The Power of Asking the Right Questions with Dr. Sue Tripathi

Hey everybody. Welcome to another episode of the Making Better Decisions podcast. Today’s guest is a thought leader in day out. All right. Today’s guest is a thought leader in data AI and transformation has worked in government, healthcare, tech, and consulting as a PhD in healthcare and statistics.

Her passion lies in data and AI, but she is also committed to DEI mentoring women in data and nonprofit work. She spearheads the data technology and transformation practice, guiding enterprises through cloud migration, [00:01:00] complexities, AI, and ML model implementations, and data, and. And data center optimizations.

Please welcome Dr. Sue Tripathi. Hey, Sue. How are you?

Sue: Hey, thank you. Thank you.

Ryan: I’m super excited to have you on today and I want to dive right in. So what is one thing you wish more people knew about using data to make better decisions?

Sue: I think, the one thing I wish people knew is to know the right sets of data they should be using. Because I think there’s a lot of data around people. some of them are, you know, in your face data, data that’s just sitting in front of you. Some data is very relevant data, but you’re not looking at that relevant data.

Some data is just sitting underneath layers of data. So knowing what sets of data are important to you and for what purpose is actually far more important than actually going and looking for the [00:02:00] data. So understanding why You’re looking for certain types of data and mapping that out conceptually first with your team members or just by yourself with your colleagues, if you’re a leader and saying, what is the outcome that you’re trying to see?

What is the problem that you’re trying to solve? What is the revenue that you’re trying to reach? What is the target that you’re trying to, um, uh, again, reach? What is the client or the, um, purpose of it that you’re, the client problem that you’re trying to address? So mapping all, all these, what we call goal setting.

You know, it’s very, very important. These are elements that are extremely important. Once you know that, then you can drill into what are the different types of data that are going to be very relevant to you for that, to address those different types of problems. I think the other part of it is Not to get too clouded and try to address all the different types of problems, you know, keep it [00:03:00] simple.

Try and just address one or two key things that will help you drive, um, the, uh, to that revenue. If you are, uh, indeed in a for profit world, um, even in the non profit world, you’re trying to address certain outcomes, right? So just keep it very simple. Don’t try to ball the ocean in one go. And in that, if you’re trying to keep it simple, you’ll really try to drill down to one or two key data sets that will help you address that problem.

So to me, I think before you start looking and delving into and thinking that you have the right set of data, you really don’t know which data you should be looking at. And maybe historically you’ve been looking at a set of data, That only gives you partial insight, and you’ve been acting on that set of data, and therefore you’ve been getting partial results, and therefore driving only partial revenues that only helps you get to those partial targets, and only helps you get to partial revenues.

So there’s a [00:04:00] level of then a lot of frustration, right? So I think, again, keep it simple, um, address the issue. Keep it very, you know, if, if you’re, if you haven’t done this before, I would say pilot test it. Go small and then go bigger, but don’t go too big. And then, you know, try and scale it up. And when you feel very confident about the validity, the reliability, then scale it up.

And even then, keep testing it and continuously improve. The quality and the reliability of the data that you have.

Ryan: Yeah. I mean, one of the things that was super clear. When you were talking through those points was this, you know, point that we talk about quite a bit. Is data and everything that we use to interact with it is just a set of tools for helping an organization to achieve its goals. As you mentioned, whether it’s for profit or not, [00:05:00] whether I’m looking at, you know, donor analytics for a nonprofit or whether I’m digging and trying to make a different business decision, everything really comes back to the goals.

of that organization and being able to tie whatever you’re doing back to those goals, whether it’s, it’s revenue or, you know, expanding into a new market or cost control or headcount or like any of these, these things. So one of the things that. You know, that, that sounds really simple, but one of the things that I think folks sometimes express difficulty in putting into practice is, okay, well, how do I, you know, think about taking company goals and then translating that into some sort of data task?

Sue: That’s a very good question. You know, goals should be metric driven. Um, most companies, small, medium or large, know about SMART, right? Um, and SMART is about accountability, responsibility, [00:06:00] it’s results oriented. It’s very metric driven. Um, you can have short term, intermediate, and long term, you know, objectives.

Objectives and goals are different. Um, understanding what your goals are, but then figuring out what your objectives are, that would help you get to those goals. Objectives are typically, uh, much more defined. They’re much more structured, and they’re much more time driven. Um, and they’re very metric driven.

So I think those are easily, um, conceptualized. I think when you have what I call pie in the sky targets as part of your goals. Let’s have 40 million and your base is actually you’re at 1 million. And you’re putting 40 million as a target in quarter one. And then if you look at your target, you really have only 20 viable clients that you really have [00:07:00] relationships.

And the remaining, say, 30 are clients that you don’t really have any relationships, you’re opening up doors. Now, this is not rocket science because opening up doors takes time and whether it’s a two million client or whether it’s a ten million client, remember that your competitors are also vying for that same revenue, right?

And relationships matter and clients at the other end are frankly sometimes tired of just sharing from multiple different vendors. So you have to be cognizant about many different things. So I go back to, is this really a viable target? Why are we going from one to 40? Why don’t we go from one to 10 or one to four or one to five?

Something that’s really reachable, something that really is not going to frustrate a bunch of, um, account managers or, um, Client relationship managers or directors and so on and so forth, right? And then [00:08:00] you start building out. Now, uh, being aggressive is the name of the game, but being so aggressive that it’s so out of reach is actually, um, It’s not a good look.

Let’s just put it that way and being very polite here. So that’s one. I think the other thing is When you are setting up goals and trying to convert that into data, you’ve got to ask what types of data are you looking at? Are you only looking at financial data? Or are you also looking at data that’s quality data?

Are you looking at data that’s human development data? The different types of data. That’s qualitative data. Now, if you’re in a for profit business, typically everybody looks at financial data because everything is revenue driven. For But you know, there’s a lot of revenue and financial data also if you improve the quality of your data.

And, um, what I mean by that is if you say that the quality of the data, we can [00:09:00] improve and reduce your risk by, Uh, improving the quality of your data for a client. Um, we can improve the compliance, um, and improve your regulatory compliance for, you know, client A, B and C by just looking at the quality of your data.

That itself becomes a revenue generating proposition. So you’re addressing actually two different metrics there, right? You can also train people. Now, if you just look at AI in the U. S., for example, We have, we are already moving into a high skilled workforce. And so if you look at certain industries like food, beverage, there’s other industries, they are stalled industries.

What we mean by that is there’s, there’s growth, but that is not necessarily where, what we mean by High wage, high income earners. When we want to invest in AI, we’re looking at STEM programs. We’re looking at high tech programs. We’re looking at healthcare industries. [00:10:00] We are looking at tech industries.

This is where we are looking at the future workforce for AI. This is where we need to have people of all ages, those who are currently, and then for the future, right? So when you’re preparing for those kinds of things, where is that human development training that we need to engage our current workforce and we need to train them?

So maybe there are companies who can generate revenues that way, by upskilling their current employees, or by getting people who are part of the workforce. You know, their training workforce and generate revenues that way. You see, there are multiple ways in which you can still get to your revenue, if you’re a for profit, and still reach multiple different targets.

And for that, you need different types of data. You need DEI data. You need, you know, personnel data. You need to create more equity data, gender equity, or more skilled equity data. You need to look at, [00:11:00] uh, data where you want more engineers and technologists, or more data scientists, or data analysts, or people more embedded in change management.

Um, change, there are very few change managers, actually, who are, trained in change management. Plenty of project managers, project managers are not change managers, right? So you need different types of stakeholders in your company that’s going to help you in driving that automation, not just technologists, not just AI, not just data scientists.

You need an amalgamation of business folks, of subject matter experts, Technical people, technologists, you need those who are, if you’re in government, those who understand the nuances of government, those who are in legal. Legal is another profession that actually will require a lot of AI, already has AI.

Procurement. So there are many different ways and which one puts a skin, that cat, and where you can [00:12:00] actually take the goals of your organization. Look at the core business of that organization, identify what I would call the non negotiables of that organization, without which the organization would actually come to a halt, right.

That becomes the core functions of that organization. And when you know what those core functions are, right, and you say this is the core function, what are the core sets of data? So you now start, um, drawing Venn diagrams. And the interdependencies in that Venn diagram tells you The core sets of data upon which, whether it’s HR, whether it’s IT, you know, which kinds of functions depend on each other, that becomes also part of your core data set.

Now you know how you’re going to translate your goal into your data sets, your core data sets. You know, in government, they call in a particular [00:13:00] area, I’m not going to get into the area, We have something called the minimum data sets, you know, if you look at substance use or in other areas in health and human services, they actually have defined this by minimum data sets, without which the core functions of that particular department, they would not be able to function.

So you can apply some of what is being done in government, believe it or not, in the commercial. Uh, sector two, and you can apply it in manufacturing and supply chain, in transportation, in auto, any, any industry, healthcare, right? And then you can just say, these are the minimum. You can apply this in your core business in a nonprofit as well as in a for-profit world.

And that’s how you take your goal and then say, this is the data set. And then you actually get into your target setting of instead of 40 million, it really should be 15 million.

Ryan: Yeah, wow. That a lot of information. I love it. So one of the things that I think is interesting about your [00:14:00] background is that you’re very much on the cutting edge of thinking and talking about artificial intelligence. And. Obviously that’s, this is a topic that has completely captured the public’s imagination, especially as it relates to work and productivity.

And one of the things that I’m interested to get your take on is, you know, obviously large language models are an incredible tool, but it’s not the entire umbrella of what artificial intelligence is. There are many other, you know, model types and different structures and all that. So I want to talk a little bit about.

What to use an expert think of, you know, what, what is your definition of, you know, AI, like, what are these tools? What are some of the use cases when to use it? What can the ROI be?

Sue: There are multiple, um, multiple questions within that question. So let me [00:15:00] actually simplify this and I actually took some notes because I, uh, don’t want to just shoot off, uh, and start talking about numbers. So 82 percent of the CEOs across the spectrum have said that AI is something that they see that they need to embed in their business.

And this is, this comes from Conferi, right? So my sources of information Or like everybody else, Conferry, McKinsey, Harvard Business Review, Gartner, all the usual suspects. So I’m not favoring one over the other. So you’ll have to forgive me if I shoot off numbers. These are coming from these types of sources in the last six months to, you know, most recent, say, so on.

So it is a game changer in terms of identifying and setting your business strategy, your operational, Um, efficiency and also in your customer engagement. These are the three core things of any business, right? Customer engagement, [00:16:00] business strategy, and your, and your operational efficiency, efficiency, and then looking at the whole spectrum of those applications, you mentioned LLMs to be one of them.

Now, you know, artificial intelligence, typically people say, okay, there’s machine learning, we can train the model, we can train the machine and the machine will do, we can then, you know, have large data sets, we’ll train it by magic, you know, we can scale it and then we can address large volumes of data, we can apply it and it addresses those three things that I just mentioned and to some extent, yes, but I want to address a couple of things before I go into the, What AI can do and where it can be applied and where AI, uh, needs to be, uh, uh, applied with a great deal of caution and where it should not be applied.

So, So there’s machine learning, there’s natural language processing, we know about, um, uh, you [00:17:00] know, uh, speech recognition, we talk about, uh, you know, data visualization, we talk about deep learning, we talk about cognitive AI, et cetera, et cetera. So there’s this whole spectrum of the maturity of what artificial intelligence can do in the spectrum of what artificial intelligence is.

But what is artificial intelligence, really? Right? And if you just Google, you’ll find the various definitions of Artificial Intelligence, which is why I call People talk about AI standards, they refer to the EU, um, you know, and they say, okay, they have these standards. We don’t have an established set of standards yet in the US where we’ve Uh, embraced it and applied it uniformly, and then there’s standardization, which are two different things, right?

AI standards, standards and standardization. Standardization, which means we’re uniformly applying it in single [00:18:00] organization. That is not entirely true, uh, in any one single organization, you, you’ll not find it necessarily, uh, there, even if, even if it is, it’s unique, right? Um. Okay, so we’ve got that established.

We have the White House, and they’ve talked about the AI Act, and they have recommended something. But if you really look in the U. S. today, I can sell you a bill of goods and say I have the best AI solution tools, I have the best LLM, I have the best sandbox, and I can scale something and please purchase it for me, and I cannot be held responsible if it doesn’t work for you.

Because there’s no regulation yet by which I can be held responsible. Right? All right. So now we get into the Also the issue of ethics we get into the issue of bias We get into the issue issue of FAIR, F A I R, which I’m not going to get into great detail here But there are plenty who claim that their [00:19:00] solutions and tools actually help in reduction of bias and so the you can always ask how do how do you do that tell me because How do you describe bias?

What does bias mean to you? So just between the pair of us. Perhaps the way I describe bias is going to be vastly different from the way you describe bias. Because our lens, I can assure you, is going to be different because the way I view the world and the way I look at bias is going to be very different for you and the way you look at bias.

Right? We are two different human beings and two different experiences. But that, in the absence of standards that you and I have to apply, in the absence of an AI compliance regulatory, uh, you know, rule, um, I can just say whatever I want to say. You can say whatever you want to say. And, you know, and never the twain shall meet.

And we are both right in our own, in our own way, we are right because my experience is mine and your experience is yours. You see, we have a fundamental problem there. So that’s the wrong bias. Forget ethics. Forget, [00:20:00] forget fairness. Forget all of that. Right? So, so, we’ve got to ask these questions. I’m not saying that there are vendors out there who are quacks.

I’m just saying we need to ask those questions of ourselves and whatever role that we’re playing and wherever we are, right? So, so that’s there. Now, there’s undoubtedly one of the things that when we are asking this thing about AI, large language models. So there’s value in that. And the fundamental value is that it does drive operational efficiencies.

That’s without, without a doubt. It, it, it does. And it does, it goes back to your previous question about, you know, it achieves your long term goals. It helps you in competitive, um, it has a competitive edge if you have these, um, large language models and it does maximize value. So, you can ask yourself, how do these large language models actually work?

And that’s just picking on that, that’s not the only thing, we know that, right? It’s all based on the algorithms, right? It’s all based on the [00:21:00] accuracy of those algorithms and the complexity of the different types of data that you have. So, data, and this is again a nuance that people who are not in data may or may not understand, or may or may not care, right?

Those not, who are not in data, those who are not in AI, those who are not in this field typically go, Well, that’s the door you need to knock because this is not my forte. That’s why those guys are there, right? You talk to them without realizing that they are very much part of the change. They’re very much part of the conversation and they’re very much part of the decision making.

There is no they. They’re all part of us and a we. They’re all part of a larger stakeholder. It’s going to come back to, to them. Right? Whatever level you may be, whether you’re the analyst or whether you’re the CEO, [00:22:00] you’re all part of a larger organization. You’re all part of the decision making, whether you are invited.

To be, uh, at the table, whether you are not, it’s going to impact you one way or the other. So you better be knowledgeable and aware of how that data is going to impact you, and how that AI, whether it’s an LLM or not, whatever it may be, that tool in which a decision is going to be made is going to be important.

impacting you, so directly or indirectly. So, so that’s, that’s, that’s the one thing. The second thing is that, you know, AI actually helps you in identifying areas for development, right? It helps you address certain gaps. The whole thing about AI, if one had to really use one word, is it’s all about speed.

It’s what the human’s brain cannot do unless you’re, you know, uh, you know, a chess, you know, a, a, a, a brilliant scientist or something of that kind where the human brain [00:23:00] can, can compete with the, with the computer. Most of us, I, or I would just say someone like me, I would never be able to compete with a computer’s brain because I don’t have that kind of brain.

I don’t have that kind of power. So it’s all about speed. But speed at the cost of what? Speed at the cost of quality? Hopefully not, right? And so when we are talking about AI, and when you’re talking about identifying areas for development, you have to be very, very careful. So when you’re applying LLMs, for example, you have to look at what I call the different types of data, but the order of data.

And then when we talk about the order of data, there are what we call simple, I’ll call it the simple types of data versus complex data, very highly complex types of data. So let’s use, this is my favorite example, healthcare, because Most of us on this planet will be part of the healthcare ecosystem at [00:24:00] some point in our lives if we are not already part of it, right?

And healthcare becomes more and more and more complex as we get older because we get more and more and more issues, right? Um, even if we are terribly fit, we still are prone to something or the other as we get older. Right? And as, as we get more and more, more poor morbidities, um, we also now actually have tried to embed mental health.

So now, because we’re looking at mental health and the physical health together as one, that’s where our payment is actually going to go in 2030, which many people are not, not actually aware of. Um, so if we’re going to do that, that means now we’ve, our data is going to get more and more complex. Very complex.

You’re going to get all types. And why is it so complex? Because currently the funding stream in the U. S. is this is the physical data, this is your mental data, and, and the funding stream is this way and the funding stream is that way. And now we’re going to bring them to, together. [00:25:00] So just for a minute, let’s just keep the mental health data separate.

But just in the physical data, you have all kinds of chronic disease, you have hospitalizations, you have outpatient data, that itself is very complex, right? If you just look at ICD 10 diagnosis codes, that’s very, very, very complex. This is all tied to how you pay. Now, in that pot, you have the insurance companies who actually determine who gets paid what and what comes out of the member.

We are talking about very complex data. We’ve got financial data, we’ve got clinical data, we’ve got patient data, we’ve got demographic data, we’ve got all compliance data, we’ve got, we’ve got all kinds of data. So the complexity and the order of that complexity is very high. Versus, in that same data set, if all I care about is how do I schedule Sue’s appointment, that is a very simplified data.

I don’t need an LLM probably for that. I just need a [00:26:00] virtual assistant for that, right? That I can automate, which already exists today. How many of us actually hear a human voice anymore? We don’t, right? You’re, it’s scheduled. You go online, it’s scheduled. You want to get a haircut, you can just go online and get a schedule.

How many, how many, whether it’s even for your airline, you just go and you just Google and now you can just, uh, you know, buy your ticket online. Carvana, you can even go get your cars online. You want to sell your house, you can actually Google and look at homes. Think about it. Think about what we’ve done.

So the basic tasks, so you have to look at data, not as a uniform, unified set of data, but the order, the complexity of the data, and depending on the complexity of data, you figure out where should you be applying AI. And the reason I bring that is that the risk. And the, remember I said, maximizing [00:27:00] value.

The lower the risk, the higher you maximize the value. The simple, the most simplified your data is. The higher you can maximize the value, such as your scheduling, it’s a no brainer, right? I can, frankly, pardon me for saying this, get rid of three scheduling assistants, automate it, and get better revenue, better speed, you know, buy a scheduling, uh, you know, uh, solution, boom, 26 facilities in one region.

I can, I can get rid of three times, you know, 26, whatever that number is. You know, so that gets to what, uh, 72, 78, sorry. And then, you know, 78 members are out. I can take the 78 and deploy them somewhere else. If I have a heart and a soul, if I don’t have a heart and soul, I give them three months wages and I say, go find yourself another job, right?

This is the nature of, of business nowadays, right? So Those are what I call, you don’t need an LLM. Now, LLMs are [00:28:00] required for what? Or, Another case, you look at supply chain, right? You will probably want to create an AI model because supply chain, manufacturing, they, they are easy. You know why? Because I call them widget counting.

They’re easy because you can to the T. They’re very precise, you know, number of nuts and bolts I want, number of, you know, whatever, um, um, attires I want, whatever, whatever it is. If it’s pharmacy, number of refills that I want, right? Number of useless medical alerts that I’m giving to the physician that he or she doesn’t need, right?

To tell the, to tell the, uh, uh, patient because patients have said over and over again, I get 10 useless Uh, alerts about my medicine, and I’m tired of it, so as a result of it, I’m not going to take any of my medicine, so I am non compliant with my medication adherence. This is a, this is an issue for a final memorial, right?[00:29:00]

So when you think about where AI can and cannot be applied, you just don’t look at, this is easy. You have to look at two things at a minimum. You have to look at the complexity of the data. You have to look at the risk value, then you have to put in a financial analysis and look at the ROI of it. You have to tie it back to your goal setting and tie it back to your initial planning and adjust that.

You have to tie it back to whether you have a CQI process, a continuous quality, a learning process, a cycle, so that you’re not waiting to calculate it three weeks later or three, Months later in your quality, quarterly review, you’re embedding this throughout. Then you apply where it’s appropriate to have an LLM model, because LLMs are not cheap, by the way, right?

Getting large amounts of data is not cheap. How valid is that data? Because how old is that data? Is that data from 10 years old that you’re then going to make predictions for the future? [00:30:00] Applying predictive analytics? Or is that more recent data? So, you have to factor in a lot of different things, right?

I’m not even talking about cognitive data, where you map your, the mental brain and, and, you know, do that kind of cognitive AI. That’s pretty expensive stuff that we are, that we are talking about. We are, that’s very advanced, and for that you need a level maturity within an organization for them to know that the investment that they’re going to make actually is going to yield that kind of revenue, right?

Um, you can look at AI imaging, for example. I’m not going to look at that. You look at GEs of the world, you look at Philips of the world, you know, they, they’ve, they’ve got that. You look at defense, You know, military, for example, uh, there are certain types of industries where cognitive AI is not only there, it’s extremely important actually to, to have that, uh, in today’s day and age to be ahead, not only in [00:31:00] terms of the competitive edge, but actually in terms of being ahead in warfare.

Uh, to, to actually, uh, know where your, quote unquote, your enemies are coming from. So the application is endless. The application of AI is endless. But you have to put it in context of what is the context where you’re going to apply it. And then you have to apply the ethics. And the bias of it, you have to have a framework around that.

And then you also have to apply it to your business. Because if you are in government, if you’re a federal government, that’s completely different. If you’re coming from a defense perspective, it’s completely different. If you’re coming from a commercial perspective, it’s completely different. So I’m just using this as a very small example of knowing what the When to engage, how to engage, but at the core of it is it has to tie in back to your strategy, your customer engagement, and your operational [00:32:00] efficiency.

Those really don’t change, agnostic of your industry, agnostic of your geography, and agnostic with your government or non profit or for profit.

Ryan: I love it. So I think by sharing all this information with everybody, like it’s super clear that you know what you’re talking about. What I love to do is to give people the opportunity to get to know the human behind all that information. Tell us a little bit about yourself. What do you like to do outside of work?

Just tell us, you know, kind of what, what, what makes you happy? What do you like to do for fun?

Sue: So I love to read. I’ve always, uh, so I used to read a book a day, not on AI, but my parents, in particular, my mother always took us to, um, you know, we, we’re not nerds. I’m definitely not a nerd, but I loved reading [00:33:00] and I would, uh, finish my homework. You know, didn’t like doing homework at all, but I would finish it just because the carrot that would be dangled in front in front of me was you could go read a book.

Um, I, we were not allowed to watch television and I think that was, we were allowed to just be kids and we were allowed to just go out, run in the wild and just, you know, do good stuff. Just, you know, just. It was fun. Uh, I think that actually shaped, and the other thing is my parents were anthropologists. I always go back to my childhood to reflect on, um, how, uh, many people have told me, and I never thought about it, but I hear it often, that it, that I find it very easy to just blend in.

Um, and the blending in is actually getting to know the human being sitting in front of you. I’m sitting in front of you today. I find Just chatting with you from one human to another, that itself is [00:34:00] very interesting. You’re, uh, you are asking certain questions, um, and I’m responding to it, but I’m also curious to find out, you know, what motivates you?

Why do you do this? Uh, what makes you tick, right? Those are the types of questions that are going on in my brain. I think when you have that human curiosity, It doesn’t really matter whether you’re in data or AI or whatever it is that you’re doing, it’s just our human nature to just find out from each other what we can hopefully, I would want, I would want to say, um, you know, what makes us all do what we do?

And I think I attribute that to the travel that my parents, uh, took us to many different places. We were not rich by any, uh, uh means, but, uh, anthropologist parents have a different way of looking at. at life. And that I think is there in me. There are genes of looking at life differently. So, from a very early phase, without my knowledge, I think I [00:35:00] started looking at things differently, and those were data points.

I didn’t think of them as data points, but now I think it shaped my way of thinking as saying, That’s enough. That’s an interesting observation. That’s an interesting, and over time as I became, as I went to school and I, you know, became more and more structured and formal education, you know, data and stats and all of that, it began to take shape.

But observation is observation, and observation is nothing more than Uh, a data point, right, that shapes your, uh, thought process that then distills it to one, uh, word or one, one, just one singular thing, because we try to simplify things because we have an overload of information in our brain, especially with everything going around us with technology and something else.

So it’s very difficult to actually retain it. And retain information at times. So for me, what makes me tick is [00:36:00] the travel, the interaction with human beings, um, the observation of what’s going around me, uh, new technology, new ways of thinking, innovation, the challenge of, um, the challenge, the frustration of, uh, not finding success.

That’s a challenge. That’s, it’s frustrating, but you know, then you go back to it three months later and say, why can’t this work? Right. And you don’t need to be the only one in the, in the room saying, why can’t it work? You surround yourself with bright people who are much smarter than you, who are equally curious, if not more curious than you, and you learn from them.

And somehow or the other, collectively, you’ll find a solution. And so there’s a, there’s joy in it. You don’t need to be the one, um, you know, finding the solution, but you need to be [00:37:00] part of that process. And if you are a true leader, a true leader actually, uh, sits back and enjoys and watches people he or she may have taken under their wings and enjoys the process of watching the people, uh, grow, you know, and, uh, under their leadership.

And there, I think there’s no greater joy than that, to see people just innovate, to see that light bulb going on, and, uh, you know, And then sharing that. I think knowledge is to be shared, not to be hoarded. Um, but there are different types of people on this planet and, you know, uh, competition is tough. So, you know, we do what we need to do.

But at the end of the day, I think that’s what makes me tick. It’s the travel, it’s the enjoyment, it’s the reading, it’s the innovation, and it’s the reflection. It’s a lot of mistakes that people make. Uh, we’ve made, I have made many, [00:38:00] uh, and, uh, you reflect and you say, you don’t beat yourself up, but you say, what could I have done differently?

And how would I approach it, uh, you know, today? I don’t beat myself up, uh, but I do say, what can I, uh, what would I approach? How would I approach it? And how can I streamline this? That’s just a natural. Evolution also of as you get older, I think it’s also maturity. It’s also, um, what you want from your life and what you want to leave behind, really.

What do you want to share with others when you, when you finally exit this world? I mean, you don’t, I, I don’t want anything. I just want something like someone could say, Oh, you know what? I attended something and I got something out of it. That would be very satisfactory to me. That’s, there’s nothing more than that.

Ryan: Yeah. Wow. I love it. Uh, Dr. C, thank you so much [00:39:00] for taking the time today. This was, you know, uh, an absolute fire hose of knowledge and like all over from data to AI to anthropology, you know, a lot of different pieces of the puzzle. Thank you so much for taking the time to share with us today.

Sue: Yeah, no, it’s, it’s, it’s been my pleasure. And thank you for that, asking that last question because I just fell into it. And I, uh, I mean, I’m going to go and talk to a scientist that I just happened to meet. A group of us actually happened to meet. And he’s going to, uh, Talked to us about, um, space and technology and what they’re doing and finding another planet.

And that’s not my forte. And, uh, he gave this talk to all of us about two weeks ago. And in that he talked about, as they’re, you know, all these scientists across the world literally are looking at it. He’s, they’re actually also looking at the intersection of creativity and then you mentioned [00:40:00] intuition and spirituality because a couple of people actually asked that, you know, how do you, you know, what is that journey?

And here you have a group of physicists and other people, literally from all over the world, some Nobel, uh, you know, laureates. And it’s fascinating. It’s fascinating just listening. I think the art of distilling something that’s so complex and the ability to distill that into what I call a layperson’s term and language and then entice people like me who are not in that space at all.

Um, I think that’s an art. And that’s how you start engaging people. I think data is that way. You don’t need to be in data to engage people in data. If you’re able to distill it in a language that speaks to an average J or Joan, uh, I mean, uh, Joe or Jane, then you can engage them. And once you’ve got people engaged, you’ve got data stewards and you’ve got change champions in your, in your business.

And that’s the whole point. The whole [00:41:00] point is you don’t need to be an expert in anything to engage people. in that conversation. And if you’ve got them engaged, you’ve got a movement going. And that’s, that’s the essence to me of life. You don’t need to be an expert. No one can be an expert in everything, but you can have people engaged in something.

Ryan: I love it. I also want to make sure to, to thank the audience. If you learned something new today, if you laughed, if you liked anything, please make sure to give us a like a subscribe, go on, write us a review, hopefully a good one, it really helps, uh, keep this thing going. It’s kind of the, uh, the podcast fuel in the engine. Uh, Dr. Soot, thank you again so much for coming on. And this has been another exciting episode of the Making Better Decisions podcast. Thanks so much for listening.

Outro: That’s a wrap for today’s episode of making better decisions for show notes and more visit, making better decisions dot live a special thank you to our sponsor canopy analytic canopy. Analytic is a boutique consultancy focused on business intelligence [00:42:00] and data engineering. They help companies make better decisions using data for more information, visit canopy analytic.

com. There’s a better way. Let’s find it together and make better decisions. Thank you so much for listening. We’ll catch you next week.