Kevin Hanegan Using Data To Make Decisions

Kevin Hanegan

[00:00:00] Kevin Hanegan: And I agree with you, I see great things in the future. Sometimes it has to get bad before it gets good. Um, but the answers to society and economic problems, they're there. We just need to ask the right question and pull them out. I mean, we, we have the best of the best people in the world and people are working on it.

[00:00:18] But sometimes the challenges again, they're not getting a different perspective or they're looking at it from an individualistic point of view. Once we all go against like a common question collectively, yeah. I mean, at that point, everything is endless. I think historically, the challenge is we have to get to that point where we face the crisis together. And unfortunately right now I think we're there, but a lot of people don't see it yet.

[00:00:50] Ed Watters: To overcome, you must educate. Educate not only yourself, but educate anyone seeking to learn. We are all Dead America, we can all learn something. To learn, we must challenge what we already understand. The way we do that is through conversation. Sometimes we have conversations with others, however, some of the best conversations happen with ourself. Reach out and challenge yourself; let's dive in and learn something right now.

[00:01:41] Today we are with Kevin Hanegan. He is an author and the chief learning officer at Qlik. His book is Turning Data into Wisdom. Kevin, could you please introduce yourself, let people know just a little more about you, please?

[00:01:59] Kevin Hanegan: Yeah, appreciate it, Ed. Pleasure to be here. So as you said, uh, author of Turning Data into Wisdom, chief learning officer at Qlik. I come at data and analytics from a point of view of a non technical person. We're all exposed to data and information in today's world, whether we're technical at work, at home. And a lot of times we, whether we know it or not, we get overloaded. And so I've been spending the past two decades

[00:02:25] researching like how the brain works, how we make decisions, how technology helps us, how it hurts us, and how we can all kind of follow steps to avoid the overload and find decisions and make decisions without any type of bias or incorrect assumption. We've all been in a situation where we've seen a visualization or we've seen someone say something in the news and we believe it to be true

[00:02:49] and turns out it's not true. It wasn't intentionally false, but it was misleading. So yeah, everyone, if you don't raise your hand, you're lying, right? Everyone's. So that's my background. I actually have a computer science and math background, but turned more to psychology and adult learning since then. And kind of brought me to where we are today.

[00:03:08] Ed Watters: That's deep. So let's start off with data. What is your understanding of data? How do you explain what data is?

[00:03:21] Kevin Hanegan: Yep, so the first thing I say is, You say that in a room of 100 people, 50 are going to start going like this, being like, I'm out data technical, didn't go to math school, whatever. Data, you know, if it makes you feel better, call it information, call it evidence, call it whatever. Like, data to me, obviously there's numbers, right? And there's, but there's also statements. You go and buy something online, you go to Amazon, you check out the feedback, that's data. You want to find a vacation, where you're going to go and you look at feedback and photos, that's data.

[00:03:52] So to me, um, when you think about a spectrum, think about a puzzle. It's one of those puzzles where it gives you like, um, metaphors or slogans and you have to determine what it means. So, you know, one of the examples I always use is there's the words P, E, A in green and then there's a split in the middle and it means split pea soup, right?

[00:04:12] So that's the outcome. To me, data is everything that leads up to, what is that telling you? We hear words all the time and the way we interpret those words is through the context. Data doesn't normally just come with context, like the information. Someone might just say, you know, you're looking for a coffee maker

[00:04:34] and one of the reviews might say, This was the worst coffee maker ever and they don't explain it, right? And then someone doesn't buy the review. Well, it turns out maybe they didn't like it because their priority was speed and it takes five minutes to brew, but it's the best coffee. Maybe you work at home,

[00:04:51] you don't care. So data means so many different things to different people, depending what your question is. That's why it's scary is you can have the exact same data point, you, I, and all of your thousands, hundreds of thousands of listeners can think something different. So we need to understand, what are we asking?

[00:05:09] What's the goal? Why is the question? And turn the data into wisdom. Wisdom is where you understood the question, you understood what the data was trying to tell you, you understood the context, you as yourself are naive in the fact that you don't have all the experience so you talk to other people to get their perspectives,

[00:05:29] and then you make a decision and then that decision paid out. Then you have the wisdom. Um, but it's not technical. What's interesting and fascinating is you don't need to know statistics, you don't need to know all those math terms like algebra and calculus. You need soft skills, you need to listen, you need to be able to communicate, you need to be able to be creative, um, to think of different contexts that could come up. And it's just skills that we don't actively practice a lot anymore, unfortunately.

[00:05:58] Ed Watters: That is unfortunate. And, you know, in our world, we're bombarded by what they say is what? 11 million data points per second sifting through that. How do you make sense of all of it? And the big point is how do we retain what we need to retain?

[00:06:23] Kevin Hanegan: Absolutely. So you said it right. Like there's a 11 million points if you think of all the senses, right? Sound, smell, and if the brain was going to consciously see everything, process it, ask you internally what to do, you would overheat the computer. Your brain being a computer, you'd also need to sleep like 100 hours a day to recover. We can't do that. So the brain is very intelligent. It has these shortcuts in it and it makes predictions based off of what your previous experiences are, based off of what your values are in life, based off of other, I mean, the common example is someone is thinking to buy a car and they're like, I'm going to buy a white jeep and then all of a sudden they see more white jeeps driving around.

[00:07:06] There's not more white jeeps. It's just now your brain said, Hey, this is relevant to you. So I'm not going to filter those out, I'm going to process them. But it's always the same. So the brain finds out what's relevant to you. There's two problems with that. The first problem is, thousands of years ago, what was hundreds of thousands of years ago, maybe even millions, what was relevant to people was,

[00:07:28] I don't want to get eaten by a dinosaur, I need to find food to survive and I need to get out of the elements. What was relevant for us five days ago might not be relevant anymore. The world changes, technology changes, all of these things change. And I still try to look at cable boxes and like, okay, I have my cable channels but now I have all my streaming serve,

[00:07:47] things change so much that your brain is saying, This is relevant. But it's not relevant anymore. And we need to kind of coach it to say, Hey, time out. That mental model was so yesterday, it's not today. And that's hard, right? Cause we're all moving so fast. So brain is powerful, but it needs to know what's relevant

[00:08:06] and sometimes it uses information that's outdated. The second thing is if it doesn't find anything relevant, it tries to make a connection. And sometimes those forced connections aren't accurate. So it's always about checking it. I know people say, Well, I always trust my gut. Your gut is right, it's going back on your history and saying, This was relevant before, this is relevant now. But you need to check it because it might not be relevant anymore.

[00:08:32] Ed Watters: It's really about perception, what we're seeing is not always what it appears to be. And going through the cognitive steps to identify that, it's like that. But yet we don't step back unless, it's like as a child, if you touch a hot burner, you learn quick and you don't touch again. But it, it doesn't seem like we have that ability anymore to have that defensive learning quick. We have to do it three, four times now to actually get it. Is there a de evolution going on with our thought process?

[00:09:23] Kevin Hanegan: Potentially a forced one, right? So we,

[00:09:26] Ed Watters: Yes.

[00:09:27] Kevin Hanegan: like you said, we, we usually when we're young, right, we learn. So that's how kids learn different than adults. Kids have no preconceived notions, right?

[00:09:36] Everything is a blank box and you touch it and it's hot. As adults, we don't learn that way. Everything we learn as an adult, whether we know it or not, we're relating it to something that already happened. So it's good in that way is we can give experience. Hey, do you remember when this happens? Now build on it.

[00:09:53] But the problem is, then you go, as a kid, you have this, you know, you're learning from the world, you're learning from touching, [00:10:00] interacting hands on. But then you start going through school and, you know, everyone's taking math class. When was two plus two ever not four, right? You learn things are black and white, you learn things are absolute truths.

[00:10:12] And then let's just say, you're, you're, you know, in, uh, pre high school, right? You're in your teen years and you want to question the, the teacher and you're like, Well, you know, maybe there is a scenario where two and two doesn't equal four. What, what happens is the teacher doesn't, you know, encourage your creativity.

[00:10:30] They say, Stop talking back to me. Go to the, go to the principal, you're in trouble. So we kind of squash this questioning, which is, at your point, it's the heart of everything we have to do is we have to question the world, but we don't. And then we graduate, and we go to school, we go to work, and then we're a new employee at a company and the boss says something that we may have a different perspective for.

[00:10:51] Usually we don't say it because we get yelled at and we're like, Hey, you've been here for one day, stop talking. And I say usually because obviously there are companies that get it. But we're devolving because we're in a high, not all the culture, but a majority of the culture is don't question, don't challenge.

[00:11:09] That's not seen as positive. And sometimes we say it's seen as positive, but we don't act like that. If, if, if someone challenges me, I get defensive and instantly I put my shield up. I'm like, Okay, you're wrong, I'm going to prove how you're wrong and I'm not listening to what you're saying. So, yes, unfortunately, I would agree with you. I think it's just out of our own behaviors and just not knowing what we're talking about is why it's happening.

[00:11:33] Ed Watters: That's right. I really think that's a logical answer to that. You are part of the Data Literacy Program. Could you tell us about that and why did this even come about?

[00:11:49] Kevin Hanegan: Yeah, so the Data Literacy Project is an organization that started out, or a think tank where it started in the corporate setting, but now we're trying to go down into universities and high schools and just everyone. Where people would, for lack of a better word, they'd have a bias,

[00:12:06] they'd see data and they'd have an answer, and they'd drive with that answer. And turns out that answer isn't right and so we're coming up with more tools and technologies to make data available to everyone. Like, data democratization is huge. Data strategy and management, every company has it. Everyone has different types of data visualization, data analytic tools.

[00:12:28] So we're giving them the tools and technology, but we're not teaching them how to use it. So someone sees a scatter plot or a bar chart that says, Ha, I prove my answer, sales are declining. And then we don't question, we're like, Okay, well, sales are declining. Oh my God, what do we do about it? There's a good scenario where the sales aren't declining.

[00:12:49] We might have missed a confounding variable, we might have missed why something was causing it, we might have done the visualization wrong because we're not trained on it. So we all have access to data in the corporate world. But we were teaching people how to use the software, not how to work with data.

[00:13:06] And so the Data Literacy Project came up as, let's be product agnostic and let's make sure that everyone doesn't hear the word data and run away. They're excited to say, You know what? Data is very contextual. You might say the number twelve is good if I'm talking about number of new leads this year, you might say it's bad

[00:13:26] if I'm talking about the number of customers that left it. It all depends on the context and the question and trying to reverse that devolution, I guess, one step at a time starting in the organizations, but then moving into individuals as well.

[00:13:41] Ed Watters: So we can actually reverse data flow or the direction data is flowing by different means. Through personalization, communication, how we perceive things, our mood, all of these things can actually influence how any data, depending on what we're talking about, it can influence data. And what drives data is, like you stated earlier, that hive mind.

[00:14:22] So what, what is the best way for us to start? Let's, let's say we're going through the news and we're dealing with what they're saying is fake news. How do we know the data that we're getting is correct? And how is the best way for us to check that the data is real?

[00:14:48] Kevin Hanegan: Absolutely. It's a great question. I mean, there's different paths we'll go down. The first thing I always say is check the source, um, right? So even if, on the news site they're going to give a source, is it a trusted source? Now, that doesn't mean if it's a trusted source, it's right. So you might see things on a news channel that come from the BBC, or New York Times, or whatever, I trust them but it still might be misleading. So, step one, trust the source or check the source. Step two, what I want to do is I want to say to myself, In what situation is this not actually true? And what that's doing is that's triggering my brain to think about it. Because a lot of what we're doing is unconscious, subconscious.

[00:15:25] So, if I'm saying to myself, When could this not be true? Then I'm starting to ask questions and look at the different questions and challenge them. And that's, usually what happens is we see a chart, like, let's take COVID. We all saw the flattening of the curve and we all saw our visualizations where it was going up and everyone's like, Oh, no, oh, no.

[00:15:45] But at the end of the day, you have to make a decision. So my decision is okay, I want to go on vacation with my family, is it safe? And then I have to define safe. What are the risks? I don't want to be hospitalized or I don't want to die. A lot of the charts that you see online, we're not answering that.

[00:16:02] They would say confirmed cases. What does that tell me? It doesn't tell me anything. Like, for example, if we're testing more people, we're going to have more cases. Simple as that. We're like, we're not testing the entire world. So the curve is scaring everyone. But in reality they should have been showing, which they started doing later on, is like the prevalence, like, how contagious is it?

[00:16:23] How widespread is it? Um, and when you start questioning like that, when, in what situation is this exponential curve not true? Eventually, if you practice it enough you'll get to the point, well, maybe they're testing more people than they used to. Maybe the population isn't the same, maybe we're using different sample sizes. And it's a lot easier to say than do that.

[00:16:44] But if you practice it enough, I always say, even though I hated it as a kid, they always tell you in math, Show your work. The reason they want you to show your work is if you, they don't want you to cheat first, but then if you got the wrong answer, they want to know where your thought process went wrong and they've cracked it.

[00:17:00] Same thing with us. We're like, Oh, my God. Okay, I'm going to stay in my house forever because it's exponentially growing. Well, I want to think out my thought process, then myself or someone else I'm working with is going to challenge and say, Well, that's not actually what it's saying. Or that's not actually what's happening.

[00:17:15] Or, so everything I do now, maybe not simple decisions like what am I going to have for lunch, but tactical strategic decisions. I show my thought process. I write it out and I share it with people and say, Break it. Tell me where I'm missing, tell me what I'm, tell me what assumption I'm making that's not true, tell me what other facts I'm missing. Um, the key point is you'll never have all the data, there's so much. But you just have to start challenging and questioning. And like you said, It's, it's something we don't do anymore, enough.

[00:17:45] Ed Watters: Yeah. And I think once you start doing that and you recognize you have to sift through the aggregate and pick those data points that are key, you really start seeing that it's a process and you can do this with every aspect of your life. And understanding that then you can turn that into good habits, good boundaries. And that actually can help save a lot of stress in your life if you just slow down and think about a few of the, you don't have to go through the thousands of data points, but you should actually slow down enough to get the key data points.

[00:18:36] The ones that are going to indicate the direction that you're going to move forward with whatever the decision is. So I, I really think it's kind of up to the individual to slow down and educate themselves to be proper in their definitions. Because I find a lot of the times, the words that I learned in school and I have missed or lost the definitions to, and I assume, and I do it still once in a while, that I assume that I'm speaking properly because I'm remembering wrong. So, how do we keep ourselves from doing stuff like that? Uh, like, remembering wrong and then giving out wrong data points.

[00:19:38] Kevin Hanegan: Exactly. I mean, we talked about how powerful the brain is. So if we remember something wrong, um, it sticks in there and we believe it's true. So we're passionate about it. A lot of what I talked about recently is that, is the concept of unlearning. Which

[00:19:52] is, because we're evolving so quickly, technology and everything, a lot of times we make decisions that's based off of, I'll [00:20:00] use a technical, mental model, like how we see the world that's outdated. It's really hard, even if you consciously know you're changing, it's hard to learn the new way as an adult unless you unlearn the old way.

[00:20:14] So, just to use your example, what I want to try to do is I want to consciously think about what's different in my life that I work with, what's different in the world, what's different in the things that I interact? And, and the more I think about them, the more I articulate them, the more it becomes prevalent in my brain, the more the brains filters are going to say, Okay, this is important for me

[00:20:34] now, this is not important for me. Otherwise, out of those eleven million checks, you're going to make a good portion of them based off of outdated information. The other thing I do is I share it with colleagues. Like, we all know diversity trumps ability. So you have people that think differently. To me, when I think of diversity and inclusion, it's the same angle, but it's more of our own cognitive diversity.

[00:20:57] I, you know, if I use a Myers Briggs type indicator profile and I'm a certain way, I want to find someone who's another way to make sure that I'm balancing things out. Just like in sports teams, the best teams of athletes don't win. It's the best teams that complement each other. So, working with colleagues that have different perspectives, you're going to find out that some of your logic is flawed. And there's a good portion of people listening

[00:21:21] that are going to say this is magic, this is voodoo, I don't agree with it. Um, just next time you go somewhere and you're going to buy something and you see that it's on sale and they say like 25% off, just stop and think. Historically, would you have said, That's great, I'm going to buy it or would you have said, Okay, but maybe they increased the list price to begin with. The, one of the shortcuts the brain takes is called the anchoring heuristic or bias that the first number or data you see is the anchor. Everything you see after that relates to that.

[00:21:55] So if I say, I'm going to buy a car and the sticker price is 100, 000 and the guys or lady says, Man, I'm going to sell it to you for 50, 000. If I don't think about that, my brain says, Yes, sign me up. Great deal, 50% off. The sticker price might have been, you know, 45, 000 in reality, right? But because we're given that point, that's how people in marketing will get the consumers. But we see it in life, too, is, and we see it in the news. Whoever says something first, usually that sticks, right? And then we have to combat it. So working with diverse populations, getting other opinions is, is, I mean, I'd say it's essential nowadays.

[00:22:33] Ed Watters: Yeah, I agree there and, and we don't see that. We, we see people cliquing up and getting into their own hives and they're driving the data that they already consume. Where if we reach out and understand there's more data out there that's relevant in many ways. It's, it's, uh, baffling to me that we can allow ourselves to isolate ourselves as much as we do. In Politics, let's use politics because it's politics season, the left and the right, the Republicans, Democrats, I know people personally that will not cross party lines because they're stuck on party lines and that's what they vote. Because they know in their own minds, they're not going to lie to me.

[00:23:38] Or, and I don't even mean to be that harsh about it. I'm just trying to explain that if you really want the truth, you have to look at both sides and we are not doing that anymore. We, we have to get out of that mentality. And diversity, it is the answer. It's what built America. And I tout that all the time.

[00:24:07] Kevin Hanegan: Absolutely. Well said, absolutely. And without getting too, like, both sides lie. Both sides lie unintentionally, both sides lie intentionally. But the point is that their end game is about winning. Whereas the end game should be about making America great again, or whatever it, pardon the pun, right? So going back to where we were in the beginning, it's about collectively making people better.

[00:24:34] But unfortunately, we have this individualistic mentality where it's about, okay, I want to win. What does the data say I should do today? Okay, it says I should follow this, I'm going to follow this. And both sides do it and that's what drives me crazy. And it's hard to trust anyone because they are using numbers to try to manipulate situations, um, instead of if we all questioned and challenged and we all said, This is what we want as an outcome, maybe, um, we'd be in a different place. But again, it's been suppressed, we don't typically question. Um, we typically just trust people until, you know, um, things like this happen. And then there's a big breakdown, but

[00:25:12] Ed Watters: Yeah. So is there a good way to present data to an individual that thinks differently from you?

[00:25:22] Kevin Hanegan: There is. So, you know, I go back to, I've been in this business forever and I've taken years of education on writing and reading, never taken a listening course and I've never really taken a communication course. I've taken speaking courses, but I never really learned, how do I communicate that to them? How do I listen to the other side? And there is an art and a science on communicating. Like, you obviously need the emotional part that pulls that, what do they care about? What's relevant for them? Many times in business you'll, not in politics, but in business, you'll see it where we communicate what the executives want.

[00:26:00] But the individual's like, That doesn't help me. Like, I don't have a share in the game, what does that matter? Um, we also need to connect sometimes with the data and the insights and in the best practices, highlighting what others do. A lot of times what we end up doing is we say, Here's what we're going to do, and we don't give a lot of context.

[00:26:18] So, you know, there is an art and a science to sharing the data. Getting different perspectives to highlight what that data means, showing it out. If we've go this path, this is what's going to happen. If we go this path, that's what's going to happen. Um, and we don't do that. And a lot of times the people listening,

[00:26:35] we don't have any formal training in listening, so, what we're doing, I can guarantee if you're in a political debate, a business debate, a debate with your spouse at home or partner, when they're talking, we're not listening. We're waiting to say how they're wrong and what our next statement's going to be.

[00:26:52] And so they might have just said the answer to world peace. I didn't hear it because all I'm thinking is, okay, well, I need to hit her with this punk next. Like totally not listening. Um, that's the problem is, is we don't know how to communicate

[00:27:06] Ed Watters: That's right.

[00:27:06] Kevin Hanegan: or listen.

[00:27:08] Ed Watters: I agree. I think listening is the key. And putting our own bias away, you know, understanding other people is the key. I love just knowing what other people think. And the freedom that I found when I turned off what was being told to me, and figuring out, I can look and see what's going on. And as, as trends climb and decline, it's up to me to climb onto that trend or leave it alone because you, you can pretty much see if it's going to be a fading trend or not.

[00:27:57] And that's the key, finding those trends that are going to be good for society instead of just for me. And a lot of people, they are stuck on the mentality where it's got to be right for me or else it's not right at all. And, and I think we have to find equality in so many different things in our world.

[00:28:25] And it's a growing process and I think that we are learning. And as we learn to sift through the data, understand what data is and how to implement it in our lives properly, I have high hopes for the world. And right now it seems like everybody thinks the world's going to a pot, and it might appear that way, but I think it's a trend. And people are waking up to that trend and they're starting to analyze again and say, Well, is this really right? Do you see anything happening like that? A trend in our political world, our, uh, economic world that is driving us today, right now?

[00:29:19] Kevin Hanegan: Well, to me, everything you're, and I agree with you. I, I see great things in the future. Sometimes it has to get bad before it gets good. Um, but the answers to society and economic problems, they're there. We just need to ask the right question and pull them out. I mean, we, we have the best of the best people in the world. And people are working on it but sometimes the challenges, again, they're not getting a different perspective, or they're looking at it from an individualistic point of view.

[00:29:45] Once we all go against like a common question collectively, yeah, I mean, at that point, everything is endless. I think historically the challenge is we have to get to that point where we face [00:30:00] the crisis together. And unfortunately right now, I think we're there but a lot of people don't see it yet potentially because of the data illiteracy. Like, look at climate change, for example, or other things.

[00:30:10] So I definitely see optimism, but I don't know if it's going to get better instantly. It might even get worse before it gets better. But we have the best tools, we have the best minds. We can innovate if we can just open up our perspectives to others, be more diverse and inclusive, um, and question things. I mean, it really comes down to that.

[00:30:32] Ed Watters: Yeah. And, and I think we're going through that baby learning stage where we have to touch to actually learn. So

[00:30:40] Kevin Hanegan: Exactly.

[00:30:40] Ed Watters: it's, it's all about growing and understanding more and more. Tell us about your book. What is the book about and why did you write the book and how people can get the book?

[00:30:56] Kevin Hanegan: Absolutely. So the, the book is similar to what we're talking about. Um, one of the reasons I wrote it is because I have four kids and one of them is autistic. And as, as they started growing up, talk about seeing a situation completely different, everything was very different. Sometimes it was black and white, but sometimes it was like, I always, why would you do that or think that?

[00:31:19] And then one of their therapists was like, Well, stop and think about why they're doing that. And then I started listening more. I was like, Oh, they're not wrong, I'm not necessarily wrong, it's just a different perspective. In fact, I could probably argue they were probably right more than I was. Reason I thought I was right was because of

[00:31:39] history, like how I've been brought up. But that doesn't mean it's right or wrong, that's my perspective. So then I kind of had this huge light bulb, like, Oh, my God. So everything we're dealing with in the world, you know, same data point, different interpretations. No one's necessarily right or wrong, it's about aligning those different perspectives.

[00:31:56] So I wanted to write a book where people would understand how the brain works, how we're susceptible to bias and how we overcome it by working in diverse perspectives. Like you said, Slowing down the thought process, sleeping on it is actually a scientific way. And helping people, whether they're individuals looking at, you know, where do I go on vacation? To businesses looking at future business models,

[00:32:19] how they could see that. So it does follow the process of how people make decisions, how data can be very useful, but how it can also be very challenging if we're not questioning it. And then what are some frameworks and strategies we can use to, to optimize our decision making? And it's available on Amazon. Just go on Amazon, type in Turning Data Into Wisdom or type in my last name and it'll pop up.

[00:32:45] Ed Watters: What do you see about our scientific world right now and how people are not trusting the scientific data model? What is the inherent danger to that? And how do we rectify what has happened there and get things back to trusting a scientific model?

[00:33:13] Kevin Hanegan: Yeah. Well, I, I will say, you know, that you hit the word, the keyword trust. If, if consumers or individuals do not trust the process, there's nothing you can do. Well, until you fix the trust, obviously, but so to your point, you got to get the trust back. And it's interesting because when I talk to universities and businesses, I always use the scientific method as the right example.

[00:33:35] So, like, if you're a research scientist, you have a hypothesis. And you do everything in your power to prove that it's wrong. You look at all the data and say, Nope, nope, that must be wrong. When you can't do any reasonable certainty, then you just assume it has to be true. In business, in life, my theory is we do the complete opposite.

[00:33:58] We have a hypothesis and we find and search for any data that tries to prove it and we filter out any data that highlights it not be right. And we say, Aha, I'm right. And that's the definition of confirmation biases, we look for data that validates it. So I talk a lot about, we need to use the scientific method.

[00:34:19] I think what's happening is people in science, there are some, and I don't know all the details, that don't go through a rigorous scientific process. They might find a data point and then if it's not peer reviewed, people are saying, Well, there's other alternatives. I trust the scientific process, but I think people listening to it have issues with trust because

[00:34:40] of everything that's going on in the world today, right? We, we see misinformation everywhere. So someone saying something that might be shocking to us, um, isn't easy for the brain to comprehend because inherently, I think, like myself, I don't trust many sources of data anymore. Um, they've been filtered and changed into whatever.

[00:35:01] So I think the answer paradoxically is, is go back to the scientific method and use it for everything. Try to, show people you're trying to disprove it. You can't open it up to peer review, open it up to criticism. And you know, if collectively we can do that, there's a reason why in research, there's research that is classified as peer reviewed versus research that's not. I never look at the not stuff because that person hasn't followed the scientific method. They haven't run it by people with different diverse perspectives. I only look at peer reviewed stuff. Maybe that's the answer is we only let peer review stuff go out there. I don't know.

[00:35:37] Ed Watters: Yeah, well, that could very well be. Because if, if an individual is within the same, you know, caliber as you and they're telling you, Look at this and you're, Well, yeah, there's, there's bias. There's definite bias there and we have to fix it. And I do believe that is the answer, you know, let it hold to be true. If somebody can disprove it, it's not true. So what's the wrong? What? What's wrong with the truth? I just, because that's really what it boils down to, if it's not truth, it won't hold up.

[00:36:25] Kevin Hanegan: Absolutely. And sometimes it does. But, yeah.

[00:36:29] Ed Watters: Yes. Yeah. Under false pretenses and

[00:36:33] Kevin Hanegan: Exactly.

[00:36:34] Ed Watters: a lot of, yeah. So do you have a call to action for our listeners today?

[00:36:41] Kevin Hanegan: I mean, honestly, I would like everyone, next time they try to make a decision, next time they hear someone talking about anything, I want them to, in a polite way, challenge or question it. And I'd like to see the reaction.

[00:36:57] If the reaction is, why are you questioning it? Maybe that's starting to break down the barriers of having a dialogue. But to me, it's not about selling the book or going to this website. Sure, you can go to Amazon, but it's really about starting to use those skills that we've suppressed, the critically thinking, the challenging, the questioning.

[00:37:15] Um, when you see a number on the news, think about, when is that? What situation could that not be true? What information would be relevant that's not there that could change the answer? Um, what assumptions am I making? This might have been true 10 years ago, is it true now? I think if everyone spends a couple of minutes with every decision we're gonna make, we're gonna be a better place. Um, so that's really my call to action is don't just treat anything as universally true, um, challenge it. Respectfully challenge it.

[00:37:46] Ed Watters: That's the key, respectfully. You know, you don't have to put people down or, you know, consider them irrelevant in any way. We're not deplorable, we're asking a question. It's simple. So we, we have a lot to, uh, discuss about data and how to use it, but it doesn't really work until you put it to use. You have to learn about data and data, like you said earlier in our conversation, It just blows people's minds and they think they have to be a mathematician, scientist to understand data. Where it's all around us all the time.

[00:38:34] Thank you so much for being here. I love what you do. Oh, one more thing before we go. I noticed that you, on Oregon University, the University of Oregon, you have a webinar. You talk about this through various means. Where can people catch webinars and find out more about that aspect of it?

[00:39:03] Kevin Hanegan: Yeah, absolutely. Thank you for mentioning it. So I do it at organizations, but also at universities. So, I do have a website, it could, you know, trying to improve it over time, but if people go to kevinhanegan.com, there's links there that show the upcoming webinars that are live as well as some of the on demand ones that have happened that they could listen to as well.

[00:39:23] Ed Watters: All right. Uh, I want to say thank you for sharing with us today, it's great what you're doing. And I think people really do need to be a little more literate on data. Thank you, Kevin, for being part of the Dead America podcast today.

[00:39:40] Kevin Hanegan: Absolutely. It was a pleasure being here.

[00:39:45] Ed Watters: Thank you for joining us today. If you found this podcast enlightening, entertaining, educational in any way, please share, like, subscribe, and join us right back here next week for another great [00:40:00] episode of Dead America Podcast. I'm Ed Watters your host, enjoy your afternoon wherever you may be.

Links

A New Model for Decision Making | Kevin Hanegan - Data Literacy Expert

Facebook

(33) Kevin Hanegan | LinkedIn

Kevin Hanegan (@KevinHanegan) / X (twitter.com)

(10) Data Literacy Project - YouTube