Do you want to be a more effective statistician?
Do you have what it takes to be an effective leader in your organisation?
Many people think that strong leadership skills are something that statisticians don’t have. However, as a statistician, you definitely have a lot to offer an organization. Statisticians often have the skillset required for leadership positions as they can use their questioning and decision-making abilities to help solve issues within an organization.
In this webinar, we will discuss how statisticians are in fact very gifted with unique leadership skills that can make them more influential and successful. We identify these skills and discuss how you can leverage them to become a more influential and effective statistician.
Listen to this episode now and learn from the following points we discuss:
- Statisticians are trained to be leaders and have many of the skills required for leadership positions
- Statisticians can use their problem-solving, questioning, and decision-making skills to help resolve issues within an organisation
- An example of how a statistician’s leadership skill can be used when there is a case in manufacturing and how looking at data from all stages of production helped identify where the problem was occurring so it could be fixed
- Do not underestimate the leadership skill set statisticians have – you may be able to influence and lead projects more than you think!
Alexander: Welcome to this webinar today about our potential characteristics of exceptional leaders that we all have, but we are may not be really aware about. And talking about leaders. This one case that really comes to mind, and this is this guy here, if you turn to the next slide. Steve Jobs is a really, really exceptional leader, or was an exceptional leader, but he didn’t start at outset way.
He had lots of struggles through his career. And if you read his biography, you can see where he struggled. He had surely had certain shortcomings, but he also had really great examples of leadership. And one of his things was to really understand a problem, to dive deep into it, to analyze it.
To understand it and to come up with a solution. He faced a really big problem when, in 1997, he returned to his company that he was previously being kicked out, which, you can say, oh, can you be kicked out of your own company? But, well, that happens to publicly traded companies. So he returned to Apple and what we found there was a really, really big mess.
The company was bleeding money quite a lot. When he was in the year. He entered just in this financial year, they lost more than $1 billion. So there it was really a big mess. There was a lot of distrusts. The stock price went down. It was really a disaster. And so he went in there and talked to lots of different people.
He wanted to understand what’s really going on in the company. So he did his research like we would do as statistician. And he saw one of the core problems of the company was they had too many products. Yeah. So for example, they had dozens of versions of the Mac. Yeah. For different retailers. They had different versions and dozens of that.
And of course with that comes to lots of bureau. You need to maintain them, you need to track them, you need to, manufacture all of these different things. All, array of additional things you need to do for each of these versions. And one of the questions he was asking was which of these, all these products do I tell my friends to actually buy?
And he couldn’t get an answer of that. So that’s when he, started to actually reduce the number of products, whereas his predecessor has increased it and more and more products, new product lines all the way. And he said, you shouldn’t be working on all these crappy products with your productive time.
Yeah. He saw that, having no focus basically means that you just develop lots of crap and finally at one meeting where they still were going for more and more is said, stop. We’ll have just four different things. And he has this diagram of products for either professionals or consumers and for desktop and portable.
And so in this four quadrants, he said, make one great product in each of these four quadrants, sets the job. So he understood what was the problem, far too many products. And this was had lots of different consequences across the company and he solved it. He, of course, as a CEO could, also tell people what to do, but he really understood the problem.
By getting them rid of all the printers, of all the servers, even killing the Newton, which is, very similar to this. This thing here, what I have here. He could return the company to profit and next time year when he gave his keynote, he ended with, oh, and one more thing, kind of his typical thing, which he would, use in every presentation there often said, think profit.
He turned the company from 1 billion lost in 1979, 1997, 1 year later to 300 million in profit. Largely due to understanding the problem, analyzing the problem, what really wast trigger, and then changing this leadership action. If you don’t know me, , I’m Alexander Schact, I’m running the Effective Statistician Podcast.
We have nearly 200 episodes now. We are running the effective statistian leadership training. I have 20 years of experience in the healthcare industry and we’re both on the farmer side. And now I’m you on the CRO side. And I really have a passion for leadership because I think whenever you wanna change something, whenever you wanna get something new done, whenever you wanna have others act on your ideas, you need to have these leadership skills. Who’s that? I hand over to Gary.
Gary: Thanks Alexander. I’m Gary Sullivan. I’m the one of the developers and the lead facilitator for the Effective Statistician Leadership Program. I retired from Eli Lil Company in 2017, and as, as just as Alexander very passionate about leadership. As a matter of fact, when I retired my sole focus was just starting a company to be able to do leadership training and leadership advising.
So I’ve been doing this type of thing for over 10 years. Started back at Lilly when I developed a program there. And has led to lots of trainings. Probably trained over 500 people over those years and the development of what Alexander and I now have as our online leadership training class.
So, thanks everyone for joining us. We will do Q & A at some point in the webinar, but feel free to type your questions into the chat box as we go. As we see them, we’ll pick them up and try to answer them at least best we can. So this notion that Alexander talked about in terms of, the leadership that statisticians can have, And I wanted to talk about, or just highlight some of the things he talked about in that Steve Jobs example.
And these are some of the skills that effective leaders have. They have the ability to understand and solve problems. And I’m not talking about statistical leaders, I’m talking about leaders at every level. We’ll talk about some examples as we go, but this is one thing that’s sort of core to leading, whether you’re a technical leader, assigned leader, an emergent leader is the ability to solve problems.
Leaders ask questions and are curious. Again, over my experience the best leaders now some, not all, but the best leaders, those that keep rising in the ranks and really deliver value to the organizations are the ones that are curious and ask question. Leaders behave with objectivity.
Again, there are some out there that will play politics and such, but those that really want to do the best thing for their customers and the pharmaceutical industry, for their patients, for their business partners, for their organization, for their employees, are ones that behave with objectivity.
They’re going to take a step back, look at a situation, and do what’s best for all their stakeholders. And then finally, leaders make sound decisions. Whether you are the decision maker or even if you’re someone that wants to bring ideas forward, you have to make decisions about this is the idea that I’m gonna push.
This is the improvement that I’m gonna pursue. Here’s the direction that I’m gonna take this project in. So leaders, effective leaders have all these skills. And I would ask you, does this sound like someone you. And if you think about it, it is statisticians. Statisticians tend to have all these skills.
We’re trained with these skills. We’re trained to solve problems, we’re trained to ask questions. We are trained to be objective and act with integrity. And we’re trained to make decisions now, not necessarily at the highest levels of a business and organization, but we’re trained to do this around data.
So we’re gonna talk more about this notion and go deeper into this idea that statisticians have leadership strengths that just come along with their training. So what we’re gonna cover today is the following. We’re gonna emphasize the skills that provided advantage for us as statisticians and being leaders.
So this doesn’t necessarily mean, you know, everyone getting to the level of a Steve Jobs, but just being able to lead projects, but just being able to influence and maybe elevating yourself to an assigned leader role. We’ll share experiences and examples that support this. All the way from really basic types of examples maybe to, to more business type examples and problems that we’ve solved or we’ve seen other people solve.
And then finally, we’ll provide three key takeaways when it comes to statisticians and leadership. So we’ll let you leave here with three things that we want you to remember about this talk and take forward with this. And so I wanna start with the first key takeaway, which is don’t underestimate the leadership skill set you have as a statistician.
I think a lot of times statisticians feel like, well, I’m not a leader, I’m just trained to be a statistician. But as we go into some of these examples, you’ll see that you actually have a skill set. As I alluded to some of those skills already, it’s very foundational to, to what leaders have and develop.
So I wanna share an experience for my time as a technical statistician. This goes back probably almost 30 years. I was with Eli Lilly for just a couple years at the time, and I was working on a project in product manufacturing and development, and we were getting ready to, we were actually scaling up a product that we thought was gonna go to market that actually didn’t, but we were getting it to the point where we could make it at manufacturing scale so that we could produce the drug at a level to meet the market demands.
And this was a tablet formulation. And when we scaled it up, we actually ran it through the larger scale process and automate to tablet production and then assay those tablets and found that there, it was much more variability between the potency of those tablets than we had anticipated. So this became a big problem because we were operating against a timeline and we needed to solve this problem quickly.
When the team leader kind of came in and said, okay, we’ve got this variability issue. We talked about a couple different ideas and then he said let’s go away and then we’ll come back in a couple days and see if we can resolve this or get some ideas together as to how to resolve it.
And so after that meeting, I went to a technician that worked on the team. There were a number of scientists, some PhD level, some bachelor’s level that worked on the team. And I went to a technician and I said, don’t we collect data all along the different stages of this mixing and formulation? Cuz there were about four or five different stages to this process where they would mix ingredients, add some other ingredients, do different types of mixing, and then ultimately formulate these, this mix into tablets.
And the person said, yeah. And I said can we get that data? So I got data from these five different stages of the process and it was just samples from some of the powders or some of the granules. And then they would assay them for variability. And I was able to get data from all the stages, and I was able to quickly see that the variability jumped up between stage three and stage four.
So after the fourth stage of manufacturing, I think, which was before the final stage, the variability jumped from about, 2% potency between tablets to up close to 10%. And so I took this data into the meeting a couple days later. And when the leader and there were more people there, because this was, like I said, an issue with a product that we thought was gonna launch.
And so there were a lot of people in the room and the leader said, Hey, does anyone, what do we think? What are some of the ideas we have to solve this? And I thought, well, for sure someone would’ve pulled this data together and looked at it. was a stats 1 0 1 type of calculation.
Just I looked at some histograms, some means and some variances. And I was early in my career and, an introvert not wanting to speak up too much, but I waited and no one really had any great ideas. So I finally offered, and I said I did some analysis of the data from the various mixing stages.
And I put up a plot that showed the variability jumped up, you know, from stage three to stage four, and everyone was like, oh that’s fantastic. This is a great analysis. So that’s where the problem is. So let’s focus on that. So I think the issue was we were either under mixing or over mixing there, but the problem got solved and it got moved forward.
But I remember leaving that meeting thinking like, I would’ve thought that there were, at least most of the scientists in the room had the problem solving skills to address that issue. And I was really surprised that no one else had gone digging for that data and done that simple analysis. I mean, maybe a little bit disappointed, but just surprised.
But it was the first step in me realizing that maybe my skill set is more unique than I give it credit for. Maybe there are fewer people that can actually do the types of things that I knew do, not just as a skilled technical statistician, but these skills around problem solving. And so I came away with this appreciation for, I’ve got some good problem solving skills that a lot of people don’t have.
I’m skilled at being able to synthesize data, looking for different sources of data to pull it together to try to solve that problem. And then I am very objective in how I approach things. I don’t approach them with necessarily a. I’m just looking to solve the problem in a way that’s going to move the product forward.
And as, time went by, this resonated with me more and more. I became more confident in, hey, these skills that I have aren’t skills that other people have. And I became applying, began applying them in ways beyond just completing statistical tasks, but looking at business problems, operational issues more strategic type of thinking.
And we’ll talk about some, those examples as we go. But the first, like I said, the first emphasis of the key takeaway here that, I, looking back, it’s like my leadership skill set as a statistician. I had a good foundation from the get-go just based on my training. Okay, so let me turn it over to Alexander and he’s gonna talk about the second key takeaway here.
Alexander: This is a really nice example because this is from one of the people that went through our leadership program. And so we were asking people to look out and work with their key partners that they’re working with. So biologists, the chemists, the medics, the safety physician, whoever you know is their most important partner.
And this statistician was working on safety analysis, and he was producing usually lots of Excel spreadsheets actually with, from these different early phase. Studies to give them to the safety physician. And that what was happening on a repeat basis, the, every time a new dose escalation needed to happen he would run these different a Excel spreadsheets and then would send it to the physician.
So after the, our training or this task that we gave people, you went to safety physician and says, actually, I keep sending you these Excel spreadsheets. What are you doing with it? And the physician said I’m looking through these and kind of, ball parking, is it safe? Is there any kind of tolerability issues?
How are all the different lap meters looking? How is, everything going? Is it safe to go to the next dose? And usually it takes me about a day to go through all this different data because, it’s lots of by patient data for lots of different endpoints and using, he was saying, wow, that’s a quite tedious task.
Yeah. So he says a physician that is truly not on the lower end of the of the pace spectrum. And he spends regularly a bay digging through these Excel spreadsheets and he said, Hmm, interesting. I think I can help you to do that more effectively. So he went back to his desk understanding kind of what were exactly the topics the physician was looking for and created interactive data visualization.
So where he could, that he could then give to the physician and then the physician would have, instead of having lots of lines, basically would directly see how the lap perimeter are changing for the individual patient and so on, and how individual patient looks compared to all the other patients.
And so all these kind of different things, and he showed it to the physician some days later and said, how about this? And the physician was, over the top with it because it saved him instead of working, going through all these lines looking at. Kind of 10 minutes or something like this.
And then he would understand the data. He would see any outliers, any kind of weird trends and could make a decision. And that was then even later replicated for lots of other physicians, for other studies so that all the different colleagues could do this. Now, that 30 minute meeting said he spent with this safety physician, resulted truly in hundreds of thousands of dollars of saving.
If you look, think about it, how many time different highly paid physicians work on this? They die out in a bigger company, and it was a bigger company that accumulates. So what is the learning there? The first is be curious. Just kind of not do the assigned task to provide the actual strategy. We have always done it that way, understand what’s going in there, because by understanding this, you can really understand what is really the value that you’re creating.
The next point, it’s not about providing data, it’s about synthesizing information. The key is not to, you know, give the data. The key is to do something with the data, and that leads to the last point. It’s not about statistical problems. There are always these underlying business problems. It’s the problems of the different peoples that you solve.
And if you can get set, done. Then you have really leadership, then you build credibility, then you become a go-to guy, then you become someone that solves problems and not just a statistical service provider. And with that, I hand over back to Gary.
Gary: Yeah. The, I don’t know if Alexander said this, but one of the comments the physician made in this problem was that he said, I need to share this with our team of physicians.
So it wasn’t just that physician, but then the solution in a sense was replicated, shared with other physicians who, again, saved probably hours and hours of their time and lots of money. Yeah. The, these three points very important. And again it’s sort of that step from a statistician going from just a support person, a service provider, to someone who’s more a collaborator.
Just thinking beyond the statistical task. I remember teaching a student, I think it might have been one of the ASA or JSM courses, and he was sharing with me, it was a university professor, and he said that he got called into the dean’s office once because the dean was asking him, he said, Hey, we got all this evaluation and market data and rankings of our university and our college.
And the dean was kind of concerned about their where they were ranked amongst other colleges. And they had provided the dean with this data this company did. And the dean didn’t really understand the data, so he called the statistician in, or maybe called the chair and set the statistician over and was explained to him, some of the percentiles or I don’t know if they were showing the multi-varied plots or something.
And when the person finished telling me the story, I said, so what did you do? How did you take advantage of? He said just answered the questions and then I left. And I almost kind of fell over. I said, that was a great opportunity. You could have asked him, Hey, where did this data come from?
Can I get the source data? Can I talk to the person that generated the information? And I said, it would’ve been a great opportunity to get more involved to understand the types of questions they were asking, maybe the criteria they were evaluating on. And then maybe you could have worked with the dean or a team to try to affect change in those areas where you had low rankings.
So it’s that notion of, beyond the question that you’re being asked or the tasks that you’re being asked to do and apply your skills of curiosity and problem solving and synthesizing information more broadly. And I guarantee you’ll have success when you do that because like I said, not everyone has the skills that you.
So the last point here is to add to your skillset to become a better leader. And this subtitle here, vice Presidents, executives and owners. So I wanna share just some anecdotes and learning here that I’ve known many people within Lilly over my years and come to know many people through some of the leadership training I’ve done for the American St Statistical Association.
So this goes to people that were vice presidents in their company, within a function or outside the statistics function. People that were executives that managed to rise themselves to a level beyond maybe being the vice president of statistics, but maybe they became the chief information officer or the dean of a college, or even the president of the company.
And I wanna share a quick story from this owner standpoint. There was a statistician that I knew back at Lilly, this is probably 15 or so years ago, maybe more than that. And it was clinical statistician by the name of Collet Za. So CLE was working in the oncology group and he was working on a product called Olya, which actually was marketed as a a treatment for non-small cell lung cancer.
And that at one point they were really ready to kill the project. It was late in phase three and there was a toxicity issue that they couldn’t solve and Colette was the statistician. And he said, give me two or three weeks to pour through the data cuz I think I have some ideas and might be able to come up with a solution.
So again, this is a statistician, this isn’t a scientist. And what he found over those two weeks when he met with the lead physician and the product team leader, he said, I think if we apply a vitamin supplement, what I’ve noticed in digging through the date is that people that have are high in these vitamins, I think it was B12 and folic acid or that have the right levels of those aren’t having the toxicity issue that others have.
And so they ran some quick studies and sure enough found that solved the toxicity issue. Oly was launched. It was a multibillion dollar drug over years saved and affected many patients lives. But it just goes to show you that it was the statistician again, as the problem solver, as the one that was curious, as the one that was looking objectively to try to solve the problem that saved the drug.
Okay? Now the story goes beyond that because quite left Lily shortly after that, and I lost track of him. But then I don’t know if it, I was doing a search or looking on LinkedIn or just saw an article come across and he had started his own pharmaceutical company called Lee Pharmaceuticals.
I don’t know how big this company is, but obviously Cle took his skills as a statistician. He built on those skills to become a good. Just business problem solver, to understand business strategy, to become a good communicator to become a strategic thinker, and got all the way to the point where I’m going to start my own company.
And obviously got business investors other people that were interested and supportive. But it’s just one example. And again, I’m not saying that every one of you, should or will become your own owner of a business. But nonetheless, that statisticians have these skills, again, that give them a good head, start, a good foundation, but by adding to these skills, and as I think about other leaders that have ascended to levels of vice presidents and executives, those were the ones that really focused on developing their leadership skills, developing their understanding of the business, developing their ability to communicate, developing their ability to negotiate.
That then ascend those levels and still use their foundational statistical skills of problem solving and being objective and integrity to solve business problems, to show themselves that they can deliver value just beyond being a statistician. So I would say the sky in some ways is the limit.
But these were people that, again, knew that they had to improve in areas beyond just the problem solving skills like communication and business acumen. They understood that it’s a data driven world, okay? And that they needed to take advantage of that so that their skills as a statistician could give them opportunities simply by being able to be the expert around the data, being able to synthesize the information and help draw conclusions could deliver value to the organization beyond some statistical tasks they were doing on a team.
And again, remember, leading is about problem solving. One last quick story. When I took my first management role back in 2002, I think it was, I was the statistician for the bio process development group. And I remember my manager at the time he said, good luck, he said, I want you to come back.
Let’s schedule a meeting a year out, and I want you to come back and we’re gonna talk about your experiences as a manager. And so a year went by, I was in this, my first management role, and I came back and I sat down with him. He said, so what do you think? How’s everything going? And I said, it’s just solving problems.
And he said, that’s exactly right. He said, management is about solving problems. It’s about solving business problems, solving people problems, solving project problems. It’s about solving problems. And because we as statisticians are, I would say, experts in solving problems, it gives us a leg up on any type of a leadership role.
So in summary, as we said you’ve got the foundational leadership skills, you’ve got the skills around problem solving, synthesizing information and data, being objective, having integrity, all those things. So use them in more than just technical situations. Again, if you’re a statistician, that’s just starting out, by all means, yes, apply those in technical statistical problems.
But as, as you develop and gain experience, look beyond that and apply those skills more broadly. And then develop complimentary skills to expand your opportunities in impact. Because like I said, as you start to develop those skills around understanding the business better, communication skills, which is something that we are often lacking as well as things like negotiation skills and strategic thinking, you’ll get to a point where you’ll see, wow, I can have impact.
Beyond my impact being a statistician on a team and have impact in ways that can really deliver value to my organization.
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