How IT Can Make or Break Your Biotech IPO with Nam Doan

How IT Can Make or Break Your Biotech IPO with Nam Doan | Essential Tips for Small Biotech Companies #biotechit #biotechstartup #itleadership 

In this episode, we sit down with Nam Doan, a seasoned IT leader who has worked with several biotech companies at critical moments, including before, during, and after they’ve gone public. Please visit our website to get more information: https://swangroup.net/ 

He shares his insights on the role IT plays in ensuring IPO readiness, building strong data systems, and balancing speed with compliance in the highly regulated biotech industry.

Unlock the secrets to creating smart IT systems that can set your biotech company up for success. Learn why data quality matters more than you think, how poor systems can derail your IPO, and why strong IT leadership is essential for a smooth transition. 

We also dive into AI, cybersecurity, and how small biotech companies can protect their data from becoming a liability. Whether you're in biotech or planning to start a new company, this conversation will give you valuable insights into how to build a strong IT foundation.

Links from this episode:

✅ Get to know more about Nam Doan: https://www.linkedin.com/in/ndoanhuy  

🔔𝐃𝐨𝐧'𝐭 𝐟𝐨𝐫𝐠𝐞𝐭 𝐭𝐨 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐨𝐮𝐫 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐬.

https://www.youtube.com/@Biotech_Bytes/?sub_confirmation=1 

🔗 Stay Connected With Us.

LinkedIn: https://www.linkedin.com/company/the-swan-group/ 

Website:  https://swangroup.net/   

🎬Suggested videos for you:

▶️ https://www.youtube.com/watch?v=NQN6X206A94 

▶️ https://www.youtube.com/watch?v=ZWXIIe66-kI 

▶️ https://www.youtube.com/watch?v=T6EzJ1F_6pg 

▶️ https://www.youtube.com/watch?v=be8szNVFrNk 

▶️ https://www.youtube.com/watch?v=_Be6WEEy2JM 

#biotechit #biotechstartup #itleadership #dataquality #iporeadiness #aiinbiotech

Chapters

• 0:00 Introduction
• 1:25 About Nam Doan
• 4:04 Why business systems often lead to IT leadership
• 9:29 The impact of failed ERP implementations
• 12:32 Compliance for public vs. private/PE-backed companies
• 19:31 Why good data matters and when it shows
• 24:53 Can good data turn bad? The case for ongoing data curation
• 27:50 Hiring junior staff to manage data under senior supervision
• 30:20 AI's current and future role in biotech and beyond
• 38:22 The cybersecurity threat landscape for small biotechs

Transcript

Steve Swan [00:00:00]:
Join me for a conversation with Nam Doan. He's been the head of IT for many small biotechs that have either recently gone public or are about to go public. And we discuss the ins and outs of it's role in a small public biotech. Also, if you like what you see on Biotech Bites, like us on Spotify, Apple or YouTube. Welcome to Biotech Bites where we speak with IT leaders in biotech about their thoughts and feelings around technology and trends that we're seeing today. I'm your host, Steve Swan and have the pleasure of being joined today by Nam Doan in San Francisco. Nam, thanks for joining us.

Nam Doan [00:00:41]:
Thanks, Steve. Excited to be here.

Steve Swan [00:00:44]:
How are you today?

Nam Doan [00:00:45]:
Good, good. I'm great. Little bit foggy in San Francisco as it usually is in the month of May, but doing well.

Steve Swan [00:00:54]:
It happens. It's dealable. Right. So, so, so usually when I start out, you know, I, I want to give our users and, and our, our audience, you know, just a little bit of base knowledge on my guest. So usually my first question's pretty much, you know, along the lines of tell me a bit about yourself and how you came into the IT industry and biotech itself. So you can give us a little background there. That'd be awesome.

Nam Doan [00:01:17]:
Yeah. So you know, little bit about myself and as you can probably tell, I'm. I grew up in Australia. Right. I've got that little bit of the Aussie accent.

Steve Swan [00:01:25]:
Yes.

Nam Doan [00:01:26]:
Maybe not as much as I used to have. I've been here for a while. So I actually came to the US as an exchange student at Cal Berkeley across the bay from San Francisco. And that was my senior year of Comp Sci. And so I did that at Cal Berkeley. And then I was offered a job by Oracle Consulting to, you know, work in Silicon Valley. And that seemed like the better thing to do rather than fly home to Australia with no job and no money. So I started working for Oracle and they trained me up to implement ERP systems.

Nam Doan [00:02:03]:
So I got trained up to do that straight out of college and I spent a good 8 years implementing ERP systems, like building up that expertise. And eight years is quite a while to be implementing ERP systems. And at the end of eight years, I kind of came to a realization I didn't want to necessarily be implementing ERP systems for the rest of my life. And so that's when I decided to jump to the customer side, which is for implementations, it's the IT side. And so initially I was managing an ERP system and then kind of grew my Knowledge base to other business systems like CRM systems. You might remember Siebel, I'm kind of dating myself.

Steve Swan [00:02:55]:
That was tough.

Nam Doan [00:02:56]:
That was tough. Yeah. So CRM systems, HR systems, business intelligence, and really built up a career in IT management from there. But you know, a strong foundation in business systems, particularly ERP systems, which are kind of my bread and butter, if you will.

Steve Swan [00:03:15]:
Yeah, well, I've found that through the years. Right, and you've graduated into a point where you're, you've become the head of IT for many different organizations and we'll get to that in a minute. But what I've found over the years in doing this for 26 plus years is that the folks that find themselves into IT leadership roles, for the most part, not always, but for the most part, the majority of them, I would say 60% or so, either come from ERP or infrastructure backgrounds because, and I think, correct me if I'm wrong, they cut across all, all areas of the company. Right. Both, both the ERP and the infrastructure. So you get a full picture of what's going on. Would you agree or disagree with that?

Nam Doan [00:03:56]:
No, I, I, I do, I do agree actually. No, I think even between business systems and infrastructure guys, it's more weighted on the business systems guys. And the reason for that is because we work so directly with business leaders to understand the business. So we, we really, you know, partner with the business in a very direct way where we're implementing systems to, to enable them. So we, to do that we have to understand the business. Right. And so we have a deep view. A deep view and a 360 degree view of the business.

Steve Swan [00:04:33]:
And that's been your wheelhouse really too from our conversations in the past, I think the business partnering, right, that's been something you've hung your hat on quite a bit and done a great job at.

Nam Doan [00:04:43]:
Yeah, definitely. And yeah, I don't think you can be an IT leader without partnering with the business and understanding the business. And you know, I think the, the eight years at Oracle Consulting, doing implementations for various types of companies, various sizes, various industries, it helps you build up kind of the, a business context of understanding different businesses. Right. You know, manufacturing company, biotech company, you know, a chip manufacturer, you know, you know, worked for a retailer. Right.

Steve Swan [00:05:18]:
What made you want to say, hey.

Nam Doan [00:05:20]:
Biotech, let's stick with this, you know, biotech. I came into biotech because of, you know, my first experience with biotech was with Twist Bioscience and that was because I, I actually knew some of the founders, right. And so they understood that I was an IT leader and at that time they needed some IT leadership. And so I was, I was the guy. So it kind of came sideways into it, but then also then continued on to do Third Harmonic, which is a drug development biotech. And so that's been my experience in the last seven, eight years. And it's, IT leadership can be very similar in different industries, but you know, you do need to have some industry specific knowledge as well, particularly when it comes to things like compliance. Right.

Nam Doan [00:06:17]:
So.

Steve Swan [00:06:18]:
Yeah, sure. Well now, so based on all those companies you just mentioned, and, and, and there could be more in there. Right. But based on those companies that you mentioned, you know, I, I've noticed that they've either, you know, gone public while you were there or had just come public right before you got there.

Nam Doan [00:06:35]:
Yeah.

Steve Swan [00:06:35]:
So, you know, which, which says a lot about, you know, where you position yourself or where others see. Right. But my question, I guess more specifically for our other IT leaders, you know, is where do you think IT sits? Where do you think, what do you think it's role is in the company that's either just gone or going public, you know, or, you know, because they tend to be in smaller, smaller sizes.

Nam Doan [00:07:00]:
Right.

Steve Swan [00:07:01]:
And.

Nam Doan [00:07:01]:
Yeah.

Steve Swan [00:07:02]:
What are your thoughts around that?

Nam Doan [00:07:03]:
Yes. So, you know, I strongly believe your IT leadership is very important. Right. And of course I would say that as an IT leader, but you know, I do think I have the experiences to demonstrate that. Right. And I was at two fast growing companies, Twist Bioscience and Sunrun, that became public while I was there. They needed a lot of IT leadership and I can dive into some details there. And then at Third Harmonic, little bit of a opposite experience where Third Harmonic went public very early with not many people at all, 22 people.

Nam Doan [00:07:42]:
No, no IT lead, no IT employees at that time. And. But then I was hired a few months after they went public and I was hired to build up the IT functions so that we could operate as a, as a public company company. So it's a little bit the opposite way around, but still kind of demonstrates the importance of strong IT leadership. And when I think about those experiences, Dave, there's two key areas I would call out where strong IT leadership is critical for companies that want to become public companies and that are growing rapidly. It's big business systems. Right. It's probably the more obvious one.

Nam Doan [00:08:25]:
But then also there's this compliance. Right. Particularly if you want to become a public company. If we look at business systems, and we kind of talked about this earlier, a strong IT Leader is going to make sure that they understand the business and they understand the business processes and they will make sure that the business system is implemented to drive those business processes. Right. And without strong IT leadership, you know, the result is often failed systems implementations. Right. And I've, I've seen many, plenty of those.

Nam Doan [00:09:05]:
And when I see those, it's very rarely because of the technology or the system itself. It's because the implementation is wrong. Right. The implementation did not meet the business requirements or the business requirements were not well understood.

Steve Swan [00:09:21]:
Right?

Nam Doan [00:09:22]:
Yeah.

Steve Swan [00:09:23]:
And you've had a long history of that going all the way back to your Oracle days, right?

Nam Doan [00:09:26]:
Yeah. Seen a number of poor implementations and when you have that, it takes you years to dig yourself out of it. And the most extreme example I have is one company implemented in the ERP system with no IT leadership, no IT oversight.

Steve Swan [00:09:46]:
Well, that's a big case for, you know, companies making sure they have, you know, folks inside representing their best interest, whether a full time or a fractional. Doesn't matter, Right. I'm not, not advocating either way on that, but you gotta have, you know, somebody in your corner, somebody on your team. You can't just, you know, they thought, they probably thought they were saving some money, right?

Nam Doan [00:10:07]:
Yeah. They outsourced it, Right. It's like, hey, we'll be fine. We'll outsource it to this systems implementation partner.

Steve Swan [00:10:13]:
And they're all the same, so we can spend less on this one. But guess what? They're not. You know, and you got to get the experts in and you gotta really, really find it. Well, so, you know, one of the things you, you mentioned throughout as we were chatting there a couple times, was compliance, right?

Nam Doan [00:10:27]:
Yep.

Steve Swan [00:10:28]:
And for fast growing companies, you know, and I hear this a lot from business and IT leaders, you know, compliance slows us down. We gotta check all the boxes, we gotta document everything.

Nam Doan [00:10:39]:
Yeah.

Steve Swan [00:10:39]:
How does you know from, from the IT side of things? How do you balance the speed of what where business wants to go versus the compliance and the fda? And I mean, it's, it's a, it's a push and pull all day long and you're kind of caught right in the middle, aren't you?

Nam Doan [00:10:54]:
Yeah, yeah. Quite a couple of things I want to say there. The first thing is, you know, if you want to become a public company, you have to deal with compliance, right? There's, there's no, no way around, so you have to deal with it. And if that's the case, then you should adopt those compliance controls as early as possible. Right. Make it a part of your day to day operations so that no one, so that people aren't having to think about it because it's how they operate. Right. It's already operate on the IT side.

Nam Doan [00:11:30]:
There's a set of controls that every public company needs to do. Right. You can't get around it. Right. Every single auditor will ask you about these controls around access management, change management and they're really best practices. So you should just do it as, implement those controls as early as you can. Train your IT staff, train your IT staff so it becomes part of their daily operations and they don't have to think about it. So that when it's time for the audit, you don't have to do anything extra or special because you're already doing it as your day to day operations.

Nam Doan [00:12:03]:
Right. You really don't want to do that audit in the first few months of you implementing that control because I guarantee you you will fail. Right.

Steve Swan [00:12:14]:
Because now what if it's a non public company? What if they're owned by a PE firm or if they just haven't gone public yet? Is it the same, would you say, or is it a little different?

Nam Doan [00:12:24]:
No, no, I would still say, you know, if you have line of sight and you, you know you're going to become, you want to become public or.

Steve Swan [00:12:32]:
Even have a little inkling. Let's start at the beginning, right?

Nam Doan [00:12:34]:
Yeah, yeah. And you know, these controls that I'm talking about, particularly on the IT side, they're best practices or leading practices, if you will. Right. And they're things that you should do anyway. Right. I mean, if it is going to make a change.

Steve Swan [00:12:49]:
Yeah.

Nam Doan [00:12:49]:
We should get approval from the business. Right? That makes sense.

Steve Swan [00:12:53]:
Yeah, yeah, yeah.

Nam Doan [00:12:55]:
So we should do that. And so the earlier you do it, the earlier it becomes a part of your daily operations and you don't have to think about it. But you know, to answer the other part of your question, it does, you know, even doing these controls, even as a daily part of part of your daily operations, it does, yeah. There is a balancing act between speed and, and compliance. Right. And you know, these IT controls are probably a softer version of that. But I have seen where on the one hand we had a compliance issue and on the other hand we had business speed. Right.

Nam Doan [00:13:37]:
And to solve the compliance issue, it was going to cost, you know, fairly significant business speed. And it, and it comes down to a business decision. Right. And it actually was an executive level decision. The CEO made the call and CEO made the call. We're going with business Speed. And so, you know, we were left with a compliance issue that we had to deal with.

Steve Swan [00:14:02]:
But that was known going in, that was an accepted risk that was made at the top level. And it's, if it comes up.

Nam Doan [00:14:07]:
Yeah. And it's, you know, it's not it's role to make that, that decision, but it is our role to identify the issue, shine a light on it and quantify it and say, hey, this compliance issue, here's the solution and here's the cost to business. Speed. Do we want to do it? What's our risk appetite? That can only be answered by the business.

Steve Swan [00:14:32]:
I hear a lot about that. Like when I, when I, I've recruited for a lot of cyber security roles and things, you know, and some of the leaders, you know, there's one company that's a huge company now, but back when I was working on some security roles for them, they were less than a thousand. Now there's, you know, 12, 15,000. There's a lot of people. But back then it was okay, Steve, we want somebody who sees and understands that gray area. We can't have somebody super black and white because like you said, it's going to slow us down too much and we're willing to accept that. Right. But some cyber leaders are like, no, no, no, no, no.

Steve Swan [00:15:07]:
I don't even play in the gray. I'm black and white and that's it. And to find their person for them was a long hard struggle because the, the security folks see the, the, the, the, the weight on their shoulder. It's their, it's their issue, not the CEOs or the CIOs. They see it as their issue. And, and there shouldn't be any grain. It should be all black and white. So some of them.

Steve Swan [00:15:32]:
But then you can find the ones that are like, you know what everything's got, it's, it's a cost risk balance. Right. The more you spend, the lower your risk. But you're gonna have to spend a lot if you want zero risk, you know.

Nam Doan [00:15:41]:
Yeah, yeah. It's, it's all depend on that risk assessment and what the risk appetite of the company is. And the risk appetite of the company will change over time. Right. As the company matures, their appetite for risk will probably become smaller. Right. You know, particularly if they want to become public or if they need to start dealing with the FDA, for example. Right.

Nam Doan [00:16:05]:
The, the FDA's risk tolerance is, is zero. Right. Is that they're dealing with patient health.

Steve Swan [00:16:12]:
We were just talking about this, right? It's the Same for us people, humans. The older we get, the more we got to lose, the less I'm going to be on my mountain bike jumping over stuff, right? You know, we got more to lose. So it's like, okay, I'm, I'm, I'm. I'm going down. I'm. I'm lowering my risk appetite, and I'm not gonna be doing crazy things on my mountain bike. When I was 25, maybe I would have. Right? But those days are long, long gone.

Steve Swan [00:16:34]:
That's an old. An old Steve Swan, not a new one.

Nam Doan [00:16:37]:
You know, we always have a better understanding of our limitations and, you know, perhaps that our bodies aren't as capable as they. They used to be.

Steve Swan [00:16:44]:
Right, Right. Yeah. My dad. My dad's 86. I think he still thinks he's 37. And it's like, dad, I mean, he's. He's shuffling, you know, and it's. He's.

Steve Swan [00:16:54]:
He thinks that he's pretty slick getting away from people and running away. He says running away sometimes when he's got to get. But he's not running anywhere. You know, it would take him three minutes to get out of a conference room. Anyway, so, you know, one. One question I like talking about, too, is, you know, as a company continues to grow. Right. You know what? You know, I always like asking folks, you know, some of.

Steve Swan [00:17:19]:
Some along the lines of, you know, what are you thinking about? Or what do you think now as your company grows. Right. Because you've been in that situation several times as your company continues to go, what are what. You know, if you're looking in the rearview mirror or after you grow, one or two or three, or in your case, several. Right. What do you know near the end that you didn't know at the beginning that you should have taken care of? Right. What are some of the things they. I'm a small company cio.

Steve Swan [00:17:43]:
It's my first time being in that seat. What would you tell me to do? What would you tell me to look for? What am I supposed to keep my eyes on?

Nam Doan [00:17:50]:
Yeah, no, this is a great question. And it's something that's not. Like you say, it's not obvious at the beginning. Right.

Steve Swan [00:17:58]:
But, yeah, I don't know anything.

Nam Doan [00:18:01]:
And it kind of always has. It really comes back to having good data. Right. And it's something that no one really thinks about when they first start out. Because when you first start out and you're standing up systems, right? They're not. People aren't thinking about data.

Steve Swan [00:18:19]:
You're just running, running Running?

Nam Doan [00:18:20]:
Yeah, they're running. And when they're running, all they're thinking about is transacting, right? All they think about is, hey, how do we transact? How do I pay that invoice? How do I process that order? Hey, I want to start a clinical trial, right? So they need to stand up systems so that they can transact. And it's kind of their way of, you know, it's a company's way of keeping score, right. The more we're transacting, the more we're doing. And so they want to see the scoreboard ticking over. As you grow, the company will begin asking questions that can only be answered by looking at your data. And that's when you're going to start looking at your data, and that's when you'll see if your systems were configured well to begin with. It's so that your data is structured well and your data is tagged appropriately so that you can analyze your data so that you can answer these questions that can only be answered with data.

Nam Doan [00:19:23]:
And so that realization generally only comes much later on when you have a lot of data and then you start trying to sift through it. It's like, oh, well, I can't sift through it because it's not tagged, it's not structured. So that's when you discover whether your systems are configured well or not and whether the systems are integrated properly. Right. And these are things that IT folks hopefully will bring to the table at the time you're implementing your systems, making sure the system is configured properly, that transactions, transactions are tagged properly so that you can analyze the transactions and you can, you know, you can slice and dice the data and, you know, we've talked in the past before, Steve, about, you know, with AI, today's AI, large learning models, right. If that data is trash, right. If it's not tagged appropriately or it's not structured properly, that AI model is going to have a really tough time making sense of that data.

Steve Swan [00:20:29]:
At best, a tough time. Right.

Nam Doan [00:20:31]:
So, so it's all coming back to the data again, right? It's like you've got to have good.

Steve Swan [00:20:36]:
Data now coming into an organization, right. A small organization that's growing.

Nam Doan [00:20:41]:
Yeah, Do.

Steve Swan [00:20:42]:
And, and let's say, you know, I'm a scientist, I just started a company. I don't, I don't know this stuff. Right. Do you find, and I'm sure it's different everywhere, but I'm just curious, do you find that they, the, the, the owners of a company, the leaders of a company, they Want to listen to their new IT guy talk about how we have to spend money on getting our data in shape for the future for something we don't even need today. Right. That's gotta be a tough sell, right? I would think, yeah. I mean, I'm just putting on my business leader hat. What would I want to hear? I wouldn't want to hear that because I'm like, oh no, do I really need that? You know, so just curious, is that going to struggle?

Nam Doan [00:21:20]:
You know, I, I don't think I have explicit conversations about good data versus bad data because it's, it's. And, and there are ways to have those conversations. But the way I position it is that you know, when you're configuring your business systems, right? When you're implementing your business systems, when it's configured properly, your transactions will be well formed and your data will be well formed, right. It's so you don't really have a conversation about the data it's baked into configuring your systems properly to meet your business requirements. And it'll come about during discussions about business requirements because let's say you're looking at a order management system and you're talking to yourself, sales leader. And he's, he's saying, well, you know, I, I want to be able to, you know, segment my orders by industry. Right? And it's like, okay, well what I'm thinking there is we got to have an attribute called industry that has all the different types of industries we sell to so that in future, you know, the, the sales leader can, can slice and dice by industry. Right.

Nam Doan [00:22:30]:
So I got to make sure that that attributes in the. Right. But if you create the system without that attribute, sales guy, he's not going to be able to, you know, slice and dice by industry. Right. And so it just kind of comes out as a part of understanding the business process and understanding the business requirements. And you know, that then leads to a well configured system that then leads to good data.

Steve Swan [00:22:56]:
Yeah. And like you just alluded to, good data is a prerequisite, an absolute bare minimum for making sure that your AI makes heads or tails of what you're asking it to make heads or tails of. Right? Because you put bad data, and I made the analogy a million times, you put bad gas in any great looking car, great looking motor, it's not going to run, it's not going to go, right?

Nam Doan [00:23:18]:
Yeah, bad data is really, really detrimental, right. And they, I've seen bad data at a number of places and you know, I liken bad data to like a slow poison, right? Because you know it's not going to stop you from transacting. You won't notice it for the first, you know, four years, three, four years, depending on how fast you're growing. You won't notice it. Right. You'll happily continue transacting. But then at some point you, you, it's going to start slowing down your business. And then even worse, you know, when you, when your business leaders start asking like the deeper questions that can only be answered by data, that's when you'll really understand.

Nam Doan [00:24:02]:
Oh, I have bad data.

Steve Swan [00:24:06]:
Yeah. Wow. Okay. I didn't realize it would take that long.

Nam Doan [00:24:10]:
Yeah, but I guess it doesn't stop you from bad data, doesn't stop you from transacting. Right?

Steve Swan [00:24:15]:
Right.

Nam Doan [00:24:15]:
And when I say bad data, I don't mean like completely incorrect transactions. I just mean like transactions that, yeah, they might be structured, okay, but they might not be structured in a way that will meet future requirements. They're structured, okay, so that you can transact, so you'll happily keep transacting, but then once you really want to analyze the data, the data will fall short and it's that type of bad data that will stop you from progressing to that next kind of analytical level.

Steve Swan [00:24:45]:
Well, let me ask you this, and this doesn't come up explicitly all the time, but I have had some conversations about this in the recent past. When we talk about data and we talk about good data and we talk about two and three years down the road. Some people who are deeper into the data field are telling me, you got, it's a living, breathing database. You need maintenance constantly on that data. But then there's others, Nam there's others that say, and these are cost conscious people say, cool, we got our database together, nobody touch it, we're good and let's go. Right. So I guess maybe that the question would be, can a good database or good data turn into bad data because of lack of maintenance over time?

Nam Doan [00:25:30]:
Yes, it can. Your requirements will change over time, Right? The way you want to look at your data will change over time. And sometimes your data set that you have doesn't have all the attributes that you need. And so you might have to go back and update your data. And there's this whole field around data curation, curating your data. And it's a continuous thing where you're curating your data so that your data gets better. Right. And people spend.

Nam Doan [00:26:04]:
There are roles for data governance, data curation, because your data starts like to your point, your Data is not static, right? You need to kind of curate your data to evolve it to meet your changing business needs. And you know, at the time when you implemented the system, you kind of understood what the requirements were at that time, and you configured the system to meet the requirements at that time. But your requirements might change over time. And then you do need to go back and curate the data, update it so that it can meet those new requirements. So you know, I'm with the other guy that said, yeah, you need to curate your data, right? And you know, maybe the AI large language model needs something extra to be able to hook into the data, right? And so you update your data, you run queries and you curate your data. And part of curation also is, you know, there's some transactions that are just bad, right? You kind of get rid of those.

Steve Swan [00:27:06]:
I had one, one person who joined a large to medium sized pharma company. He came from another company and he was more on the business side, but he had data scientists working for him, directors and associate directors of data science. And he calls me one day and he says, Steve, I got a problem. I said, what's your problem? He said, and again, new company. So he wasn't there to put the data together and stuff. He said, I'm spend, my directors and associate directors are spending a lot of time going in and triaging data when something's not working out. And I said, well, in my mind, you know, and this, this is me talking. So maybe this is, I'm forming a question here for you in my mind.

Steve Swan [00:27:42]:
What I said to him, my knee jerk reaction was, listen, like I, I pointed out, my daughter, for example, I said, you got data science kids coming out of school who've dealt with data, right? They've, they've helped companies get data and database together. If you get some sort of, I don't know, you call it almost minor leagues or whatever, where these kids would maintain your data for half the price or less of what you're going to spend on that director, associate director. Their mind's going to get numb in two years or a year and a half. So maybe you promise them again, you're going to move from here to here to here. And you know, you could keep that process going and you could keep your, your database live and curated and clean and the ontology all correct and all that stuff, as opposed to letting these bigger titled folks spending a lot of money to go back and triage data which they probably have no interest in doing anyway, you know. So does it make any sense to suggest that to somebody, what I did or does that not make any sense?

Nam Doan [00:28:38]:
No, it, it does make sense. Right. And you know, it's a good entry way to, to understanding data. What I would say there is, it's, you know, the data science guys, you know, they, they should supervise these entry level data folks because, you know, the data science guys will understand. Well, hopefully we'll have an understanding of what the business context and what the business requirement is so that they can direct, give, give direction to the data analysts, let's call them. Right. But then over time, the data analyst will also gain an understanding of why they're doing certain things and you know, what the business reason is and be able to connect, you know, business drivers with what they're doing to the data. Right.

Nam Doan [00:29:21]:
And then they can progress as well and hopefully become, you know, hopefully progress to a role where they have an understanding of the business and where they can talk to the business and have an understanding of that and take it back to what they're doing with the, with the data.

Steve Swan [00:29:37]:
So we, we've tackled, you know, the data coming into the systems, the data coming into AI. Maybe we'll back up a little bit further into AI itself. What, talk around AI. I mean, a lot of the folks that I have on my podcast really, you know, they've got varied opinions, they've got varied ideas, but, you know, I think they all agree about the data, that what we're talking about, we all have different angles on it, we talk about it, but AI itself for the biotech industry, you've seen some small companies, you've seen what they do with it, you've been on the enabling side of that. Right. What are your thoughts there for AI in our industry?

Nam Doan [00:30:12]:
First of all, the impact of AI in general on all businesses. I struggle to think of industries where we won't have an impact. I have no doubt there will be a big impact and it's happening as we speak. And I think the thing to keep in mind is that the progression isn't linear. The progression is kind of geometric. Right. And so things are changing very quickly. And I think in the tech world, it's particularly impactful in some specific business functions that really lend itself to large language models.

Nam Doan [00:30:57]:
And I know that the function that springs immediately to mind is actually customer support. Right. Because every company, most companies that have a customer support function, they have the customer support, has a good, well formed knowledge base. They've got their content well organized, it's tagged, that's perfect for pointing a large language model at it, consuming that knowledge base, contextualizing it, and then being able to answer questions about it and knowledgeably. And there's a lot of progress being made in customer support is one area. Then there's other areas where I see a bit of push and pull of. On one hand, there's the hey, rah, rah, AI is going to take over. And then the other hand, there's other people saying, hey, hang on, it's.

Nam Doan [00:31:49]:
It still needs supervision. And the one, the interesting one is, you know, the writing of code by AI bots, right. And. Or AI collaborators. You know, AI can definitely write code, but it definitely still needs to be tested and QA'd and integrated. And there's, you know, there's so much else that goes into writing code as a part of a bigger team. You know, we'll definitely accelerate the process and, you know, over time I think it'll get better. But I think Gartner just recently came out with a paper and they, you know, the headline was, yes, AI will accelerate code development, but it may take.

Nam Doan [00:32:34]:
But in the end, it may take you more developers than what you started off with without AI. But the message there, I haven't read the paper, but I think they were being delivered deliberately. Kind of headliney, catchy. And you know what they, I think what they mean there is that you'll still need people to qa, check it, test it, do all those things, and in the end you may end up with more developers, but you will, but your velocity will be higher, right? Yeah. So there's a lot of progress being made. What I think is really interesting is this, the concept of agentic AI bots, right, where these AI models have a framework where they can actually make decisions now based on what they learned, and they make decisions on their own and then they execute them. Right. So it's taking that knowledge to the next step where they make decisions and they execute them based on what they learned.

Nam Doan [00:33:35]:
That's. That's kind of really exciting and kind of really kind of a little bit scary for some, for some fields, right?

Steve Swan [00:33:44]:
Yeah, well, and that, that comes to the. One of the points that one of my CIOs made to me when he was talking about AI, you know that when AI is being QAed, one of the sort of processes that should get pushed through AI, especially, you know, the customer service bots and things like that is, you know, kind of put it through a test of being an employee. Right. So it doesn't. If it says something that's not good, that's like an employee. So you, as a company, you're on the hook, you know, and you've got to justify those answers, right? Otherwise you can't just say, well, I did it. Sorry. You know, that doesn't, that doesn't roll.

Steve Swan [00:34:26]:
That doesn't work. And that's one of the things that somebody brought up to me. And I thought it was pretty interesting, you know. So I guess that leads to sort of. My next thought is, how do you think that, you know, employees, right, in the workplace that are, that are at some of your companies, how do you think, what are your thoughts around how your company or companies should approach using AI in the workplace? I mean, should it just be, let's go, let's all beat it up. Should it be controlled there be, you know, how do you think this whole thing should be? Because everybody's got a different idea on this one right here.

Nam Doan [00:34:58]:
Yeah. And it's, you know, you see people on the whole spectrum. Right. But, you know, my opinion is that you need to embrace it. And I think the, the main reason why I say that is because it's already in your workplace. So you may, you know, you might want to stick your head in the sand and pretend, no, like, no one's using AI in my workplace. It's like, no, it's already in there, Right. It's there in your Microsoft Office 365.

Nam Doan [00:35:27]:
It's there in your Zoom. It's even in your Adobe Acrobat, right? When you open a document in Adobe Acrobat, Acrobat says, hey, would you like me to summarize your documents? Like, hey, I did ask you to scan my documents. You can try to turn that off, but you can turn it off, but then whenever it gets patched, it'll probably come back on. Right. It's almost impossible to hide yourself. And so you need to embrace it and put in, like, some guardrails. Right. And, you know, I think every company should have AI use in the workplace policy, right.

Nam Doan [00:36:03]:
So that people can understand how they can use AI. And there can be some really basic things, like, hey, don't input sensitive data or IP into any AI search boxes. Right. It could be simple as that. And it's, it depends on what industry you are and what your appetite for risk is and all those things. So definitely work with your legal counsel to develop that policy. But it should be a part of your employee handbook or whatever it is that all employees read. And then that can give you and the employees guardrails to how they can use AI and I guarantee that there are plenty of use cases where employees can gain use out of AI.

Nam Doan [00:36:54]:
Right. Whether it's in the workplace or whether it's on a website, maybe they can use Perplexity or there's so many tools out there now, you will find ways to be more efficient and you should embrace it because it's already there in your workplace.

Steve Swan [00:37:13]:
Yeah, yeah. I had one guy say to me, he's like, you know, I can, like you said, I can bury my head in the sand. I can pretend like no one's doing it or using it. But guess what? If I have it off my corporate machine here, they're over here using it, you know, it's here, they're going to use it. So why don't accept it? Why don't we accept it and why don't we come up with our policies, you know, and some even go as far, you know, some are on the fence on this. Right. They don't know whether they should have sort of programs to beat it up and use it. Let's keep hammering away on this because let's figure out what it can do and what it can't do and our best use cases for it.

Steve Swan [00:37:45]:
You know, some are like, oh, that's a little aggressive. Some talk about toggling on the learn switch. Right. Or talking off. You toggle it off, it's not going to save the data. I had another guy tell me that he was using AI once with, with a consultant about a year and a half ago. Up pop proprietary information of a competing company's drug.

Nam Doan [00:38:05]:
Right.

Steve Swan [00:38:05]:
He screenshotted it, got it over to that CIO and yeah, I guess they took care of it. But you know, I mean, that's, that's, that's the ultimate nightmare. Right. Right there for biotech.

Nam Doan [00:38:14]:
Yeah. And then that's why you want to have that AI use in the workplace policy to, to provide those key guardrails so that, you know, you can play with it safely.

Steve Swan [00:38:25]:
Right? Yeah. And if you're not, then you could put your company. Right. Right in the crosshairs of big, big risk. Right. Thank you very much. I always have one last question I ask of folks before I get to that, more on the personal level, but before I get to that, I think we've covered a lot of ground, right. On data and AI and all sorts of different things in your background.

Steve Swan [00:38:44]:
Right. And how you got to where you are. Anything else that you think we should cover as it pertains to technology, biotech, where we are, where we're heading, anything new that's coming. Your thoughts and feelings on anything along those lines that we haven't hit that you think is important for our listeners to think about or pay attention to.

Nam Doan [00:39:05]:
The only other one I would mention is cybersecurity, Steve. Cybersecurity now is. The risk from cybersecurity is very clear and present for, for any company now. Right. And that's why the SEC now requires all public companies to. To do certain things about cybersecurity. Right. Because, you know, it's a risk to any public company and it's a risk to any company.

Nam Doan [00:39:31]:
So, you know, make sure you're taking care of some really foundational key cybersecurity security controls that are frankly pretty cheap to implement. Right. Some basic things like cybersecurity training, you know, a good laptop, good laptop protection, single sign on with mfa. And these little things will make you so much safer. And everyone is at risk from cybersecurity risk. And so just keep that in mind. I think I saw a stat saying smaller companies are at higher risk than bigger companies because the hackers know that the smaller guys think they're not targets, but they are. These hackers, they're motivated by all sorts of different things.

Steve Swan [00:40:20]:
It's a lot. I mean, I hear these companies, even the small ones, you know, they've gone again exponentially, you know, the billions of, of attempts on their, on their, you know, to hack them over a year's time frame. Billions.

Nam Doan [00:40:32]:
Yeah.

Steve Swan [00:40:33]:
I mean, it's just crazy. I mean, you know, and these hacks.

Nam Doan [00:40:36]:
Are getting more and more sophisticated. Right, right. You know, you want to hear a crazy hack story?

Steve Swan [00:40:42]:
I'd love it. Yeah, go.

Nam Doan [00:40:43]:
So have you heard of deep fakes AI? Deep fakes, right, the video or audio AI simulations of people. And so this didn't get much coverage a couple of years ago, but happened a couple of years ago in Hong Kong. An analyst working for a financial institution in Hong Kong gets an email from his CFO saying, hey, big emergency. Gotta jump on a zoom. We will talk about the. The emergency. The guy jumps on the Zoom and there's his cfo and he sees some other C Suite folks on the Zoom and they're discussing this emergency, and the end result is they turn to him, say, hey, you got to wire. I think it's 23 million, by the way.

Nam Doan [00:41:33]:
23 million to this account. And he's like, what? And you know, the other execs are saying, yeah, yeah, you gotta do it. It's a big emergency. So he does it, and then later on finds out that each of the C suite on the Zoom is a deep fake imitation. Right. And, you know, I haven't seen too much details of whether it was like a live simulation or whether it was just recordings that these hackers were playing, you know, at the right time so that it looked like they were having a conversation. I think it's live. It's a recording rather than live, deep fake simulations, because I don't think we're.

Nam Doan [00:42:18]:
We're quite there yet, but I. I can imagine we, we will get there pretty soon. Right. But you know, it was 23 mil, right? I'm pretty sure it was 23 mil, so.

Steve Swan [00:42:29]:
Wow, that's crazy. So, you know, it's gone. Nothing you can do. It's over.

Nam Doan [00:42:35]:
Yeah.

Steve Swan [00:42:36]:
So, you know, you did it, you did it voluntarily. It's not like someone stole that from. You sent it.

Nam Doan [00:42:41]:
Yeah.

Steve Swan [00:42:41]:
Right. And then an insurance company's gonna say, well, how do we know, you know? I mean, you didn't send them when, you know, we're not reimbursing that, you know. Wow, that's crazy. That's a good story, though. Well, thank you for sharing that one. The last question that I like asking folks, like I said, it's more along the personal lines. It has to do with music. So I like music.

Steve Swan [00:43:04]:
Yeah, we go and see a lot of concerts. There's a lot of different bands we see. So I like asking every guest about any live performance they've ever seen and what their number if. If you've seen live music. Some. Some haven't. Right. But what would be your favorite live band or live performance or live concert you've ever been to in your entire life? What would you say? That's a tough one, huh?

Nam Doan [00:43:31]:
Yeah, yeah.

Steve Swan [00:43:31]:
Do you go to a lot of live music or have you.

Nam Doan [00:43:34]:
Not a ton, but, you know, I've been to a number of them. I think it's between two.

Steve Swan [00:43:40]:
That's fine. Yeah.

Nam Doan [00:43:41]:
Between two to two performances for different reasons. One is U2. I love U2. And, you know, they've been around for so long, produce so much good music. And, you know, I think the, the heyday was when I was really into music, right in the 80s and 90s, so. So definitely one of my favorite acts. And I. I really regret not being able to see them when they played at the.

Nam Doan [00:44:07]:
The Sphere, right. In Las Vegas.

Steve Swan [00:44:09]:
I wanted to go out to that. No, I didn't make it, but by.

Nam Doan [00:44:12]:
The time I heard about it, I was looking at ticket prices. I go, no.

Steve Swan [00:44:17]:
And then you gotta fly. And then you Gotta stay and then you gotta do all that. Yeah, I agree.

Nam Doan [00:44:20]:
But then the other experience is kind of, you know, a little bit opposite end of the spectrum, where it was kind of like a small venue. The great performance was Lenny Kravitz. Right. And you kind of forget how much material Lenny Kravitz has, but he, he's been around for a long time, too, so.

Steve Swan [00:44:36]:
Yeah, so loved that performance.

Nam Doan [00:44:38]:
And it was, you know, a small setting. It was at the Bill Graham center in San Francisco. And, you know, we were right there. If you could see Lenny. Yeah, so I'll always remember that.

Steve Swan [00:44:48]:
That was my wife just saw him in. Where was she? Jazz Fest down in New Orleans.

Nam Doan [00:44:52]:
Oh, right. Okay.

Steve Swan [00:44:54]:
She said it was great. Yeah, she went down for. Not this past weekend, the weekend before. She was down there. Yeah, we like seeing a lot of live music, but. Yeah, you too. One of those. I remember we were in my, my, my.

Steve Swan [00:45:06]:
One of my best friends and I, we were in ninth grade and I was living in. I grew up in Rhode island, and 9th grade we went into Prof. There's only two places to go in Rhode Island, Providence or Newport. And so it was spring, so it was too cold to go to Newport. So we went to Providence one day. It was a, I don't know, Saturday, Sunday, and we went to Brown University, which is right there in the middle of the middle of the city. And we were hanging around with a bunch of college kids and they were having their college week and their band that showed up for their college week. This is 82, 83.

Steve Swan [00:45:42]:
Anyway, was you too. Oh, and tickets. Tickets were like 15 or 12 bucks. We didn't have the money. We were like $8 short to buy two tickets, so we didn't get in, you know, and that would have been awesome.

Nam Doan [00:45:55]:
That would have been great.

Steve Swan [00:45:56]:
You know, live and learn, I guess. Ninth grade, carry more money, you know, if I have to go back and talk to myself. So. Well, listen, Nam, thank you very much. That was, that was awesome. And to our listeners, if you liked what you saw and what you heard here, go to Spotify, Apple, YouTube, like us. Subscribe to Biotech Bites and get in touch and let us know what your thoughts are. Nam, I appreciate your time.

Steve Swan [00:46:21]:
Thank you very much.

Nam Doan [00:46:22]:
Hey, thank you, Steve.