Digital Transformation in the Pharmaceutical Industry: Innovation at Drug Companies

Michael Krigsman: Welcome to Episode #255 of CxOTalk. And, we are speaking about the pharmaceutical industry. We’re talking about healthcare and patients, and the relationship between drug companies and patients. And, the business model of drug companies, and the societal, cultural, political pressure. This is going to be a fascinating show. I’m Michael Krigsman. I’m an industry analyst and the host of CxOTalk. I want to just say a brief “thank you” to Livestream for supporting us with video infrastructure. And, if you go to Livestream.com/CxOTalk, they will give you a discount on their plans! So, do that.

We have two extraordinary guests on our show today. And, I want to introduce first Richie Etwaru, who is the Chief Digital Officer for QuintilesIMS, which is a huge company. Richie Etwaru, how are you?

Richie Etwaru: I’m doing fantastic, man! Thanks for having me on the show and, you know, every time I’m here, I thank you for what you do. You drive a great conversation, and we all learn from listening to each other. So, thanks for that!

Michael Krigsman: Well, thank you! This is your third time on the show. Richie, briefly tell us about QuintilesIMS and tell us what you do there.

Richie Etwaru: Very quickly, QuintileIMS, we are the largest partner for the life sciences industry, and we are what I like to call the “patron saint” of taking the silos out of the industry. So, we’ve got services all the way from molecule to market, and we work with our customers across that journey to make sure that we can connect the organization horizontally as pharma transforms, which is what we are going to talk about today.

Michael Krigsman: I love that! The patron saint of taking silos out of the pharmaceutical industry. So, Richie, thank you for being here. And, we are also joined by Milind Kamkolkar, who is the Chief Data Officer at Sanofi. And Milind, this is your first time. Welcome to CxOTalk!

Milind Kamkolkar: Yes, it is! I can’t wait to go through my initiation rites on the show. Looking forward to it!

Michael Krigsman: So, Milind, tell us about Sanofi and what does a Chief Data Officer at a pharmaceutical company do?

Milind Kamkolkar: Okay! So, let’s start with Sanofi because that’s the easier question. Sanofi is a large pharmaceutical company; biotech company; headquartered in Paris, France which is where I’m actually dialing in from, today. They focus on a couple of key areas, mainly vaccines, general medicine, specifically diabetes and cardiovascular, and also, with the acquisition of Genzyme, we’ve entered the world of specialty in rare disease and oncology which I find incredibly powerful.

If you sort of take these three combinations of areas past our device and manufacturing divisions for patients with diabetes, you've got a really nice roundup. I should also say, I forgot the consumer health division as well. So, you've got a nice round-up from, let's call it chronic care to specialty rare disease, to then supporting patients through devices down to the consumer level when we're in the consumer health world.

Michael Krigsman: I was going to say, you’re a huge, huge company. Your revenue last year was what, like $33 billion?

Milind Kamkolkar: Yeah! Something like that. I mean, to be perfectly honest, I don't track it all the time. You know, I think being a Chief Data Officer, it's my fourth month over here now and I guess it's sort of helped to turn… Part of the reason why I don't get to spend perhaps as much time as I'd like to on the financials… You know, the reason, I think, why Sanofi decided to hire a Chief Data Officer was for one very clear reason. We have an endemic problem within the industry and that is really because of the silos that have often been created over a period of time. We really produce three assets: One is product — compounds; Two is a share price, particularly if you're a publicly traded organization; and Three, is information. Now, if you ask yourself what are you really good at, I don't think any shareholder's going to say “You're doing the best you can” because who wants … you want to keep shares going up.

Likewise, when it comes to product, you can always produce a better pipeline. But, when it comes to information, that's the one critical piece that whilst we produce a lot of it, you can argue that the distance between data to decision-making is still far than less desirable. So, my job at Sanofi as a Chief Data Officer is really to help accelerate creating better decisions faster that are more relevant in context across the spectrum, from R&D all the way through to commercial, and across our business units.

Michael Krigsman: So, Richie Etwaru, when we talk about silos inside the pharmaceutical industry, what are we actually speaking about and how does this relate to the broader changes that are going on regarding healthcare and ultimately, the impact on healthcare for people? For patients?

Richie Etwaru: Well, I think most of us understand silos pretty well, right? These are vertical departments within large companies that tend to perform a specific function. And then they perform that function well, but if you look at multiple of those silos together as a broad organization, you’ll see inconsistencies and gaps to be solved for. I think, with the pharmaceutical industry, there was a time when it was okay to have silos. Not that you wanted them, but if you had them, it was not the biggest deal in the world, and we’re seeing what I like to call the “three waves” of change enter the environment of the pharmaceutical industry that’s creating the financial reality and quite frankly, the competitive reality, to start to think about what the business model looks like and take the silos out. These three waves, I think, most people would recognize.

Now, the first wave is what came from the supply side. So, I think of this as the patent cliff, right? I think we’ve heard enough about the patent cliff that the supply of discovery of drugs in the pipeline has changed quite a bit. And, the good thing about the patent cliff is that it was sort of contained within the pharmaceutical industry, right? Yes, there was some implication too of the stakeholders, but it didn’t radically change the landscape because each pharma company was suffering from the same strain from a supply side.

The second wave is what I like to call the wave coming from the demand side. This is the influence and the pressures of reimbursement being changed, payment terms being changed. I think what we see in the United States with Obamacare and the model of delivery has created a tremendous amount of strain that created a whole new wave of pressures for the pharmaceutical industry. So, I think that’s the second wave. Now, this was not as self-contained in the industry. This included the patients; it included the payers; it also included the government to kind of look at that.

Where we are today is what I like to call the “third wave” of change that’s coming through the pharma industry. And, this is the digital health and technology paradigms that are entering at the same time. Now, the patent cliff is not completely solved for. That's still moving over, right? The changes in the payment are not completely solved for. That's still here. And, we have this new wave that's being driven by this new stuff that Milind is talking about, which is more data, digital health, some of the new technology paradigms. What's interesting about this third wave is that it is not self-contained in the pharma industry or in healthcare. Now, you're seeing new entrants start to enter the competitive landscape. Apple is a good example, Amazon is a recent good example, and this is creating the type of strain where we, as vendors in the industry, have to start to look at what type of solutions we provide to our customers because it's not just that the competitive landscape is changing, and the pressures are changing, we are seeing new entrants to the marketplace, which is going to drive disruption.

Milind Kamkolkar: I think Richie brings up a really good point, there. You know, I often get asked the question: “But Milind, you’ve only been here a few months, but what’s your observation in the industry? You know, how do you see us benchmark?” I always have a quiet grin whenever that question comes up because I reply with the following: “If you were to compare a brontosaurus and tyrannosaurus rex, you could argue that they’re both dinosaurs. But when the comet hit, does it really matter, you know?” And, I think what the industry is facing right now is, in fact, that horizontalization. When we see consulting practices, when we see folks coming in and often talking to us about their discipline in the pharmaceutical industry, I almost have to argue “Well, how relevant is that when you’ve already got most businesses already becoming technology businesses, and most technology businesses {are] already eating away at our business?”

And, what you’ve seen here now is this, “I’ll take ‘cautious reluctance,’ if in many ways, to understand what the implications of that are.” And I think the jury is still out. But one thing is clear; this is a comet and there’s no getting away from it. And, the sooner we are to embrace that, I think the sooner we are, in terms of really maximizing our business value to our customers — namely the patients, physicians, payers, and regulators that we work with. And of course, consumers.

Michael Krigsman: So, what… Is there an answer where, where does this need to go and how does the pharma industry; what does the industry need to do to get there?

Richie Etwaru: I think the issue here has really changed in the last five years. When I first entered the industry, for example, the name of the game was “How do I thrive ahead of my competition?” Right? If I think about some of the problems that we’re solving for customers today, they’re starting to look like, “How do I survive? Right?” And I love that metaphor, Milind, on the comet and the dinosaurs that you’re talking about. The crux of the matter today is not so much how I engage with patients, or how I discover drugs, or how I figure out my pricing, or my analytics. Those are all individual problems. But at the core of the problem is the notion that the business model of the pharmaceutical manufacturer is starting to expire. If the three of us were given $3 billion today, from (I don't know) KKR, or TPG [Capital] to go start a pharma company to manufacture treatments for those that are disenfranchised by health, I'm not sure we would pick the model we have today that takes 8-10 years to discover a molecule and bring it to market. And takes $2.8-3.4 billion to do that. I'm not sure we would take that model.

So, there’s a lot of pressure that’s starting to wake up [to] the realization that the business model is no longer one that is profitable and we’ve got to go at the business model. Milind, I’d love to hear your thoughts on that because that’s sort of where I’m starting to anchor most of our investment.

Milind Kamkolkar: Look, I could agree more, Richie. And it’s one of those areas where, at the crux of it, yes, the business model is changing. Right? Once upon a time, it was a rep-led — really a rep-led, if you will — commercial model. I think it’s clear to say that model, particularly for chronic care and general medicine, is, we’ve seen it already, eroding away. It doesn’t mean that sales reps aren’t important. I’m still a big believer that at the end of the day, when it comes to sales, relationships matter. Right? But, the nature of those relationships, the nature of those engagements, the channels under which you take under; the also primary market research, if you will, (Right?) to understand what do consumers really want. The timelines under which you operate; those have dramatically shifted. Right? Where once upon a time, you would do a year-long brand plan. You could argue, shouldn’t they be months long now, right? Based on what we’re seeing out there.

Likewise, in R&D, starting up a biotech company, you could argue with some of the particularly garage buyer techs that are coming in that you probably don't even need $3 billion. You probably just need a cheap sequencing machine, which is getting cheaper, as we know. You have some knowledge of biology. But, I think what's emerging even more so is the nature of technology in that pursuit of achieving a product that can hit the market.

Now, in saying all of that, I don't want to be disrespectful to the regulators in this instance because clearly, there needs to be a new ecosystem that's evolving. And we're all learning at the same time. So, I think the opportunity for us as an industry body, if we were to conclude that this is a healthcare and life sciences industry, is that we need to work with the regulators because this technology's moving so fast. But yet, the regulators themselves are still learning. So, what is true and appropriate may not necessarily always be relevant. For example, I was having a discussion today. Cybersecurity is the new sexy. I mean, you've got to know cyber if you plan to do digital health. Otherwise, the risk at which you put patients and physicians in is significantly high.

Richie Etwaru: You can't hire a music major like Experian and hope that it would work. Michael, before you go, I want to touch on this notion of time for a little bit. There is a construct of time, that not being as important, that is left over in the life sciences industry, that we're now starting to wake up to. I'll give you really simple examples. I touched on this notion earlier. It takes about eight to twelve years from the moment you discover a molecule to the time you get it approved to be able to bring it to a patient. It takes about eight to ten years to do that. You know, if you think about an automotive company, let's say, Mercedes, for example, from the day they start to draw the first car when they do a full model design, all right? From the day they start to the day it rolls out on the showroom, it takes about three years, okay?

Now, that’s probably not as impressive as my second example that I want to share with you. If you think about the first video of autonomous driving, you guys remember that first video on the roof, all right? With those young kids with the car, with the cones, right? When we first saw autonomous driving, to the day when it was rolled out by Tesla and Google, you know, to be like commercially ready on the road, granted, it needed permission, it was about five years. It took about five years to go from a couple of young people on a laptop controlling a car with some cameras, to industrial-grade autonomous driving. That is an insanely different way of looking at time, as opposed to the life sciences industry, where we still do things in decades. And that part of it is dead.

Milind Kamkolkar: Yeah. I mean, if I could just add one last thing, Michael, to that point, I think where the time, perhaps has that risk-averse nature in the pharmaceutical industry is that perhaps I’m like… banking in other such industries… The reality is that if you get this wrong, people die, right? And that’s the real crux of it. So, I can fully appreciate and respect the fact that sometimes, you do want to be a little bit cautious because, of course, who wants to create a medicine or some kind of patient service that really is not yielding a positive outcome?

So for one, I'm quite grateful for the fact that we are basing more medication and pricing, and reimbursements, etc. really on health outcomes. But I think, in many ways, it's not just the actual medicine that has to take into play, it's also the customer experience at point of treatment that needs to be part of that equation as well.

Michael Krigsman: This is a very fascinating discussion where you talk about customer experience at point of treatment. And, let’s come back to it because we’ve got some questions on Twitter already. And, I want to remind everybody, go to Twitter, use the hashtag #cxotalk and you can ask our guests questions and they will respond! It’s a great opportunity to get access to these folks who are usually hard to reach. So, ask your questions using #cxotalk. And, we’ve got three similar questions from Wayne Anderson and from Ian Girtler, and from Chris Peterson who are all asking about security, privacy, the aggregation of data. And Milind, you mentioned security. Thoughts? You guys are in the industry.

Milind Kamkolkar: Look, I think this is perhaps one of the most… You know, when we think about how we organize our enterprise information management assets… You know, I remember back in the day when we used to do some of this work. You know, our security compliance legal teams were often the poor guys left at the last minute to decide on a particular initiative because the cool factor was all around the app, or it was all around the campaign. But, the reality is that has to change. It is changing, and it’s probably the most critical ingredient. I think recently you would have heard the Head of the FDA talking about, you know, building security is part of your design process when it comes to devices and digital health aids.

So, as far as I’m concerned, it is one of the first things we think about. You know, all of that really stems from the basic question: What is the intended use of that data, right? If you can’t answer that question, you probably shouldn’t start that project. I think the other two pieces that are becoming equally important in the utility of data is the social responsibility aspect of it as well as the ethical responsibility aspect of it.

It’s not that these are new concepts. They’ve always been there, but they’ve often been there, say, as part of the overall embedded program but not necessarily integrated programs that make sense. They’re always on the periphery, you know? “Oh, did we check off this piece?” “Yes.” Now, you can’t check it off, now it has to be, “Did we design data and exploration activities with these in mind thinking ethically, socially responsibly, and from a privacy perspective as well?” So, certainly for my team, this is something we take very, very seriously.

Richie Etwaru: I think it's good that security and privacy are becoming more actionable in the narrative because it was in the narrative for a while, but it wasn't necessarily something we were taking action on, right? One of the things that I find interesting, and this is exactly the way it was in financial services, right; when financial services was going through a transformational barrel a decade ago. It's that when you start to have conversations about security and privacy, the automatic reaction is to think about, "Oh, look at all these new datasets! They're dangerous." "Look at all these new pipes and integrations that we've opened up. They're very dangerous." And that's because it's an issue of familiarity. Most of the threat is actually in the data. You know, we've had for a very long time that we work with, that we've become comfortable with.

And, I think we make a mistake of thinking that when you do new things with new datasets, suddenly that must be more dangerous than the one we have right now. I think that's the first thing that we ought to make sure that as we go about this exercise — to start to unlock and unleash data to drive the integration to get those data-to-decision sort of outcomes that Milind's talking about. We don't sort of mistakenly misappropriate where the risk really is. Right? Because, there are still a lot of risks internally.

But, I think what’s even more interesting is that we are at a point now where there are technology paradigms that are allowing us to have data that is protected but shareable at the same time, okay? We used to have to make this binary decision before whereas if the data’s protected, then it can’t be shared. And if the data is shared, then it’s probably not protected. And, I’m going to intend which paradigm that is, if you’ve been following me, you know it’s what it is! But, we have a reality in front of us with the invention of the blockchain, with asymmetric cryptography and distributed ledgers to truly start to build business models where data can be protected but shared at the same time. And that, I think, is opening up a lot of opportunities.

Michael Krigsman: So, we have another comment, a really interesting comment, from Twitter. And, this is from John Nosta, who says…

Milind Kamkolkar: [Laughter]

Richie Etwaru: Hey, John! [Laughter]

Michael Krigsman: [Laughter] All right. So, John Nosta is saying, “Yes, it’s true!” As one of you said earlier, if you fail in the pharma industry, then people die. I think, Milind, you said that. But, he points out that, quote, “Snail pace success also results in deaths.”

Milind Kamkolkar: Could not agree more.

Michael Krigsman: So, how do you balance? How do you balance this need for speed and obviously, there are elements of our healthcare system that are just failing miserably. So, what do we do? And, link it back, again, to this notion of silos that you were both talking about at the beginning.

Milind Kamkolkar: Yeah. I mean, I think a lot of it really comes down to, yes, there is a historical paradigm which is still very much prevalent. In any pharma, or even, I would argue, most biotech companies as well, where the knowledge base under which we operate is often underwritten by risk. And those risk parameters that are often created are done either protecting liability of an organization and ideally protecting the protection of the consumer or patient in this instance. And, I think it’s a fine balance, right? I don’t think there’s any sort of golden key in terms of how do you uncover that. But, what we are seeing, though, is that the use of technology allows us to accelerate some of those processes quickly where, for example, the use of machine learning or deep learning in some of these instances allows you to explore much larger populations and look for non-responders as much as you see responders.

And, if you really think about, you know, clinical trial management in many regards, this is an area we are often looking to see the positive clinical outcomes, right; and balance that with the underwritten risk of a trial. In post-clinical trial, and that's why I often find it quite interesting, we have a treasure trove of information already. We don't need to buy, necessarily, additional data, if you will. If we just mined what we already had in a more sophisticated way using algorithms to go faster, we might already uncover new mechanisms of action, new therapeutic conditions, and so forth that I think… You know, will it be faster? Yes. Will it be, you know, like the bullet train? No. And, I think we need to have a fine balance between both of those. But, I do agree with John, that there is this historical basis of working at snail pace. A lot of it is also because the adoption of technology in this place has been quite limited as well.

Richie Etwaru: I think one of the things that are changing in the industry; and by the way, John's the best looking guy in the state of New Jersey, in case you're wondering; one of the things that are changing in this industry, which we're seeing the same pattern coming from other industries, is that the demand side, right? The demand signals, the patients, the way they start to work together in communities like patients like me, the way they start to get involved in understanding therapeutic areas and researching illnesses and treatments, the patients are waking up, so to speak, metaphorically and are starting to say to the life sciences industry, "Hey, I need it better and I need it faster."

So, I think there’s a lot of inside-out debate and discussion that we have around how we change our operating model and how we move faster, and how we optimize. But, the final say always comes from the customer. And, you’re seeing more and more patients interested in early clinical trials because they’re becoming aware that this is there. You’re seeing more and more patients have discussions about pricing and about reimbursement, and about the efficacy of a drug. And, I think it’s the end of the supply chain. I think it’s on the demand side, where the customers are actually going to wake up and are going to force us to get out of this snail pace, as Nosta describes it.

Milind Kamkolkar: Yeah, I think, you know, if I could add to that, Richie, you’re 100% correct. I really believe that this healthcare industry is being driven by customers and these customers are not only becoming more digitally-savvy, but they’re also becoming more information and context-sensitive as well. You know, I’ve worked with the COPD Foundation, which is such a wonderful organization, in in the past and I can tell you that the patients that we met in those groups knew more about their disease; knew more about the molecular mechanisms than the latest research in this area that really, I was truly humbled to really understand not only what they’re going through contextually, right; just living with that disease; but also scientifically in their notion to fight for truth, if you will, and access to information which is so critical. And, we still have some major boundaries to get over there.

I think where the paradigm shift is going to happen is when you do start having these trusted relationships. If you think about what a farmer probably is trying to do ultimately, it’s really trying to enrich that relationship between physician and patient. And, we all know that even with the advances of digital technologies, the time a physician actually gets to speak face to face with a patient is getting more and more limited. And, we would think, that’s the disturbing effect of digital, right? And I actually think there are a lot of elements within digital, and particularly the digital dystopia, that are probably more advantageous to being able to provide patient support programs which ultimately then lead to companies like ours progressing faster to provide these services.

Richie Etwaru: And, this is a known pattern, right? The financial services industry has gone on through this. I'll tell you what I call the pattern. I call the pattern the phenomenon when an industry goes through an industry that has to provide answers to an industry that has to provide evidence, okay? About fifteen years ago, I remembered the day when financial services went through that sort of change. I was on the phone with my broker and I was talking to him about some trade that I was trying to make, and I just looked up for a second. I said, "Wait! I know more about this trade than the person on the other side of the phone! Why am I calling them, right?" When the financial services industry went from an industry where you would go to the industry for answers, to an industry where today, you've got all the answers. You're going from sort of evidence, right? And sort of confirmation. The life sciences industry is going through that same change. We were at a point where people came to us for answers, right? “What’s x, y, z? What’s a, b, c?”

Today, our customers are not looking for answers, they're looking for evidence or confirmation. And as you talk about that constricting sort of moment there, where a patient and a provider, a doctor, a PA, a nurse, gets to chat, even the delivery side has to go away from sort of only providing answers, to start to really contextualize that conversation properly. These are the changes that I think are coming from the outside of the industry that will marry with some of the great stuff that guys like Milind are doing, to really get us to where we need to be.

Milind Kamkolkar: I think, in some ways also, they’re really helping in dislocating the silos that we have today. If you really think about it, right? Typically, you go to a pharma company or a biotech company, you’ll have therapeutic area base divisions; you know, cardiovascular, diabetes, neuroscience, etc. Now, oncology, etc. And all of those groups, God bless ‘em, because they do work very, very hard to be able to provide the right services to patients. But, the reality is they're often incentivized and motivated by supporting that one therapeutic area alone. But, you know who doesn't care about that? Patients. Because, sometimes, patients have different, you know, unfortunately, different diseases or conditions at the same time and they don't care how we're constructed. They're looking for solutions. They're looking for services to help lead a dignified life. And I, for one, and one of the things I've experienced is really profound here at Sanofi, I've seen this demonstrated. I've seen this led. I've seen the projects that we're doing right now and we're really making a conscientious effort to break down some of those silos and really tackle these issues from a patient-centric perspective and also, from a physician and caregiver perspective, as well. It's really looking at the holistic care package.

Michael Krigsman: You know, John Nosta, again, he’s raising some very, very interesting points on Twitter. And I just want to remind everybody, use the hashtag #cxotalk. There’s a very vibrant discussion going on about these issues on Twitter right now and you should definitely be participating in that. So, he’s talking about clinical trials. He’s saying the future is about genomic-based AI augmented trials for smaller patient groups. And, he doesn’t want a doctor… He says, “I don’t want a doctor anymore. Give me the power of AI and I’ll adjust that genius bedside manner.”

Richie Etwaru: This is when I love when a Chief Data Officer and a Chief Digital Officer get trolled by Nosta on Twitter. [Laughter]

Milind Kamkolkar: [Laughter]

Michael Krigsman: [Laughter]

Richie Etwaru: See, this is where you have to balance reckless disregard with responsible action, right? At the end of the day, the statements that John are making — of course, we all live that every day, right? There's no one of us affected in the industry who are driving the change that doesn't recognize that. But, we have to be responsible in our actions to recognize that some of these discussions are on the edge. And yes, it's our responsibility to scale the edge and bring them to the center of how we change. But at the same time, change in large, commercial organizations, requires the marrying of that reckless disregard and responsible action at the same time. So, there's no disagreement here that, yes, genomic data is certainly a landscape that will be fruitful for moving particularly from care to prevention. And implementing artificial intelligence in certain areas will be beneficial as yet.

I would argue that AI is probably a little bit too much of what we’re overpromised, and we’re being too irresponsible with it. But, we have to recognize that in any given instance of the history of our species, when we’ve made macrostructural and social changes, like the ones we’re about to go through where healthcare is about to become demand-driven, as opposed to supply-driven, there’s a place for the reckless disregard but at the same time, it has to be married with responsible action. And I think that’s what being in the healthcare industry is about today. If you’re a Chief Data Officer, transformation officer, you have to balance those, too.

Michael Krigsman: Two points here. Number one is we have a question from Twitter. Victoria Walters has asked twice and she's getting annoyed because I have not asked you this, which is value-based contracts; what do you think of them? Are they truly valuable to the customer, to customers or patients? And then, I want to come back to AI because an article just came out in STAT Magazine journal about how Watson is not working. So, can we take a quick detour; value-based contracts because Victoria Walters is mad at me that I haven't asked you that?

Richie Etwaru: Yeah, Milind, you take Victoria’s question. I want the Watson question! [Laughter]

Michael Krigsman: [Laughter]

Milind Kamkolkar: [Laughter] Well, I’ll cover a little bit of that but I’ll let you do that as well! All right! Okay, Victoria, so value-based contracting. I think we’re at the early stages of what actually a value-based contract actually means. Initially, it’s based on specific clinical endpoints based on a targeted population. And often, these targeted populations have inclusion and exclusion criteria that don’t necessarily meet you, as an individual, when it comes to a point of reimbursement, right? That said, I think the science is getting smarter. And I think the contracting over a period of time is going to learn how to make that more effective.

I want to give you a very simple example. I do think, in many ways, it's the right way to go. I just don't think we're there yet at the level where it's satisfying and benefiting patients at a holistic degree. Let's take a hospital, for example. And you have a terrible experience in the hospital. The likelihood, potentially, of you not having a great outcome is, in fact, higher whereas if you had wonderful customer service all the way through, you could argue that, yes, the outcome is higher. So, in the contractual language, who then owns that component of the outcomes-based contract? Is it the patient satisfaction score when being hospitalized and treated? Is it the clinical evidence that's coming from the medication? Or, is it something more?

And I think these are the things that we need to start drilling deeper into. I know there are a lot of watch streams going on in this space, but Victoria, my apologies, I think we’re just really at an early stage of that market. There’s a lot more work that needs to be done to really define what that means. You bring up genomics and AI. And again, John, a lot of the things. But, you know, one thing that I tell the team internally; one of our primary job descriptions is to know when to triage bullshit versus when to triage other things that exist out there.

Michael Krigsman: Hey! This is a family-oriented show! You can’t say, “triage bullshit” or not. Anyway, go for it!

Milind Kamkolkar: Well, let me put it this way! BS and buzzwords lead to a lot of wasted pilots, right? And God knows we have a dead sea of pilots going on today. Look, I think we're at a really remarkable stage at ML, right? I'm not going to say "AI," because honestly, AI is a buzzword that's grossly overused at this point in time. Nobody's doing AI. We're doing natural language processing, we're doing computer vision, we're doing other elements that may constitute as machine learning algorithms supplied within an AI family, including deep learning. AI is a farce at this point in time in terms of where we are. In healthcare, I think other industries are probably a bit further ahead, but in healthcare, we're not there yet.

When it comes to genomic data, I fully agree. Genomic data probably has one of the most wonderful opportunities around the utility of machine learning and deep learning in these areas. But what’s changed from forty years ago, when these classification algorithms, these clustering algorithms, are already there in a naïve Bayes or a random forest, see; they were there many years ago. Computational processing power; cloud; these are the things that have allowed the utility and application of what we could have done many years ago a lot easier. But, why is it easier now? Because your resource allocation to provide the cost necessary to use that infrastructure is a lot cheaper.

Back in the day, we had blade environments to be able to do high-performance computing. But if you asked yourself, well, if that was really all then there, why is it still taking almost 48 hours to do some of these analyses, right? That’s what I would argue. The opportunity for most of the work that we’re doing… Look, I love AI as much as the next person. I mean, just look at my Twitter handle. You’ll know what I mean, right? I do love AI as much as the next person and I do believe that’s where the world is going, but we need to have ethical responsibility in the utility of those, and not overuse buzzwords like AI when, in fact, you’re actually talking about a very narrow field of machine learning solving one specific problem.

Now when it comes to Watson, and you need to be careful here, I do like IBM as a company. First and foremost, I think the one thing that I really cherish about IBM over the years is their ability to transform their existing business, time and time again. And, that’s amazing. You really want to look at transformation industry? They are the gospel, if you will, in many regards of how that can be done.

I also like them because they allowed, through the cognitive computing environments and their wonderful marketers who I’m sure made a ton of cash in this process, enabled an extra zero on my budget line item. Because, now, I was being asked a question: “Milind, what are we doing with cognitive?” “What are we doing with AI?” “What are we doing with machine learning?” So that was great. That was cool. So now, I was able to hire a bit more stuff to start looking into these things.

But you know, I think, Watson's at an interesting point where, and of course, the last thing I would say about Watson is that their research is perhaps some of the most brilliant minds on the face of the planet; I mean truly impeccable people that work at the Watson research facilities. Where I think the rubbers hit the road is sometimes the overpromise of what can be done and by when. Now, I know that I've had some good use cases. I'll be the first to say that's great. And that's wonderful because it's trending in the right direction. I think sometimes, it's easy to pick on the big guys because they're just big, so the target's bigger. I think they're learning as a firm. I think they redefine how they approach the industry. I don't think it's all going to be about professional services anymore while that's still honestly still their main model, but it's, you know… It is what it is.

Michael Krigsman: Richie, we have about seven minutes left. So, and this conversation has just begun. So, Richie, again, John Nosta, I feel like John Nosta is part of our conversation here. He’s on Twitter and you guys are here. So, John Nosta is saying, “Well, so is Watson marketing over technology; over results?” Richie, your thoughts about this AI topic. And then, let’s move on to a couple of other things quickly before we have to close out.

Richie Etwaru: So, I share Milind’s perspective here, right? I have a tremendous amount of respect for IBM as an organization and the people there, and they’re doing a great job. I think that first and foremost, if you think about the comments from Elon Musk and others around AI, one of the things we have to do; this is for the non-expert viewers; is to always separate robotics from artificial intelligence. I think the entertainment industry has sort of merged those two at our heads, where we think about robotics and AI, it's hard to separate them.

AI itself, as algorithm or software, has a lot of different sub-sciences to it, okay? There’s reasoning and problem-solving. There is knowledge representation. There’s planning, learning, natural language processing, creativity, general intelligence, social intelligence… There are a lot of pieces of that science that I will call “sub-sciences.” What Watson has done extremely well is to really move forward in the area of natural language processing. I’m not saying that they haven’t moved forward in other areas. I’m saying there’s a tremendous amount of benefit to that. It’s raised the conversation in the enterprises for us so that we can start to experiment in these areas. There’s a lot of other small startups that are operating in the small pieces here and there.

But I think the hype, if I may, it doesn’t fall on IBM’s shoulders. It doesn’t. The hype actually falls from the analysts and the reporters who are the ones that are getting ahead of their skis because they’re looking for clickbait, right? I don’t hold IBM responsible for the overhyping of Watson or artificial intelligence. I think it’s a brilliant commercial strategy. There’s market conditioning-led [garbled speech]. I think the responsibility is on the analysts and the writers who are getting way ahead of their skis and don’t just understand the subject matter well enough to write intelligently about it.

Michael Krigsman: We have another question and as an industry analyst, yes. You know, mea culpa I suppose, right? [Laughter]

Richie Etwaru: [Laughter]

Milind Kamkolkar: [Laughter]

Michael Krigsman: Hey, I don’t mind calling a spade a spade here! [Laughter]

Milind Kamkolkar: We’ll keep talking about it because we’ve still got more stuff to talk about, so fine!

Michael Krigsman: But, we have another question from Twitter. And, Wayne Anderson has got this… Tweets have flown by so fast. He is asking, "How does data help make the decision about when to be recklessly innovative versus to be cautious and balance that risk?" So, the role of data in innovation versus risk-taking.

Milind Kamkolkar: Okay, so let me clarify a few things. First of all, innovation in itself is a risk, right? The notion… I mean, the whole idea of innovation; people often mistake innovation as being blue-sky thinking. It's very easy to come up with ideas. It's very difficult to come up with things that are either commercially or utility, or experience-based, where you're driving positive outcomes in innovation. So, I would argue, innovation, by nature, is a risk-based venture. That's why we have companies like venture capital firms that are measuring risk or startup companies who have brilliant ideas as part of their valuation, right?

So, I think when you use data in those instances, you're often looking for a couple of different things. Number one, and first and foremost, what is the problem we're trying to solve, right? If you don't start with that, as a hypothesis, that's when you can use data to start either creating a null hypothesis or an outcome-based hypothesis. And, I think the beauty of innovation in this instance when you start having a data-driven, experimental approach; is that you can very quickly reach conclusions or pivot accordingly based on where you are in that life cycle of the experiment. So, I think as a pharma company, for example, I mean, this is what we've done. This is how we've grown. We've designed experiments and produced products, but we often don't apply that same methodology, that rigor, to experimentation when it comes to innovation. In reality, they’re exactly the same thing. You have an aim, you have methods, you have materials, you have a hypothesis, and you have results and conclusions that derived from that.

So, I would say, you know, we should not separate innovation and, let’s call it “day to day operations.” In fact, I tell our company, you know, as employees, we really have two pools of employees. We have those that want to do things better, right? So, you can have innovation within that bottom line factor, if you will, but then, there are those that want to do better things, right; that break the rules a little bit. But, you can’t have only one type of group. You need to have both, right? And that’s when our data allows you, in many ways, to be able to resource allocate accordingly, based on what the business outcome or the projected customer experience outcome, or frankly, what the value is that you’re trying to achieve.

Michael Krigsman: Richie, I know you’ll have thoughts on this. I’ll ask you just to keep it short because we’re just running out of time.

Richie Etwaru: Well, I think we should do it on another show. If we’re running out of time, we should take the time to say proper goodbyes and sort of closing statements here, so I’ll pass and I’ll look at our closing statements.

Michael Krigsman: Okay! So, as we finish up, I mean, the time has just flown by here. So, as we finish up, let me ask you for your kind of summary or your… The distilled wisdom of your broad experience? And if you can come boil that down to about a tweet-sized statement? [Laughter] Milind, you want to go first on that one?

Milind Kamkolkar: Believe in data, be responsible.

Michael Krigsman: Wow! That was really good! “Believe in data and be responsible.” So, maybe elaborate with another tweet.

Milind Kamkolkar: [Laughter] Think about the ethical and social implications of the use of information for good.

Michael Krigsman: Okay! Fantastic. And, Richie, your final thoughts and, you know, maybe address that silo issue that we spoke about right at the very beginning?

Richie Etwaru: I think the life sciences industry is suffering from what I would call a trust deficit. I think we do great work in the life sciences industry. There are smart men and women here that toil away at improving lives. The issue is that we have silos that prevent us from being completely transparent and sharing the great things that we’re doing and the progress that we’re making. And, we have mechanisms in place that lower our transparency, okay? At the end of the day, you know, when Apple launches a new product, everybody lines up in front of the Apple store or the Apple campus. But, when Pfizer launches a drug that saves hundreds of thousands of lives, nobody’s lining up in front of Pfizer headquarters at 42^nd Street in Manhattan, okay? I think the industry is suffering from a lack of trust and transparency, and I think we’re going to see, within the next five to ten years, the life sciences industry go from one that is either being chastised or hated, to one that is being liked or loved. And, I think trust is going to be the factor there.

Michael Krigsman: You know, we’re out of time, but Milind, I mean, this is such a kind of powerful statement. And so, Milind, any final thoughts on this notion of trust and transparency? I think it’s so important.

Milind Kamkolkar: Absolutely! I think, again, it comes down to the ethical use of data to drive meaningful decisions, right? If you derive meaningful decisions and allow people to understand the communication of those decisions, then you reach a place of trust or at least, understand what the key issues are in that trust relationship so you can further advance a more meaningful relationship.

Michael Krigsman: Okay! Fantastic. And, I will just have the last word by pointing out that on Twitter, Bob Egan, who is an industry analyst, says — is agreeing with Richie Etwaru that analysts don’t write intelligently about AI because they’re more focused on clickbait.

Richie Etwaru: [Laughter]

Michael Krigsman: And so, there you go! What an interesting show and I feel like we've done forty-five minutes and the time has just flown by! You have been watching Episode #255 of CxOTalk and, a huge, huge "thank you" to our guests. Milind Kamkolkar is the Chief Data Officer at Sanofi Pharmaceuticals and Richie Etwaru is the Chief Digital Officer of QuintilesIMS. Thank you, everybody, for watching! Be sure to "like" us on Facebook and be sure to give us a thumbs up… No, even better; you should subscribe on YouTube! Please do that. Go to cxotalk.com/episodes and you'll see all of our shows. We have more shows coming up. Thank you, everybody, have a great day. Bye-bye!