on our LinkedIn posts. Please do checkout etr.ai, they got the best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis. (upbeat music)

(thoughtful electronic music) >> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> Enterprise applications are an enormous market, and they're enormously important to organizations globally. Essentially, the world's businesses are running on enterprise applications. Companies' processes are wired into these systems, and the investments that they make in people, process, and technology are vital to these companies' success. But it's complicated because many of these systems are decades old. Markets have changed, but the ERP system for example fundamentally hasn't. Hello everyone, and welcome to this week's Wikibon CUBE Insights, powered by ETR. This week, we're going to do a data download on the enterprise software space, and put forth some themes in our thesis around this very important segment. I'd like to do a shout-out to my friend Sarbjeet Johal, who helped me frame this segment, and he's a strategic thinker and he shared some excellent insights for this episode. What I'd first like to do is let's lay out the scope of what we're going to talk about today. So we're going to focus on the core enterprise apps that companies rely on to run their businesses. Talkin' about the systems of record here, the ERP, the financial systems, HR, CRMs, service management we'll put in there. We may touch on some of the other areas, but this is core that we're going to drill into. This is a big, big market. Customers spend many hundreds of billions of dollars in this area, you could argue about a half a trillion. And it's a mature market, as you'll see from the data. Look, it's good to be in the technology business today. This business is doing better than most, and within the technology business, it's better to be in software because of the economics and scale. And if you have a SaaS cloud model, it's even better. But the market, it is fragmented, not nearly as much as it used to be, but there are many specialized areas where leaders have emerged. ServiceNow and ITSM or Workday and HCM are good examples of companies that've specialized and then exploded, first as we saw ServiceNow blow past Workday's valuation. It was nearly 2x at one point. Now, that was before Workday crushed its earnings this week. It's up 15% today. ServiceNow took a slight breather earlier this month, but it's up on Workday sympathy today. Salesforce also beat earnings, and of course replaced Exxon Mobile on the DOW Industrials, can you imagine that? But let's bring it back to this digital transformation that you hear about. This is the big cliche from all the tech companies and especially software players. Now a lot of this DX, I sometimes call it, is related to old systems. It's especially true for the mega-caps like Oracle, SAP, PeopleSoft, JD Edwards, and even Microsoft. Take ERP and some of the mature products for example, like Oracle R12, or SAP R3 or R4. Many of these systems were put in place 15 years ago, and yeah, they're going to need to transform. They are burnt in. They were installed in what, 2005? It was before the iPhone, before social media, before machine learning and AI made its big comeback, and before cloud. These systems were built on the 1.0 of cloud. The businesses have changed but the software really hasn't. It happens every 10 to 15 years, companies have to upgrade or re-implement their systems, and optimize for the way business now runs, because they had to be more competitive and more agile. They can't do it on their old software. And God help you if you made a bunch of custom modifications. Good lucking tryin' to rip those out. And this is why pure play companies in the cloud like ServiceNow and Workday have done so well. They're best-of-breed and they're cloud, and it sets up this age-old battle that we always talk about, best-of-breed versus integrated suites. So let's bring in some of the other themes and feedback that we get from the community. Now we've definitely seen this schism play out between on-prem and cloud plays. And that's created some challenges for the legacy players. People working remotely has meant less data center, less on-prem action for the legacy companies. Now, they have gone out and acquired to get to the cloud and/or they've had to rearchitect their software like Oracle has done with Fusion. But think about something like Oracle Financials. Oracle is tryna migrate them to Fusion, or think about SAP R3, with R4, SAP pushing HANA. All this is going to cloud-based SaaS. So the companies that've been pure play SaaS are doing better, and I say quasi-modern on this slide because Salesforce, ServiceNow, Workday, even Coupa, NetSuite which is now Oracle, SuccessFactors which SAP purchased, et cetera, these are actually pretty old companies, the earlier part of the 2000s or in the case of Salesforce, 1999. And you're seeing some really different pricing models in the market. Things are moving quickly to an OPEX model. You have the legacy perpetual pricing, and it's giving way to subscriptions, and now we even see companies like Datadog and Snowflake with so-called consumption-based pricing models, priced as a true cloud. And we think that that's going to eventually spill into the core SaaS applications. Now one of the concerns that we've heard from the community is that some of the traditional players that were able to hide from COVID earlier this year might not have enough deferred revenue dry powder to continue to power through the pandemic, but so far the picture continues to look pretty strong for the software companies. We'll get into some of that. Now, finally, this is a premise that I talked to Sarbjeet about, the disruption perhaps comes from cloud and developer ecosystems. Y'know I remember John Furrier and I had a conversation awhile back with Jerry Chen from Greylock. It was on theCUBE, and it was kind of like, went like this. People were talking about whether AWS was going to enter the applications market, and the thesis here is no, or not in the near future. Rather, the disruptive play, and this is really Sarbjeet's premise, is to provide infrastructure for innovation, and a PaaS layer for differentiation, and developers will build modern cloud-native apps to compete with the SaaS players on top of this. This is intriguing to me, and is likely going to play out over the next decade, but it's going to take a while, because these SaaS players are, they're very large, and they continue to pour money into their platforms. Now let's talk about the shift from CAPEX to OPEX and bring in some ETR data. Of course, this was well in play pre-COVID, but the trend has been accelerating. This chart shows data from the August ETR survey, and it was asking people to express their split between CAPEX and OPEX spend, and as you can see, the trend is clear. Goes from 48% last year, 55% today, and moving to over 62% OPEX a year from now. It's no surprise, but I think it could happen even faster depending on the technical debt that organizations have to shed. And hence, the attractiveness again of the SaaS cloud players. So now let's visualize some of the major players in this space, and do some comparisons. Here we show one of our favorite views, and what we're doing here is we juxtapose net score on the vertical axis with market share on the horizontal plane. Remember, net score is a measure of spending momentum. Each quarter, ETR asks buyers, are you planning to spend more or less, and they essentially subtract the lesses from the mores to derive net score. Market share on the other hand is a measure of pervasiveness in the dataset, and it's derived from the number of mentions in the sector divided by the total mentions in the survey, and you can see each metric in that embedded table that we put in there. So I said earlier, this was a pretty mature market and you can see that in the table. Eh, kind of middle-of-the-road net scores with pretty large shared ends, i.e. responses in the dataset, but a lot of red. There are some standouts, however, as you see in the upper right, namely, ServiceNow and Salesforce. These are two pretty remarkable companies. ServiceNow entered the market as a help desk or service management player, and has dramatically expanded its TAM, really to the point where they're aiming at $5 billion in revenue. Salesforce was the first in cloud CRM, and is pushing 20 billion in revenue. I've said many times, these companies are on a collision course, and I stand by that, as any of the next great software companies, and these are two, are going to compete with all the mega-caps, including Oracle, SAP, and Microsoft, and they'll bump into each other. Which brings us to those super-cap companies. You see Microsoft with Dynamics, they show up like they always do. I'm like a broken record on Microsoft. I mean they're everywhere in the survey data. Now Oracle and SAP, they've been extremely acquisitive over the years, and you can see some of their acquisitions on this chart. I've said many times in theCUBE that Larry Olsen used to denigrate his competitors for writing checks instead of code, but he saw the consolidation trend happening in the ERT, ERP space before anyone else did, and with the $10 billion PeopleSoft acquisition in 2005, set off a trend in enterprise software that did a few things. First, it solidified Oracle's position further up the stack. It also set Dave Duffield and Aneel Bhusri off to create a next-generation cloud software company, Workday, which you can see in the chart has a net score up there with ServiceNow, Salesforce, and Coupa, and it also led to Oracle Fusion Middleware, which is designed as an integration point for all these software components, and this is really important because Oracle is moving everything into its cloud. And you can see that its on-prem net score, which puts it deep into negative territory. Now SAP, take a look at them, they have much higher net scores than Oracle, and you can see it's acquired SaaS properties like Ariba, Concur, and SuccessFactors, which have decent momentum. But you know, SAP, and we've talked about this before, is not without its challenges. With SAP, HANA is the answer to all of its problems. The problem is that it's not necessarily the answer to all of SAP's customers' problems. Most of SAP's legacy customers run SAP on Oracle or other databases. HANA is used for the in-memory query workload, but most customers are going to continue to use other databases for their systems of record. So this adds complexity. But HANA is very good at the query piece. However, SAP never did what Oracle did with Fusion, which as you might recall, took more than a decade to get right. HANA is SAP's architectural attempt to unify the SAP portfolio and get, (laughs) really get off of Oracle, but it's many years away, and it's unclear when or if they'll ever get there. All right, let's move on. Here's a look at a similar set of companies, but I wanted to show you this view because it gives you a detailed look at ETR's net score approach, and it tells us a few things more. And remember, this is a survey of almost 1,200 technology buyers. That's the N, that's the respondent rate. So this chart shows the net score granularity for the enterprise players that we were just discussing. Let me explain this. Net score is actually more detailed than what I said before. It comprises responses in four categories. The lime green is new adoptions. The forest green is growth in spending of 6% or more, the gray is flat spend, the pink is a budget shrink of 6% or greater, and the red is retiring the platform. So what this tells us is that there's a big fat middle of stay the same. The lime green is pretty small, but you can see, NetSuite jumps out for new adoptions because they've been very aggressive going after smaller and mid-sized companies, and Coupa, the spend management specialist, shows reasonably strong new adoptions. Now ServiceNow is interesting to me. Not a ton of new adoptions. They've landed the ship and really penetrated larger organizations. And while new adoptions are not off the charts, look at the spending more categories, it's very very strong at 46%. And the other really positive thing for ServiceNow is there's very little red. This company is a beast. Now Salesforce similarly, not tons of new adoptions, but 40% spend more. For a company that size, that's pretty impressive. Workday similarly has a very strong spending profile. At the bottom of the chart, you see a fair amount of red, as we saw on the XY graph. But now, let's take another view of net score. Think of this as a zoom in, which takes those bar charts but shows it in a pie format for individual companies. So we're showing this here for ServiceNow, Workday, and Salesforce, and we've superimposed the net score for these three in green, so you can see ServiceNow at 48%, very good for a company headed toward five billion. Same with Workday, 40% for a company of similar size, and Salesforce has a comparable net score, and is significantly larger than those two revenue-wise. Now this is the same view, this next chart's the same view for SAP and Oracle, and you can see substantially lower than the momentum leaders in terms of net score. But these are much larger companies. SAP's about 33 billion, Oracle's closer to 40 billion. But Oracle especially has seen some headwinds from organizations spending less which drags its net score down. But you're not seeing a lot of replacement in Oracle's base because as I said at the top, these systems are fossilized and many are running on Oracle. And the vast majority of mission-critical workloads are especially running on Oracle. Now remember, this isn't a revenue-weighted view. Oracle charges a steep premium based on the number of cores, and it has a big maintenance stream. So while its net score is kind of sucky, its cashflow is not. All right, let's wrap it up here. We have a very large and mature market. But the semi-modern SaaS players like Salesforce and ServiceNow and Workday, they've gone well beyond escape velocity and solidified their positions as great software companies. Others are trying to follow that suit and compete with the biggest of the bigs, i.e. SAP and Oracle. Now I didn't talk much about Microsoft, but as always they show up prominently. They're huge and they're everywhere in this dataset. What I think is interesting is the competitive dynamics that we talked about earlier. These kind of newer SaaS leaders, they're disrupting Oracle and SAP, but they're also increasingly bumping into each other. You know, ServiceNow has HR for example, and they say that they don't compete with Workday, and that's true. But y'know, these two companies, they eye each other and they angle for account control. Same thing with Salesforce. It's that software mindset. The bigger a software company gets, the more they think they can own the world, because it's software, and if you're good at writing code and you see an opportunity that can add value for your customers, you tend to go after it. Now, we didn't talk much about M&A, but that's going to continue here, especially as these companies look for TAM expansion and opportunities to bring in new capabilities, particularly around data, analytics, machine learning, AI and the like, and don't forget industry specialization. You've seen Oracle pick up a number of industry plays and as digital transformation continues, you'll see more crossing of the industry streams because it's data. Now, the disruption isn't blatantly obvious in this market right now, other than SaaS clouds going after SAP and Oracle, and it's because these companies are deeply entrenched in their customer organizations and change is risky. But the cloud developer, the open source API trend, it could lead to disruptions, but I wouldn't expect that until the second half of this decade as cloud ecosystems really begin to evolve and take hold. Okay, well that's it for today. Remember, these Breaking Analysis episodes, they're all available as podcasts wherever you listen so please subscribe. I publish weekly on Wikibon.com and SiliconANGLE.com, so check that out, and please do comment on my LinkedIn posts. Don't forget, check out ETR.plus for all the survey action. Get in touch on Twitter, I'm @dvellante, or email me at David.Vellante@siliconangle.com. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching everybody. Be well, and we'll see you next time. (thoughtful electronic music)

>> Live from the MIT campus in Cambridge, Massachusetts, it's theCUBE covering the 12th annual MIT Chief Data Officer and Information Quality Symposium, brought to you by SiliconANGLE media. >> We are wrapping up a day of coverage here at theCUBE for MIT CDOIQ here in Cambridge, Massachusetts. I'm Rebecca Knight, along with Peter Burris. We've been here all day, folks. We've learned a lot, we've had a lot of great conversations here, a lot of lively debate and interest. So, Peter, this morning, you were talking about this fundamental idea that data needs to be viewed as an asset within an organization. Obviously we're here with a bunch of people who are drinking that Kool-Aid, but-- >> Living that Kool-Aid. >> Living that Kool-Aid, embodying that Kool-Aid. So based on what we heard today, do you think that business has caught up? >> Well, I would say two things. First of all, this has been, as you said, it's been absolutely marvelous series of conversations in many respects. This is what theCUBE is built for, right? Smart people in conversation on camera. And we've had some smart people here today. What I got out of it on that particular issue is that there is general agreement among CDOs that they have to start introducing this notion of asset and what that means in their business. There's not general agreement, or there's a general, I guess not agreement, but there's general concern that we still aren't there yet. I think that everybody that we talk to I think, would come back and say, yes we grew those practices, but the conventions are not as established and mature as they need to be for everybody in our business to agree so that we can acculturate. Now we did hear some examples of folks that have done it. So that great BBDA case we talked about was an example. There was a company that is actually becoming, is really truly institutionalizing, acculturating that notion of data as an asset that performs work, but I think we've got general agreement that that's the right way of thinking about it, but also a recognition that more work needs to be done, and that's why conferences like this are so important. >> Well, one of the things that really struck me about what BBDA did was this education campaign of its 130,000 employees, and as you said, really starting from the ground and saying, this is how we're going to do things. This is who we are as an organization. >> Yeah, and it was a great conversation because one of the points I made was, specifically, that BBDA is a bank. It is an information-based business that has very deep practices and principles associated with information, and when they decided that they need to move beyond that, they were able to get the entire bank to adopt a set of practices that are leading to new types of engagement models, product orientations, service capabilities. That's a pretty phenomenal feat. So, it's happening and it can get done, and there are examples of it happening. Another thing we talked about was the fact that over the course of the next few years, one of the big, one of the most exciting things about digital business is not just digital business and digital, what people call digital maintenance, but that transformation practices. That way forward. And we talked about the idea of how you wrapper existing goods and services and offerings with data to turn them into something else, and the incumbents are going to find ways of doing that so they can re-establish themselves as leaders in a lot of different markets. >> And that's what will separate the people who really get this from the people who, or from the organizations that are going to lag. >> Yeah, we're starting to hear that a lot more from clients, is that the idea increasingly is, okay, I've already got customers. I've already got offers. How do I wrapper them? Using a term we heard from a professor at MIT. How do I wrapper them to improve them utilizing data? And that's a big challenge, but it's happening. >> One of the other fun interviews we had was all about clinical trials, and the use of data in these clinical trials. There are so many challenges about, with clinical trials because of the time it takes to conduct one of these, the cost that it takes, and then at the end you are dealing with patients who just say, "oh, I think I'm not going to take that drug today." Or other factors that take place here. I mean, what do you see, I know your dad is a physician, what do you see as the most exciting thing about the use of data in clinical trials, but also just in the healthcare industry in general? >> Well, so what we heard, and it was a great combination of interviews, but what we heard is that to bring a new drug to market can cost $4 billion and take 15 years. And the question is, can data, first off, reduce the cost of bringing a new drug to market? And we heard numbers like, yeah, by $1 billion or even more. So imagine having the cost of bringing a new drug to market, but also reducing the time by as much as two thirds. That's very, very powerful stuff when we come down to it. And as you said, the way you do that is you have to protect your data to make sure you're complying with various regulations, but as you said, for example, sustaining someone in the trial even though they're starting to feel better because the drug's working. Well, people opt out. They abandon the trial. Well can you use data to keep them tied in, to provide new types of benefits and new types of capabilities so they want to sustain their participation in the trial. >> Or at least the pharma company, hey, this person's dropping out, you need to explain that to the FDA, and that's going to become a point, yeah. >> Or you need to provide an incentive to keep them in. >> Right. >> Or another example that was used was, if we can compress the amount of time, but then recognize that we can sustain an engagement with a patient and collect data longer, that even though we can satisfy the specific regulatory mandates of a trial, shorter, we can still be collecting data because we have a digital engagement model as part of this whole process subject to keeping privacy in place and ownership notions in place, and everything else, complying with regulatory notions. So that is I think a very powerful example. And again, Santi, Dr. Santi was talking specifically about how ERT is helping to accelerate this whole process because over the course of the next dozen years, we're going to learn more about people, the genome is going to become better understood. Genomics is going to continue to evolve. Data is going to become increasingly central to how we think about defining disease and disease processes, and one of the key responses is to learn from that and apply data so that we can more rapidly build the new procedures, devices, and drugs that are capable of responding. >> When we're thinking about what keeps the chief data officers up at night, we know that data security, data fidelity, privacy, the other thing we really heard about from Melana Goldban from PwC Accelerator is the idea about bias, and that is a real concern. From the way she is talking about, it sounded as though companies are more aware of this. It really is an organizational challenge that they recognize that not just matters for social reasons but really for business reasons too, frankly. It affects your bottom line. Where do you come out on that? Do you think we're moving in the right direction? >> First of all, it was a great interview, and a lot of what Melana said was illuminating to me, and I agree with virtually everything she said. We're doing a piece of research on that right now. I would say that, in fact, most companies are not fully factoring the role that bias plays in a lot of different ways. That's one of the things that absolutely must happen as part of the acculturation process, what's known as evidence-based management starts to take grip more within businesses is to understand not only what bias introduced into data now, but as you create derivatives on that data, how that bias changes, delays that data. And that is a relatively poorly understood problem. >> But it's a big problem. >> Oh, it's going to be even bigger because we're going to utilize AI and it's actually going to limit the range of options that people consider as they make a decision, or make the decisions directly for the individual, act on behalf of the brand, what we call agency, a system of agency. And not understanding that range, not having it be auditable, not understanding what the inherent bias is can very quickly send a business off the rails in unexpected ways. So we're devoting a lot of time and energy into understanding that right now. But here's the challenge, that we've got business decision makers who are very familiar with certain kinds of information. There's nobody gets to be the CEO or the COO or a senior person in business if they don't have a pretty decent understanding of findings. So financial information is absolutely adopted within the board room and the senior ranks of management in virtually all businesses of any consequential size today. What we're asking them to do is to learn about wholly new classes of data. New data conventions, what it means, how to apply it, how you should factor it, how to converge agreement around things, that allows them to be as mature in their use of customer data or production data or partner data or any other number of metrics as they are with financial data. That's a real tall order. It's one of the significant challenges that a lot of businesses face today. So it's not that they don't get data or they don't understand data. It's that the sources of data and therefore the range of options that are going to be shaped by data are becoming that much more significant in business. >> And it's how they need to think about data too. I mean I was really struck by Tom Sasala at the very beginning saying, one of the reasons the intelligence community didn't predict 9/11 is that we didn't have people who were thinking like Hollywood people, thinking audaciously enough about what could happen and that similarly we need to have business leaders and executives, who may be very good at crunching numbers, really think much more broadly about the kinds of-- >> And Tom is absolutely right. We also, cuz I was very close to the DoD at the time, there was serious confirmation bias that was going on at that time too. >> Exactly. But clearly he's right, that the objective is for executives to, as a group, acknowledge the powerful role that data can play, have a data-first mentality as opposed to a bias or experience-first mentality. Because my experience is very private relative to your experience. And it takes a lot of time for us to negotiate that before we can make a very, very consequential move. That's not going to go away. We're human beings. But we increasingly need to look at data, which can provide a common foundation for us to build our biases upon so that we can be more specific and more transparent about articulating my interpretations. You can't start doing that until you are better, more willing to utilize data as a potentially unifying tool and mechanism for thinking about, thinking about how we move forward with something. >> That's great. And it's a great way to end our day of coverage here at M.I.T CDOIQ. Thank you so much. It's been a pleasure, >> As always, Rebecca. >> hosting with you. And thanks to the crew and everyone here. It's been really a lot of fun. I'm Rebecca Knight for Peter Burris. We will see you next time on theCUBE. (techno music)