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How Data Science is Transforming the Healthcare Industry

Curren Katz, Senior Director for Data Science & Project Management at Johnson & Johnson, discusses how the healthcare industry presents a set of unique challenges for data science, including how to manage and work with sensitive patient information and accounting for the real-world impact of AI and machine learning on patient care and experience.
Jun 2022  · 34 min read

Adel Nehme, the host of DataFramed, the DataCamp podcast, recently interviewed Curren Katz, Senior Director for Data Science & Project Management at Johnson & Johnson.

Introducing Curren Katz

Adel Nehme: Hello everyone. This is Adel data science educator and the evangelist at data camp two years into the pandemic. The potential for data science and machine learning and healthcare has never been more apparent. Whether it's drug discovery, acceleration, operational innovation, virtual assistance, and disease prevention.

The margin of opportunity for data science and healthcare is massive. However that doesn't come without its own set of unique challenges and risks that require unique solution. This is why I'm excited to have current cats on today's episode of data. Framed current is a senior director for data science, portfolio management at Johnson and Johnson.

She has decades of experience at the intersection of healthcare and data science, and is deeply attuned to the state of data science and healthcare. Today throughout our conversation, we discuss where the landscape of data science and healthcare is. The unique challenges of applying data science and healthcare, the importance of ethical AI when working on healthcare use cases, how to solve some of the data challenges of the healthcare industry use cases, she's been excited about how data science was used to tackle COVID-19 and much more.

If you enjoyed this podcast, make sure to rate us and subscribe and add a comment, but only if you enjoyed it now let's dive right. Current. It's great to have you on the show.

Curren Katz: Yeah. Great to be here. Thank you for having me.

Adel Nehme: I'm excited to talk to you about data science and machine learning in healthcare, your experience leading data teams and complex organizations and how you've led R and D Johnson Johnson.

But before I'd love to learn more about your background and what got you into the data space.

Curren Katz: Yeah, absolutely. So I guess like, most people I've always loved data and my first statistics courses, I started to think, oh, this could be really, really fun. And especially when I started applying it to data, I had collected as a researcher. It was pretty addictive. And then as I moved along in my career, I'm a cognitive neuroscientist by training, but did SMRI research as well as looking at some large epidemiology datasets and 20 years ago wrote a paper on predictors of suicide.

Not exactly an AIML approach to it, but that interest in of like how can we predict some event? And then I had been in neuroscience studying neural networks, all of these things and applying actually machine learning techniques to FSRI images, which are images while someone's doing something. So it's a fairly complex, although clean dataset got me really excited, and then I've always been passionate about healthcare and solving problems in healthcare.

And my first corporate data science job was at Highmark health. So I started on the payer side, building a bunch of models and seeing how those models impacted care and was hooked. Move to the parent company. It's an integrated healthcare system. Second largest integrated payer provider system in the U S and started a data science department at that parent company, looking at the payer, the insurance side, the provider side, and a few other diversified healthcare businesses, and then came to Johnson and Johnson where I am now.

And it's been a really exciting career where I get to see a lot of impact from
day-to-day.

Current State of Data Science and Machine Learning in Healthcare

Adel Nehme: Let's start off our conversation. I'd love to understand the current state of data science and machine learning in healthcare early in my career, about five years ago. And that's not too long ago. Healthcare was often and still is talked about as an industry with a large margin of opportunity for data science, but it comes with its own unique sets of challenges, which makes it slower in comparison to other industries.

Given your experience as a data leader in healthcare, I'd love to first start off our conversation by understanding. How you would describe what the current landscape of data science and healthcare it looks like today. And how has it evolved in the past few
years?

Curren Katz: Oh yeah. That's an exciting question.

And it's, it has evolved and different parts of healthcare I'll say are probably at different places and evolving and at different paces out of sometimes necessity. And you say there's a lot of opportunity in healthcare. There is. And I think it's one of those industries. You have to take a bit of a careful approach to anything new they're practically are regulations, and there's a lot of risk for something going wrong, but huge benefits.

But what I've seen over the last few years is really a couple of things that we're seeing in a lot of industries, but in healthcare as well, scale. As we're moving into, Hey, the data science can be very, very useful for solving real problems in healthcare. There's a focus on deploying these models and not just having perfect comps concepts, but really using them to drive.

Core business decisions and core insights. And that requires data science at scale, where at first it was a little more experimental, a little more. Well, let's just see how this goes alongside what we do today, but we're not going to go all in and really use this to drive our business. But we're moving towards that.

The other change, I guess, are the problems that, that can. Solve or just we're realizing them, right. We're expanding the scope of what data science can do in healthcare. And of course there's diagnostics, there's also operations, there's clinical trials and how those are run, how patients are found. There are so many things we can do.
And then a third, I really important, I wouldn't say change, but something that's just continues to mature and we think about, and I think has helped accelerate. Data science and healthcare. It's just thinking about the ethics of what we're doing. Considering it's impacting people and the care they receive, and it can be life or death, or it can either help or hurt disparities we're seeing in care.

So really have thinking about ethics, which is important in healthcare, and then having tools and ways to address that at scale has really evolved over the past few.

Adel Nehme: That's really great that I'm excited to unpack these with you even more. So you mentioned at the beginning, some of the areas of impact that data science and machine learning have in healthcare, do you mind expanding on these main areas of value where you've seen data science and machine learning, push the envelope forward within the healthcare
space?

Curren Katz: It's hard to pick a few, but one I love to talk about, and this is something my former team did. And I, I really, I love the way they approached this. And I saw it as impact patients was looking at operations. So sometimes in healthcare, we go at the we're going to cure this disease. We're going to diagnose this disease.

And of course, how do we not say we're gonna put every data science tool we have towards cancer and we should. But a safer way in, and a way in that makes a huge impact can be the operations of healthcare itself or the operations of a clinical trial. So I'll give you an example. When I was at Highmark health, we built a tool to help schedule patients receiving chemotherapy and a big thing for me to start with the problem we heard about, Hey, we're scheduling patients for chemotherapy.

They have long wait times, but she seemed to not great. We notice we're really busy in the mornings and then things are empty in, in the afternoon. So our clinicians are either overwhelmed or don't have a lot of patients. And we dug in that was two things. They didn't know how long a treatment could take and there could be side effects and clinicians want to care for their patients and make sure they have plenty of time.

So there are blind to how, how long each. Might need a stain there in that location. So if we're able to predict that weekend, start efficiently scheduling and then just optimizing the scheduling, optimizing the operations, where in the calendar, can this go? Where location wise, can this go? And we had this full ready when the pandemic started and it became even more important to space, vulnerable patients out.

It started with an operational challenge though. Schedule. Very practical thing to solve. And it made a huge difference. I I've heard stories from patients and saying, Hey, I, I can get on and back to my life and not wait. I can come at times convenient to me, but now their area that. And impacted a lot of promises, diagnosis, or detection, early diagnosis, early detection to give clinicians some time to intervene.

We heard about this and things like sepsis or acute diseases. We're talking about early detection of things like pulmonary hypertension, which is frequently diagnosed late. And I know that's something we're we're doing now. These are big, big areas of opportunity where we can treat patients. Because we can detect these diseases and diagnose them.

And then the third is patients' own experience like with the operational component, of course, that had a patient experience piece, but just understanding a patient's their journeys, where they're facing challenges, how they're experiencing the healthcare system and where we're not maybe delivering care in the way we should data can help us see that and help.

Deliver a better experience, deliver a more personalized, tailored experience on a biological level, as well as just a individual level preferences, ways of interacting and ways of receiving care.

Main Data Challenges the Healthcare Industry Is Facing

Adel Nehme: I love how you framed the operations component here, because whenever we talk about. Machine learning in healthcare.

We always talk about aspirational use cases that I think we're all in agreement are extremely important. For example, I'm very excited to see the impacts of deep minds, alpha fold and drug discovery, but that doesn't mean we can not create impact on people's lives right now with data science, just by solving operational challenges.

When talking about data science and healthcare, we often talk about challenges, unique to the healthcare space, such as access to relevant interoperable data, ethics of AI and a host of other. I'd love it. If you can break down, what are the main data challenges you think that the healthcare industry is facing?

Curren Katz: I talked to my colleagues across industries, everything men actually, and automotive are just very different industries and no one tells me our data's perfect clean. Hadn't really had a problem there or thought about it. Of course, you're not surprised to hear this. And in healthcare, we face that as well and interoperability in different four rounds of data where we're facing the same things.

But I think we're realizing. A other industries that face this and B you know, there are solutions that will work here as well. It's the whole topic. The ethics of AI is, is huge, a huge one here and really, really important. So this becomes crucial. And in healthcare, I'm not saying if, if you're selling a consumer, good, of course you don't want to.

But if I get a recommendation to buy a toaster oven and I just bought a toaster oven, so I'm probably not going to buy a second one. And this just happened to me. It's not a big deal. It didn't really affect my, you can experiment with those algorithms, get them out there and get them out there quickly.

And in healthcare, we've obviously had to think in other industries face this as well. There's risk. So you have to really think through what you're doing and what could happen and how this algorithm is going to work. What, how you're going to build this process and get it right. That's not to say there aren't things we can do.

There's a lot because there are a lot of problems and things we're not doing really well today. So as long as we're not making it worse, we should try some things, but that's always going to be. A really big challenge and an important challenge that we should take on relative to other industries. It's just talking about the data.

Obviously the sensitivity of the data itself makes it maybe a little harder to get access to data or think about how to use it, share it, what kinds of environments that data can be in. And it should be, I mean, that's a challenge we should take on as a good challenge. And the one we said. We're never good enough because this is the most sensitive data in people's lives.

So we should be continuously improving and thinking about how we protect this data, how we use it, how we make sure we're using it in a way that decreases inequalities and how we deliver care, which I think it can, but we have to use the data responsibly and consider it is very, very sensitive. Maybe more so than if there's a, a leak of that.

I bought a toaster oven, not that excited. I bought a coffee maker. Not that it's not that exciting, but this, this is a pretty big one.

Adel Nehme: I completely agree here. And let's spark the chat a bit and talk about the ethics of AI in healthcare. When we talk about using machine learning and AI in healthcare.

Version that whatever we develop will end up creating harmful outcomes or that it could be used irresponsibly. And oftentimes the response is not to leverage machine learning and AI. So I'd love to understand how you evaluate the risk of harmful outcomes of machine learning and AI in healthcare. And how do you go about minimizing it?

Curren Katz: Well, a great question. One big thing to understand the potential harmful outcomes. You have to understand the problem that you're solving. Be working collaboratively with a cross-functional team, with clinicians, with whoever is using and implementing and acting on your model with. You have to have everyone in the room and involved in this process and understand that end to end, because that's the only way you're going to find where the risks might lie.
You have to understand how, how. To use this information and make a decision. What mitigation is, can you build in, where are the risks at every point in the system? And that is sometimes something data scientists, especially when they get started, they're excited to build models and they skip over this piece of it unintentionally.

And when I read about, you know, resumes from the HR world, like the algorithms. What you feed it. And historically data reflects our human biases. So the algorithm, if you don't think about it and you don't account for that is going to learn to do exactly what people have done, which is not really necessarily ethical, but.

When with data and with an algorithm, we have an ability to mate to fix that and to control that a bit more than, than we do in people. But I always think about the end to end how the decisions being made. It can't just be about the algorithm. And another part is it sounds kind of simple, but empathy and the human centered design design thinking approach is very valuable for data science, because you start.

Putting yourself in the shoes of the person who's affected by this, the patient, all of the things they may be facing and all of the things that may happen based on the algorithm. So you've got to really think about it from that angle. And then it's of course the technology, the data it's. What biases are there?

The algorithms you're choosing, the ways you can mitigate and correct it. Can you and that's job, a technical expertise, a data scientist has to have, and it's essential now, especially in healthcare, but everywhere we want to think about. The other obvious one is really going way back and saying, did we pick the right use case?

And like the operations example, there is a lot of problems to solve in healthcare. We should be thinking about all of them, but maybe the easier, quick wins are ones where there's a little. Less opportunity for harm. If it's, maybe we're just randomly, we're communicating with everyone in the same way today.

And maybe if we try to figure out some preferences and try to customize a bed and learn from there that may be lower risk than detecting a disease or changing the course of care. And in medicine and health care, this doesn't replace a clinician. We want this to enhance the clinician's decision. Make.

Creating AI Governance Frameworks

Adel Nehme: That's awesome. And I love how you draw inspiration from other fields like human centered design, given that, do you think also healthcare can draw from risk management risk analysis to create AI governance frameworks?

Curren Katz: I think that is a great question. And absolutely there is no industry. We can't learn from, we have to be looking outside of healthcare all the time and looking across healthcare to different parts of healthcare, but definitely looking outside.

That's why I very intentionally hired people from other industries on my teams. I've wanted people from manufacturing and it has worked. They've come in and looked at things and said, this is not an easy, but a pretty easy problem to solve. We deal with this all the time and something that. Somewhat, my background is mainly in healthcare.

I would think. Scheduling certainly movement of chemotherapy drugs around a different locations that I thought it was well, that's a pretty big challenge, but I knew that other industries had solved it. And so I looked to people from those industries to come in and bring some of that thinking to healthcare risk management.

Of course, that is something we do. We have a risk mitigation plans for everything we do think through everything early, these, the, every industry we need to be looking outside all of the time in healthcare.

Adel Nehme: When thinking about some of the other obstacles that are unique to healthcare, such as data access and draw operability and collection, what needs to change.

So that data science, healthcare innovation accelerates here. Is it regulatory innovation industry standards that need to address.

Curren Katz: The regulatory component is there it's important. There's collaborative work and discussions going on across health care to make sure the regulatory environment meets the needs of data science.

That's an ongoing process. Another one though, that maybe is every industry, but I see it a lot in healthcare. The systems are very complex. We have different EMR systems. Those have a lot of steps in here. Data scientists don't always understand how a clinician interacts with that system yet. That's that may be the place where their solution is delivered, enacted on where the value is realized, but they're very complicated systems and to get them all to connect, maybe we want to use multimodal data from multiple sources, imaging devices, everything to really get a full picture of the patient at different timescales.

To really scale that solution and implement it. We need those systems connected that you can do it once, grab all the data, put it together and build a model. But how do you then deploy that model, seen some simplification of these systems and some consideration of the, Hey, it's very important to use this data to deploy solutions and to seamlessly connect and simplify things.
It would be great to see. And I think we're probably going to see that. As I said, it probably exists in other industries as well. The other one is experienced with data science, data literacy, or AI literacy. We don't need clinicians and hospital administrators. They don't need to be experts in data science spot.

I think as we all bring up that level of understanding and understanding. How data science works, how some of this stuff can be used and be able to speak a bit of the same language that would help. And then we're saying that in again, in every industry, but one, I think we have a good chance of solving in medicine.

A lot of people have a scientific background and it's data science has the science. So. It should be a good place. And I've seen a lot of engaged clinicians and a lot coming in with a lot of knowledge, experimental design, and that's moving along, but we could be better there and we need to keep, pushing.

Adel Nehme: And that data literacy component is huge from a data quality perspective, because a lot of healthcare professionals are the ones who are inputting this data into these systems. And if they do not recognize the role that data plays in the value chain of data science, then that value chain will end up breaking because no one is paying attention to the data quality, right?

Curren Katz: That's a great point. And it actually that data literacy, then it's going both ways. It's a business literacy on the data scientists, part of understanding. How a clinician is inputting data and how they're interacting with an EMR system, or how on the insurance side, maybe a care manager is identifying and reaching out to members of an insurance plan to help them coordinate their care and manage a chronic disease.

But we have to understand how that data comes in and conversely. If we show the value of data science, the people delivering care. And part of that healthcare ecosystem are going to be able to work with us and say, okay, like I can eat. I can see the value of this distinction as long as we don't take time away from their interactions with patients and make it.

Adel Nehme: That's also, and given we're discussing the value of data science and healthcare, I'd like to pivot to discuss your experience as a data and AI leader, a Johnson and Johnson. I'd love to understand and dig through some of the most exciting use cases. You've seen data teams working on, especially in healthcare at Johnson and Johnson, especially given what must've been a very interesting time for R and D teams with the release of the J and J COVID-19 vaccine.

Curren Katz: Yeah. There, there are three that really come to mind and one, we all are. So deep in it, it's always a great example. So this is, this is something I think is an excellent example of using data science to solve a real problem and make an impact when clinical trials are planned, as you can imagine, they're complex.

There's a lot of planning and. To decide where to have those trials. In the case of the vaccine, we needed to find places where there were there was COVID was, was spreading so that we could see whether this worked quickly and get it out to people. And what the teams are able to do using data science was predict where these, uh, future hotspots would be in plan the clinical trials in those.

Then it was effective and it allowed us to accelerate that and be really targeted and where we were doing clinical trials and where we were seeing high levels of COVID. So I think that's just a very great example and it shows data science can. Rise to the challenge and really solve big problems under pressure.

When it counts with there is no bigger, I'm really pressure in recent times, then the whole world's in this pandemic and we need to do something about or data science. So I'm really proud of that. The other, I think I mentioned the learning hypertension example, but just one example of how we can bring data.

Together and use AI to diagnose a condition earlier. And that's something we're doing and working on. That's very, very exciting. This is an under diagnosed disease, or it's not diagnosed early when, when we could treat it and make an impact. So if we can bring together diverse data sources and predict that diagnosis, we can really make a difference in people's lives.

And then the third is just generally using data to accelerate what we're doing and how we're doing. Um, at every part of the process, we could talk about that all day, but we using digital data and digital end points to better measure outcomes using real-world data, claims data, EHR data, to really make sure we understand the patients.

We understand their needs were developing drugs that are going to make a difference. And we're doing it efficiently and quickly because. It always strikes me that every day that this is not out there, the patient's not getting this treatment. So I love that. We're always focused on how do we get medicines to patients faster because.

This matters. And we all either have that know someone or will be effected by this.

Adel Nehme: I absolutely love the COVID-19 use case here. And it's really exemplary of a data science use case that requires relatively simple data science that can provide value now for patients and healthcare providers. So I'd love it.

If you can, back that use case even more and maybe discuss the methodology used here.

Curren Katz: I think it's a general process that really is important for solving any data science problem and at a high level. And I've done this set up multiple companies. It starts with identifying a clear problem in this case, right?

It was clearly, we don't know where to plan to have these clinical trials and it's not something we can spend on. In a day, it takes some time. So how could we know earlier it's finding that problem that can be solved with data science. That's one piece that was crucial here, and then it's collaborating, working together.

With the business clinical areas to design and implement that solution in time. Sometimes data science, if it gets too exploratory or just experimental, we're not thinking about the urgency and the timelines where we need to deliver and working closely as a core member across the team. And to, to make something like this happen, you have to do that.

Those are just two key things that have to happen at any high-impact data science use case. And I think ones that have served well. And then the third, a piece of advice I got very early and I've always used. I've seen, um, as a component of successful projects is really understanding how the solution you're building is going to be used and making sure the people who are going to use it are involved in the planning and have bought into this because you, if you don't have adoption, you're, you're not going to solve the problem that you wanted to solve.

Data Science in Large Organizations

Adel Nehme: So I think one thing that's evident is that there's a lot of different data teams at J and J doing different work. It's one challenge to do this data science and healthcare, but it's another challenge to work in a large matrix organizations, where there are tons of stakeholders and a lot of different teams working on different problems.

I'd love to know how you ensure that you're staying effective despite this complexity and some of the best practices you can share. And managing and working with data teams and large matrix organizations with other data leaders.

Curren Katz: I think a big one is coming back to the shared mission vision, what you're trying to do because in a healthcare organization or any organization, but definitely in healthcare and at Johnson and Johnson, it is very clear.

We are getting medicines to patients were saving people's lives at the end of the day. Cuts the matrix, the complexity of a large company. Sure. It's there, but the culture and the focus on the patient and what we're doing unifies and brings us all together and breaks down those silos. And I think if at any company, if you find and focus on that, the problem and what you all care about, how everyone's benefiting.

It really helps. The other is something I think is just crucial. Bring people in early from across your company, that those silos can happen. And B if it becomes more complex, when data science happens in the silo, and then you show up with a solution and different parts of the business are thinking, oh no, we needed to be involved earlier.

Or this is slightly off here. And it can be harder than it needs to be, which is. Brings me to the good part of a mate, large matrix organization and why I keep working for them. And I love to be at when I love to be the leader in a large matrix organization, you have incredible resources. You have experts, you have legal teams, you have supply chain.

There's, there's so many experts in. The area where you're developing solutions, that's it is a luxury to have when you're a startup. I talk to companies, people that have great ideas and they have to work so hard to just get access to, Hey, can you just tell me about some of the problems you have or how this works?

And if they don't have all of these resources surrounding them at a large company, you have so much support and you can never read. Too much or too early and think about, Hey, you know what, I'm struggling a bit with, maybe how do you think about marketing? Oh, we have a marketing team. They, everybody loves to get involved and they love to help.

And most companies, I think you'll find this. So reach out and use those resources that make a large company great. Because otherwise you're going to have. All the bad parts of the big company and not at the good parts and that, why do that?

Adel Nehme: That's great. Then it must be especially rewarding to have access to healthcare subject matter experts across the value chain, because this will help you develop this empathy to create human centered data science solutions.

Curren Katz: Exactly. No, absolutely. And we have that easily just phone calls. Quick message away. Like we're people are happy to talk and using that as key. Yes. It's wonderful to have great to use.

Adel Nehme: Awesome. So I'm sure these conversations with subject matter experts also influenced a roadmap, given the importance of R and D in the healthcare space.
How do you ensure an adequate split between long-term research and short-term wins that can help you move?

Curren Katz: Yeah, absolutely. And right now I'm in this R and D environment, developing medicines, and it's a long term view, which is really interesting to see and to have that said there's a lot of short.

Pieces and wins along the way to get to that end goal. So if you're working with the clinical teams and as we do, we really work together or in any company you're working with the business area and talking about what is that end to end? What's the ultimate like kind of long-term outcomes and then work backwards.

What are the pieces? And those quick wins, as you say a lot to get you there, you get that mix. And then I think it's important to look at. At the portfolio you have for data science and go through and see how many of these are really, it's going to be years before we see the value. And that's something in data science, you need to know because you have to be careful not to let that timeline and that pace of technology and changes conflicts.

You've got to think about it early, but yeah. Looking at how many long-term projects we have, how many short, quick wins do I have? And then also. It's okay to have purely exploratory. I'm going to play around with this data. See if I can develop this model. That's great to have too. It's just looking across the portfolio and making sure that the percentage of work that's in all of these buckets is where you want it to be a, need it to be.

Adel Nehme: And how do you determine which areas to research in your R and D agenda?

Curren Katz: The good thing is in an R and D organization that happens at such a high high level, but to bring it back to one simple concept it's unmet need and what do patients need. And I think it's something that applies everywhere that where's there an unmet need where we can bring data science, but of course, that's.

Goes into the planning of what do we develop? And it's a pharmaceutical R and D organization. It's a big process. It's the core of the business. And then there's the data science component. How does data science support and accelerate and enhance. That, that portfolio and that, that R and D process. And as we mature and talk to each other and data science grows, which we're doing a Johnson, Johnson, Johnson, R and D, which pharmaceutical companies of Johnson and Johnson, the data science team and capabilities are just exceptional shot.

Con is our chief data. Officer has built just in a really incredibly advanced capability. And, and the company is putting a lot of investment into data science and R and D and commercial and across the company. Great to see. And that shows me that there's right. We've had the discussion about this can impact.

The R and D portfolio, this can help you meet your goals and we've had that conversation it's been successful, and that's why we're able to, to grow and really use data
science.

What are you looking forward to?

Adel Nehme: Now, Curren, as we close out, I'd love to have a look into the future. And what you think are the data trends and innovations that you're particularly looking forward to see within.

Curren Katz: One that is very important. And I'm very excited about is the concept of fairness. So we talked about the risks and the reasons people don't want to use AI in healthcare. And this one com comes up a lot and it really is. Any kind of high stakes industry, it affects that industry, but I'm really excited about the capabilities and the thinking that that was evolving around fairness, both being able to detect bias and unfair pieces of the algorithm, and then even fix that on the fly at scale, make corrections.

I think that has the ability to allow us to really use data science, AI, and machine learning and healthcare, but it really. Brings a ton of value to, to people, to patients and make sure they're getting care. That is fair. That we're considering things that maybe we haven't been great at in the past, and maybe this can make medicine a bit better or any field of it better.

So fairness is a huge one for me. Future trends. Of course, I think we're going to continue to see scale. We're going to continue to see a bit of a, I don't want to say a catch up, but we're in a nice position to leapfrog other industries, right? Really perfected or made a huge, a lot of the advancement and embedding AI into every part of their business.

We can take the technical learnings and platforms and pieces and start from there and healthcare. And I think we're going to see that continue to grow because as we start making an impact, we're going to need to consider how this becomes a core part of healthcare

Adel Nehme: Caryn. It was great to have you on the show. Do you have any final call to action before we wrap?

Curren Katz: You know, it is to focus on the impact. Like I just always encouraged data science and data science leaders to think through how is the state of science solution solving a business problem? How is it making an impact and how is it doing something the right way?
So focus on impact, understand the context, be fair, but really go all in and make a difference because data science we're ready for.

Adel Nehme: Thanks for being on dataframed.

Curren Katz: No, thank you. Thanks for having me.

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