#188 – Solving Problems with Data With Kevin Hanegan

Thomas Green here with Ethical Marketing Service on the episode today we have Kevin Hanegan. Kevin welcome. Thanks pleasure to be here. It’s a pleasure to have you, Would you like to take a moment and tell the audience a bit about yourself and what you do? Yeah, absolutely. So I worked for data integration and analytics company called click and I’m the chief learning officer there and we help customers as well as our own employees make better use of their data trying to drive from data to insights to value. Um I also teach at a couple of universities around data data literacy data analytics and work at the data literacy project which is on the advisory board trying to help advocate the world to be a more data literate place. Um Yeah it’s a great introduction as I said previously um there’s a there’s a bunch of stuff I want to talk to you about but I think the theme at least that I picked up was you’re an advocate of people being more data literate. So if you were to summarize that, how would you describe it?

Yeah, it’s a good question. I think you might get many different answers from different people when you hear that and that’s one of the challenges is there there’s sometimes a misconception. Data literacy is about data science, it’s about predictive analytics and then there’s a misconception that data literacy is about the analytics and the technical side but in a nutshell data literacy is for however you work with data in your role, how can you maximize the transition from data to insights and values. So meaning if you’re working on the data prep side you have a set of skills and mindsets that are needed to to prep and transform the data. If you’re consuming the data like you already have someone build a report for you when you’re trying to interpret it, you need to understand at a high level What the data is, what it’s telling you. So for example if someone gives you a statistical report and you have like 95% probability that’s not 100% it’s that whole spectrum.

But I think the last misconception is yes there are skills involved, you have to understand data, it’s not a technical thing but a lot of that process of making decisions with data involves human skills and soft skills like challenging your assumptions and mitigating your bias and listening to diverse perspectives. And so it really is a competency because you need some of those skills but then you also need that those mindsets that growth mindset to be humble enough to know when you don’t have the right opinion and you need more to know that your hypothesis could be wrong. All of that to me falls under which is why I love it because it’s part technical part soft skills. I think I read before in prep for the episode that being able to read data is not looking at a grass. Did you want to maybe elaborate on what the where the skills shortages there? Yeah, it’s, well, you know, so my background is actually math and computer science and I think part of it is our the way where we grew up.

So I went to, you know, many years of school math and there’s never a situation where two plus two didn’t equal for. Um there was never a subjective answer to a math question. And so then you hear the word data and I think a lot of people associated at the high level with numbers and and think that it’s all facts. It’s all subject, it’s all objective and the so you said the bar graph like that is the first step in the process is alright, let’s plot our sales by region and let’s put it on a bar graph because it’s used to show comparisons. But what is that really telling you? It’s not really telling you anything. You need to then take that fact and turn it into a insight and kind of answer that. So what question? And so sometimes what happens there is that’s where the thought process comes in is maybe that the challenges are sales are down, but maybe that’s not the right question. Like maybe our cumulative units sold are down. But maybe our profits are up and that’s really what we care about. So when we look at the chart, if we don’t know what the question is for the right question, we don’t know how it’s tied to organization.

We don’t know what it’s telling us. You see on the news a lot with this misinformation which you know, and sometimes it’s deliberate in the business world. It’s not always deliberate. It’s you have a data point that is true and we’re told to believe it’s a fact. It’s an objective point. But our interpretation of what that means to us in the so what is subjective based off of our experiences. And so that’s why charts are useful as a starting point. But it’s just a starting point. You don’t have to interpret what it means. And is that really trying to tell you to answer the question you’re asking, You mentioned in one of your answers about some of the biases, what would you say is the most common bias that people have when concluding from data tough question, I would probably say confirmation bias. So confirmation bias is the idea that you see a data point conforms to your preconceived notion of what the what the answer is and you’re like, I’m done. So you look at the opposite of that. You look at science, you look at legal and medicine, you go with science and the scientific method, you have a hypothesis and then you do everything in your power to try to disprove that hypothesis?

And then when you can’t disprove it, you have to assume that it’s true in business. We see a data point and it’s not our fault. It’s how the brain works. And we’re like, aha, I told you that was the problem. We don’t challenge it. We don’t question it. We don’t say when is there a situation where that could be misleading where that number is true, But it’s not telling you the whole story. We we don’t follow processes and business like you do in science to help us there. So the confirmation bias, what probably happens daily to too many of us 34 or five times a day. I think other things we see a lot is more of the availability bias. The more we’re exposed to something, the more we believe it to be true. And so we’re very influenced from a marketing perspective. You know, sometimes on the on the better end we can use it for for good things. But then sometimes we just build this mental model based on what we’re exposed to. And that’s again one of the reasons I’m passionate about diversity and inclusion because you’re breaking down those siloed mental models of everything’s right because that’s what I’ve seen, knowing that the world is a much bigger place, there might be other perspectives that are better than mine.

Um, and that bias creeps in every day as well. Have you got any thoughts on sample size in terms of how big or in terms of making conclusions from perhaps a sample size which isn’t. Well yes I mean so we’re we’re giving a lot of people access to data now and I don’t think everyone has the proper training. So this goes back more of the technical skills. Like I’m not saying everyone needs to be a data scientist but maybe you partner with the data scientists or maybe you partner with someone that can do inferential statistics like inferring. Um But if you don’t have the right sample size or you don’t use the right type so you don’t do a random sampling. We see a lot of news situations and outlets where we get surveys that say you know 95% of the respondents voted for a over b. But to your point when you look into the sample size, it’s not a true demographic. It’s not so much that there’s not enough people in it, but it’s not reflecting the reality of the situation.

It’s not randomized enough. So we draw inferences from it which are less than ideal because it’s not a true reflection now it’s not an exact science but we saw that back in the U. S. Presidential election a few years ago where no one predicted trump to win and it ended up being the population and the sample sizes were incorrect and the people that knew that were actually predicting that trump would win. It all came down to the populations and the sample sizes and the people within those that they measured. You also mentioned that some people do it on purpose and some by accident. Have you have you got a an opinion or a data based approach on the should we say the breakdown between those two? Yeah. I like to believe that everyone is good by nature, right? But you know in reality I’ll split out between like the world where we live in in business. Obviously everyone has built in into their DNA. They want to succeed, right? They want to protect themselves. They have the lizard brain and so it’s let’s protect everything at all costs.

So I think to answer the question it’s there’s a fine line between is it deliberate? Is it not deliberate? There’s something in the middle that is it’s probably deliberate in your brain but you’re not consciously aware that you’re doing it. It’s your brain’s way to protect yourself. It’s the way that you were brought up. It’s the way that you perceive threats is you want to use the data to help yourself. That’s just human nature. Um But that doesn’t make it okay. Right. We still have to challenge the data. We still have to mitigate our biases. But certainly you see it all the time. There are companies that use you know availability bias for for advertising and marketing for for their benefit. Right? But that’s not really unethical. I think it’s where it’s unethical. It’s where you get into some of the things you see in news and politics, which we don’t have to get in today, but they’re using it for much much more nefarious means, I should say. Yeah. One of the examples that sprung up for me was the apprentices on at the moment in the UK And they did some market research on a particular task and split into two demographics and the demographic was boys.

And there was four boys of which two said they would buy the product. So the statistic that they gave to when they were pitching was 50% of the demographic we’re going for our and it was, I don’t know whether that was an example of intentional or not. As you say, maybe just trying to do what’s right for me without really being too conscious of it. But it brings me to my next point, which is one of the things which I think that you’re happy to speak about, which is understanding how the brain makes decisions. Would you like to open that a little bit? Yeah. So we have we have these things but again, without getting into a psychology background, we have in our brain these things called heuristics which are the brain is a massive pattern matching computer and were exposed to hundreds, millions of inputs every second. If you go through the four senses and the computer would overheat if we intentionally thought about everything coming in and what it means. So unconsciously without us knowing or subconsciously all that input is filtered through our brain through a level of filters.

Um one of them is heuristics which helps us make shortcuts for decisions shortcuts, so we don’t have to think about it. So if we’re walking down the street and there’s a crane above us and let’s say it has a piano that it’s loading into a skyscraper. We don’t sit and calculate the probability of the wind having it fall and then land right on us. We just moved to the other side. Usually that’s us not thinking that’s the heuristic and it’s helped us for evolution to survive back when you know, we were fight or flight just trying to survive now, we’re in a world where the world changes every day and what was true yesterday is not true today. So these shortcuts are really, really good. The problem is they’re using your previous experiences. So one if you’ve never been exposed to something new, you’re going to have a first response of, I’m going to protect myself which which isn’t open and diverse and inclusive. The second approach is if if you’re in a situation where you’re stressed and there’s too much information, your brain is going to flip into that quick twitch, let’s make a survival response that might not necessarily be accurate.

So when we talk about people using gut intuition versus decisions, I think intuition gets a bad rap intuition is not bad. It’s your brain going into everything that’s happened to you in your life and quickly looking for patterns and saying you don’t even consciously know in 10 milliseconds. These five incidents happened five years ago and this was the outcome and this is what you should do. And then, you know, the answer, the problem is things change and so that’s why we’re victims sometimes of our environment. It’s hard for us when we get exposed to new things. We have to consciously move out of those mental models and the good thing is that is the brain is malleable so we can change those perceptions. But it means we need the growth mindset, it means we need to work in an inclusive way and get different perspectives to help us, you know, remodel those, so to speak, doesn’t make me think if it is the case that’s happening, that the brain is automatically recalling based on previous history, how much you want to in the future shall we say shape, who and what you’re exposed to based on that.

Have you given that any thought I have? I mean, I think about all the time when, you know, we live in a community that is, you know, everyone is for the most part similar, but I obviously, you know, one of the things my wife and I talked about, we have four kids. I want them to study abroad because I was, you know, not exposed to many things when, when I grow up, I want to have them take jobs where they travel the world and as early as possible see other cultures. I want them to be out of their comfort zone. But again, it’s against human nature. A lot of us don’t want to be out of our comfort zone. So I think that’s the best thing you can do. And it’s a lot, it’s not impossible doing when you’re older, but it’s a lot easier when you’re younger. So it’s starting at the kids level and trying to find schools that are inclusive, trying to find schools that teach different ways to think, you know, allow you to learn critical thinking. One of the things we talk a lot about with, with my families are our kids are being taught how to pass a standardized test. They’re not being taught how to question or critically thanks. And it’s, it’s a different mindset. So there’s a lot of stuff we have to do at the schooling level, but I completely agree with you getting them exposed early as people, One of the previous things you said about working, with shall we say incomplete data or maybe not making conclusions based on that because there’s not 100% makes me think of a quote that I heard from one of the big tech guys.

I think it may have been facebook um about how if you more data will always be the solution. So from my perspective I think making a decision based on data is better than not making a decision based on data. You know what I mean? So what’s your approach there or what your thoughts when when that quote? Yeah, it might have been Jeff Bezos. So he did something in a public filing or annual stockholder’s report that said look we we can wait forever until we get data on paraphrasing but we’ll never have everything. So when is the right decision? And I forget what percentage he made. But you know he classifies them into two different types of decisions. One is a type where it’s almost operational and tactical you can revert back from it. So it’s it’s not a big deal. If you make the decision and then realize with more information and more data you have to pivot the other one are more strategic type decisions like we want to change our entire business model and I think he puts a percentage against each one but they’re not 100% there like 50, And what he said, I firmly agree is That’s not the same percent for everyone.

You as an individual and an organization have to figure out what you’re comfortable with. But the point was it will never be 100% or by the time you answer, it the business has already changed On the flip side. It should never be 0% because then you have no data informed decision. It’s it’s just going on guests which is not good. So you you kind of have to classify your decisions at the organizational level and put that percentage next to it of what you’re comfortable with. Of course he works in an environment and a culture where they embrace it’s okay. I don’t like to say fail. I’d like to say that they fall but they learn from falling. There are some organizations where people are so nervous of making the wrong decision. They won’t and that’s what stifles them. So saying saying all that it’s also important to be an organization where the culture supports that all the way from the top down. Have you got any thoughts on because my audience is typically business owners um What to what degree in typical business? If you can even say that maybe an average um are owners making decisions based on data or based on intuition or perhaps biases.

Yeah. I mean through click where I work in the data literacy project, we’ve done surveys every couple of years around these things and there’s definitely a more awareness with business owners and executives that they need to make data driven or data informed decisions. Um they realize the value of it. However they also acknowledge that many of them are not doing that today for a variety of reasons. It could be that they don’t have access to all the data. It could be that they don’t trust the consumers with the data. It and that’s not just they don’t trust them. It’s that the people consuming it might not have the right training where they might not have the right technology or the tools. So I think we are seeing kind of uh evolution of now people realize they really need to use data in all types of businesses. I mean the local pizza shop, political Subway shops, they all can benefit from the data they seem or say data, it’s not just quantitative, it’s also qualitative using anything unstructured in social media. So I think more and more realizing the value but that’s kind of what we’re trying to solve is there’s that gap there too.

Okay. How do I do that? Um And very few few have been able to actually cross that chasm of actually being able to leverage the data and the right, more optimal way to help them make those decisions. Have you got any thoughts on what the typical gaps are? Yeah. I mean it looks if you look at the data pipeline, you know, you start at the data level, it could just be that maybe you don’t have the right data governance if if you’re you know, in a certain business where information is sensitive due to G. D. P. R. Or whatever. You don’t want to expose this to all of your analyst. So it could be that level it could be that the person who’s trying to make the decision says I don’t know where to start. I don’t know where to get this data, I know what I want to do. I want to look at the social media tweets and look at my sales and see if I can find insights of how I can better market. They just they don’t have the data strategy. Um some of them have that but then in the middle they don’t really have the analytics process and and it doesn’t have to be predictive. It could just be a customer 3 60 B or what what is my key demographic.

They don’t know how to answer that. There are more people you know high school secondary school kids coming in to buy my food or is it more elderly or is it more you know middle aged workers that come in at lunch, They don’t have that visibility. And then the final end is they might not be able to take those analytics even if they have them, they don’t have the right mindset to challenge what it’s telling them. They’re nervous that I I see this you know it says last week to your point that you know 75% of my uh people that ordered French fries are high school kids. But if only two people came in. Do you really want to make an insight from two people they need education on data literacy to help them with those challenges to know what they need to do and so I think those are kind of the big things across you know loading the data all the way that you’re finding that insight and then acting on that insight. Have you got any software that you should we say prioritize or is a favorite for you to do? I mean I work for a click right? It’s the data integration data analytics software. So it follows that entire pipeline where we have tools that can help you integrate your data, bring it in in real time in the right format.

So it’s you know analytics ready and then we have the analytics tools so people could do kPI S measurement frameworks, dashboards all the way up to advanced forecasting and predictions. Um and then ways to communicate it out and make alerts and notifications so certainly unbiased but I think click is the best tool out there that covers the whole pipeline for sure. Um And then we also offer training on data literacy which is the soft skills and the tech skills around it as well. So good for pointing out your bias there. Well done. But there was one thing that I wanted to ask you about which is thinking fast and slow. Our internal operating system is that a reference to the book or is that Absolutely yeah. So deem a conman and others the most diverse. They came up with all these experiments and highlighted these heuristics which is the thinking fast, you know, aspect of it where we consciously follow representative bias or availability bias or confirmation bias all the time. Um and then they give us strategies of how we can flip that.

So instead of we talked about the thinking fast is you’re not actually consciously thinking, you just act sometimes when you have to make a strategic decision, you don’t want to do that because it’s a big decision, Things have changed and then you want to recognize am I making a thinking fast decision? Am I making a thinking slow decision? Is it that instant subconscious or let’s take a step back And that’s why sometimes when you hear people say, you know, let me sleep on it. That’s actually based in science because you’re trying to take the emotion out, you’re trying to consciously think about it just like in kids school work, I always say they, you know, my kids hate it, but matt, they have to show their work. It’s because it’s making him not rush. And my son used to make stupid decisions. He would do a multiplication table and always get it wrong. That’s him thinking fast. He’s not processing. He’s not checking. He’s not questioning when they write it out. He has to question it and then as a, as a teacher, as a business owner, we can see where their assumptions might have been flogged. So thinking slow is saying this is a big enough decision.

I’m not gonna go just based off of that? Thinking fast, fast twitch. I’m going to take a step back and I’m gonna follow some processes to make sure I connected the dots and everything makes sense and then make the decision. Have you got any examples in your own sort of personal life where you sort of say, well if I, if I didn’t have these skills, then that would have been significantly more difficult for me. Anything come to mind. Yeah. And I share this one a lot because it’s, it’s a personal story and I think that’s the key thing with data literacy is if you can explain it and get people to connect to it on a personal level, it’s a lot easier than using a bit. So I could give you a business example where we’re looking at sales and everyone was like, oh my God, our sales are slumping but turns out we just had increased, we had a discounting problem. We were discounting too much, but you can’t connect to that. So again, I have four kids, one of them is has autism. So he has a lot of behaviors in school. And this was like the light bulb moment for me, which is where I got into data literacy in the first place at a team meeting. They captured all of the data of the behaviors.

So what behavior was he having? Um what was the consequence, What did they do before? And we were talking to the school and we were concerned that they were gonna kick him out and go to a different school because all of these behaviors and as I said before the data doesn’t lie, facts are facts, their objective, these behaviors happen. But I took a step back and like, alright, I want to see more data. So it’s it’s challenging what’s there. I want to see the timestamps because my hypothesis was, well if it’s unstructured time like right after launch or it’s monday right after a weekend, those are the times that that he tends to act up and then we can focus on those unstructured time. So we get the time stamps. Um And and sure enough it’s not related to the, it’s not right after recess or lunch, it’s not right after. But I’m looking at all the data and one of the data points was what’s the consequence? And the consequence was more often than not, he would go to the principal’s office. So instantly I get the smile and it was like this is serious stuff. Why are you smiling like your son might be kicked out?

And he said you have an assumption here where your data is right but it’s your your interpretation is wrong and we’re talking about it and said your assumption is that kids don’t like going to the principal’s office and they’re like no one likes it in the principal’s office. And they said, I know my son, he loves adult stimulation. So went home test hypothesis asked him like dad, it was great. I kicked the teacher and they sent me to the principal and she read to me for an hour tomorrow and see if I get two hours and it was that lightbulb moment and that shock and awe when I showed them and their faces were like that the data is the data. It doesn’t lie. It’s subjective but your interpretation of that is marred by not just biased but assumptions. They had never seen a kid that didn’t not that like going to the principal and that was that aha moment to me that this happens in business every day. We make decisions based off of what we perceive to be true and there’s no way that that’s always an absolute truth. There’s always other perspectives that will be contrary to that.

So we need to then go and try to find those other police and use the hypothesis of the scientific method? It doesn’t make me think a lot about incentives. So like are your incentives all in the right direction. Does that make you think of any examples at all about how you’ve changed the data based on incentives at all? Well, absolutely. It’s you know, we do a lot. Again a click and at the dealership project about helping organizations find the right key performance indicators because one of the best ways to, to be data literate is to make sure that what you’re looking at ties to the organizational goals. So at a high level, what are the organizational strategies, what are the right key performance indicators? And then what are the right sets? So you have lagging indicators, which is like the results. So I have a sales revenue target, but then you can’t really measure to those, those are just, those are the outcomes. What you want to measure is what’s driving me towards there. And those are the leading indicators. So how many sales calls am I making? How many opportunities do I have in the pipeline?

How many marketing campaigns do I have running? And so we can visualize that all in dashboard. But to your point, there are many times if you pick the wrong actions and indicators, it has unintended consequences because people aren’t thinking systemically. So one of the famous examples, which isn’t a business one, but it was called the Cobra effect. Again, it’s a non business one because it helps people understand um, there was a cobra problem in India hundreds of years ago. And so their decision was let’s put a bounty on the cobra’s and every time you bring in a cobra will give you the equivalent of $10 and rational thought process. Right? Let’s let’s bring all those back and fast forward six months, the Cobra infestation tripled or quadrupled and everyone’s like how is that possible? It’s because the KPI was became a measure that then people were working towards. So they started building cobra farms and farming progress so that they could then wait till their maturity, bring them back into the police station and collect their money.

They turned it into a money making machine which again is rational and people need money and it’s it’s a logical process. But it highlights if you picked the wrong KPI s, you’re going to drive the wrong behavior. So if you’re a support center has has an objective compensated or an incentive to how quick do you handle the ticket? Do you think they’re really going to go the extra mile and help the customer if it takes longer or if your transportation company is tracked with how, how many times there on time. If there’s a reason why a bus or a train or a plane is always late, they’re going to cancel the service because then it’s not considered late but it doesn’t help the consumer. It doesn’t help their business because then people are mad and frustrated. So absolutely sent to incentives play a huge part in this and that’s where we need to balance it with that systems thinking of we fix over here it’s going to impact over here and let’s let’s make it in a balance and then we don’t tend to think systemically, which is another kind of challenge we have is a that is an interesting example you gave the still still recovering from that one.

Um, there is one that I want to ask you about which is I think would be beneficial to the audience, which is the 12 steps, 12 steps to make smarter better business decisions you want to if you don’t know it off by heart, perhaps list a few of those. Yeah, absolutely. So that the thought process was we went through a lot today when we realized that there’s so many things that you can kind of get stuck in the weeds with in terms of how do you make data informed decisions? And just kind of just to clarify, we say data informed. Many people say data driven, but I like to go to the extreme data driven to me means you’re blindly following the data, you’re not using your human intuition. So the most exaggerate example is the urban myth that someone drives off a cliff. And they asked them why and they said, well, the GPS told me to that’s data driven. Data informed is well, I stopped because there’s a clip, I probably die. That’s not a good thing. That’s data informed. So I we thought it was a good idea to come up with this structured approach that’s two things.

It’s systematic and it’s systemic and so systemic means we’re thinking about the whole organization. It’s not just the opposite of that is working in a silo. So I fix my process. It breaks the rest of the order. But I don’t care systemic is let’s look at and balance and systematic means it’s repeatable so that we can follow it every time. And the process goes through the stages of taking data and making the decision. So it starts with what is the question or the problem trying to solve? Um, and a lot of times it won’t go too deep. But a lot of times the challenges we get a question that is not a good question to answer. It’s like how are my sales doing? I don’t know what is good look like compared to when, what demographics. So you kind of have to learn the art of questioning and that’s something we don’t really do too well is pushed back on our bosses whoever that ask us those questions. You have to ask the right question. You have to um categorized as we talked earlier. Is this a decision that strategic that we can’t go back from?

Or is this a quick decision like what do I have for lunch where I can use some of that past switch approach. Then we have to go to the acquire and and find the right data that’s going to help us answer that And that’s where we really have to think systemically what are the drivers of this outcome. So the example I gave was sales revenue. We have to look at marketing campaigns. We have to look at what is our pipeline. We have to look at what our close rate is. All of these data points need to be thought of systemically and then we need the data to be in the right forum and make it analysts ready. Then we go into analyze where it’s okay. Let’s look at those dashboards and a lot of people will use dashboards to say yeah, my sales are down. Well that’s great. But that’s not helping you. You don’t know why they’re down and you don’t know what to do because of it. So it’s going from descriptive all the way into diagnostic, which is root cause analysis. Oh, it’s down because we changed our discounting policy. We no longer need approvals and now everyone’s discounting 70%. Well then our action there is let’s change the discount policy. It has nothing to do with marketing at that point. Um, so we go through the different levels of analytics.

You come up with an insight. You come up with the result of an Inferential statistics like you mentioned the sample set or you have a confidence interval then you what you have to do is think and challenge. So in the apply step it’s when could this number be misleading. What am I not thinking about? Did I get a diverse perspective? Am I mitigating that I might have confirmation bias. What are my assumptions? I’m not thinking I’m going through and quick but there’s a list and it’s prescriptive and then you try to say, okay, here’s my answer. I’ve gone through everything. Here is my answer. Now. If this is just for me I’m done. If it’s for an organization, I have to go and communicate it. And a lot of times what fails is it’s a big change management. So if your boss says we’re going to pivot and go from A to B, you need to educate them in data. What’s in it for me? Why are you doing this? So it’s, it goes to the process of how much evidence do we show? How much information? How much best practices, identifying the stakeholders. And then the last thing is the assess phase where if it’s going to be one of those fast twitch where we can fall, we just want to learn from it, but we need a process to learn from it.

What went wrong, what went right? How does that change my mental model And then let’s go back to the gas phase and see if we have to iterate through it again. And so it gives you this this overarching approach where you can go through the different steps, um, make a better data informed decision and then iterate through it as needed. After you keep assessing thank you for the answer is a great answer and thank you for summarizing it you mentioned about the scientific method and there’s been a couple of examples where Teresa human error has come into it. Have you taken the scientific method and applied it to any of those 12 steps at all? Absolutely. So yeah, it’s it’s thread throughout and the most important one. Well they’re all important but when you get into that you’ve done your analysis and then you’re trying to the apply phase where you’re applying the bias, mitigating the bias, the assumptions, getting diverse perspectives. The whole goal in there. There’s a couple sub steps is when is this insight not true? When can this insight not be true?

Um One of the tools we use is something called the ladder of inference where you take a statement and it goes all the way up to an opinion and then how do you work backwards to find out where you potentially had an incorrect assumption? Um And one of the famous examples is um this cartoon, I can’t remember the lady that drew it, but the girl brings her boyfriend home to dinner and the mother makes a cake and the boyfriend says, oh that cake is interesting. That’s that’s what he said very interesting. But then it goes and it shows the daughter, it shows the mother shows the husband and shows the guy what did they infer from that? And so the mother was like oh my God, he hates my cake, how rude! Get him out. And the daughter was like similar. I can’t believe he said that, but you follow the steps of their thought process how they got there and their actions were get him out of the house and then you look at his thoughts and he’s saying oh my mother makes it with apples, you made it with pears, I like pears better. It’s interesting. So that inference was completely different. You know response of interesting being good to interesting being negative.

Um You need to challenge it when could interesting be seen as a positive? Is it always negative? That’s using the scientific method. And then if you can’t think of a scenario then you’re like maybe yeah maybe he’s being rude but it’s it’s the whole process of questioning the insight to validate if there’s any situation you’re not thinking where you’re misinterpreting it. It sounds like a little bit like Socratic method definition of terms. So we can be speaking about the same thing but have a completely definite different definition for a word. Exactly and you see that in business right? Like we said how are my sales doing? You could define sales as units sold. I could define it as profit. Probably five other definitions and if you don’t have the right definition you’re gonna have very different answers in terms of because I think it’s a really interesting conversation and yeah I think I think your work is quite cool. Do you enjoy it? I love it every day is different. Well you know I I do have A. D. H. D. So I I like to be all over the place and I think one of the things about data literacy is you get to be technical certain times you get to be used psychology sometimes you get to learn more about how the brain works and every situation even though there’s a common approach is different so that I like that and I do feel like we’re making a difference because everyone Can benefit from data.

Um even you know just as the listener, if you think about you’re probably making 20 decisions with data every day, you don’t even realize it data. Some people think of data as numbers. It’s information, it’s evidence. It’s the same thing you hear on the news it’s the you know I’m not going to buy the car, here’s the number. All those things are decisions where this can benefit and it’s not just the oh my God I have to do predictive modelling on which car is going to be the best car based off. You know it’s like what do I want to do? Why is this telling me that? Are they lying? Are they telling me this information? How do I challenge it? Like to me that’s that’s important in life that’s a lifestyle. Do you find that the more you know about the topic, the actual in some instances the more difficult it is to make a decision based on everything that can go wrong I’m going to say yes or no, but I’ll clarify yes, because the more you notice something again, you unconsciously or subconsciously you’re cocky. You don’t mean to be, but you feel like, you know, this is the mental model, a mental model is how you see the world, you feel like you’re confident that so you will do more of those fast twitch decisions and it’s good that, you know, that, you know, everything systemically.

But sometimes what that means is we forget to follow the scientific method because we have that. So my example I gave with my son, if I didn’t know that my son liked adult stimulation, who knows an adult, we never would have gotten there right. We wouldn’t have solved it. So absolutely needed to be close to it. But sometimes, like, let’s look at the other side, the school and the teachers, they were so close to it. They just basically, he didn’t even have to think like there’s no way a kid likes adult attention, like, so they were too close to it. So yes, and no, it’s it’s all about balance. But that again, is why it’s a team sport and why diversity inclusion is so important because your view is going to be very different than mine. And I can’t be closed minded to your view because it’s your view and yours quite honestly, potentially probably more important than mine and more relevant and more actual. So it’s like you’re building this puzzle of the world, And I’m one piece, why wouldn’t you want to see the 20 other pieces to know where it fits? Why do you just want to focus on yours? Bring up an interesting point?

Because it perhaps gets to how you deal with people who who make decisions based on opinion rather than data. Because there’s a couple of things, there’s, shall we say, being tolerant, meaning, being patient and perhaps seeing things from other people’s perspectives. And then there’s perhaps the objective side of it, which is actually, you know, this is not a debatable point. This is just a fact. Have you struggled with that at all based on what you do? Yeah, again, it’s a lot easier to improve when you’re younger, but we see it a lot and you know, we try to teach, it’s about education. We try to teach people like you mentioned discussion when we’re making a decision. I don’t want to have a discussion because the discussion is basically a debate. All I’m doing in a discussion is I’m trying to get you to agree with me, I’m not listening to you. I’m not gonna I might say I am, but I’m thinking of everything you’re saying. I’m thinking how I’m going to counteract that. Whereas if you come into it with a dialogue, the whole point is you’re not you’re suspending any assumptions, you’re listening to the other person and seeing how that fits into your model.

And so that type of education of dialogue versus a discussion is critical. Also it’s about having the right skills as a facilitator or a coach to challenge them. Ask open ended questions. You know, one of the biggest things I always talk about is show your work. I said we do with our kids. So this was your answer. Tell me why you came to that conclusion. And by doing that, they’re going to talk about it. And what will happen more times or not is they will expose something that there is an implicit assumption they think this to be true and it’s not. And then that’s where you can say, oh, it’s because you’re assuming that your assumption is that our customers are like this. Well, did you know the past two years, our customers are completely different. And then it’s like, okay, then you can have that dialogue, but it if they just give you the answer and that’s it and it’s closed minded and there’s no dialogue. There’s no open ended questions. It’s really tough because people do not inherently like to be proven wrong, especially in a business setting where there’s a culture and your boss is there like we sometimes make poor decisions because we fear the groupthink approach, we fear of going against the norm and going against our bosses.

Well, it’s one of the things that you’re happy talking about if I’m not mistaken and I did want to ask you about it. Groupthink. Yeah. So it’s I mean it is a form of bias but Groupthink is also more cultural, right? So a bias to me is very individual. Like a confirmation bias. I want I see my data point. They prove it. I believe it to be true. I don’t try to challenge it. Groupthink is more a social and emotional component of of organizational decision making. So you have either your manager or you have everyone in the team making a decision. You do not want to go against them even though you have a different perspective. So you feel like you can’t speak up even though you think it’s the right decision. And you see this played out a lot like in the space shuttle Challenger and Nasa it wasn’t the root cause right? The challenger would have blown up. But people all had to go through and say go no go. And everyone was saying go except for a group that had the concerns and with group think you’re like if I say no go I’m gonna get fired or if I get no go.

They’re gonna think less of me. So you don’t human nature is you don’t do it again in the US. We saw it with president Kennedy when he started with the Cuban missile crisis in the bay of pigs and we won’t go into a lot of details. But the first interaction um the Cuban missile crisis we or the um we just started in office and he was getting with his FBI and his military and everyone was like this is president Kennedy. I’m gonna tell him what he wants and it didn’t go well, it ended poorly for us. But then he reflected, he used the assess and he brought in outside psychologist I think one was from Harvard M. I. T. And said what can I do different? And so he set up in the next, you know, potential military issue two separate groups. He had people in each group separate answering the same question. He stayed out of it. So no one said I’m going to go into the president and then he appointed devil’s advocates for each 11 of them was his brother Robert and one of the groups and the output was remarkably better.

It was how they challenge he identified I have group things and I don’t want to have it again. How do I fix it? Um, so again, it’s a summer you can read about it online, but it was a fascinating example of the power of groupthink especially you have the, I mean, would you say no to the President of the United States and tell him he’s wrong. I wouldn’t, but at the same time would you say no to your boss if you’re in an organization where culture, you know, says don’t challenge me. So it’s a big problem, but it’s it’s a blend between individual bias and more about your bias. But it’s not because of your brain, it’s because of the social emotional you know, fear of of the organization shutting you. These types of conversations remind me a lot about philosophy. Um you heard of the is your problem? No, well I might have but I don’t remember at this point. So it’s um you can’t can’t get an ought from an ear. So there’s there’s the data and then how you should act on that data is is basically up for up for debate in a way. Absolutely. Data is objective.

Your insights from the data is subjective. I guess you need some sort of you need some sort of goal to work to in order to make the decision. You know some I don’t know maybe that’s a bias of mine that just comes to mind. But you know I have to check myself on these things after this conversation. You know you’re right. I mean there is a form of analytics. Exploratory data analytics which is like here’s a haystack go find a needle. But a lot of times, you know there’s value in that but 90% of what we’re gonna do with organizations is you have to have a goal of mine it has to tie to an organizational strategy, it has to tie to those alignments. You have to have the right KPI s that you can drive towards. But there is that find the needle in the haystack approach and that’s used many times where you just want to learn things that if if your boss comes to you and says here is a terabyte of data, go find me an insight, you have to work cut out for you. The best thing would then be questioned. What are you trying to find? What are your goals where our numbers down? What are your KPI s, are they turning up and down? And I think a lot of times we suffer from not just group, think we don’t know how to question what we’re told, we just do it and that’s where we could also benefit from more education on the art of questioning and critical thinking.

There’s a lot of good stuff in this particular episode. So thank you for that in terms of actionable steps. Do you maybe um conclude by saying here’s a, here’s a takeaway for people who want to improve in this particular field? Yeah, I’ll do a small and big one and a small one anytime you make a decision that is bigger than what am I going to have for lunch, you know, which way am I gonna drive on the highway? Something that you know, strategically could change work or life or whatever because I’m not going to sleep on it. That’s cliche and think about why you’re making that decision and actually write it out on paper or speak it to a colleague in showing that work is going to help you bring those fast twitch is out and and look at things that that’s a smaller and the bigger end because I would say use a process, use a methodology. We have one in the book because it’s not the only one google decision science, decision intelligence, decision making methodologies and just adopt one that allows you to question the data.

Um question again, data is not numbers, question the information, question the evidence, be skeptical and it’s not saying that we need everyone to be pessimistic and say no to everything. That’s not what we’re saying. We’re just saying don’t say yes to everything. Think about it. Just use your brain any thoughts on people who sort of see it as futile based on the let’s say data can be flawed and therefore they won’t accept data. Have you got any thoughts there? Yeah, there’s data. Data doubters, it’s really for them they need to adopt a growth mindset because I mean the world is very different than how it was even just 567 years ago and you need this new mindset, this is not something that’s going away. This is not something that you know, if you just close your eyes and forget about in five years, the myth has gone and something else has come like this is with us. It’s only gonna get more and more. And some people say I don’t need to learn these because ai and automation they’re going to take over the world, they’re not Ai and automation are not going to challenge assumptions, they’re not going to mitigate bias, they’re not going to look at the sample size and say from a from a soft school perspective was I inclusive?

So we always need to challenge these things and I think they’re just lacking a growth mindset I guess. And I guess the question then becomes how do we get them to get a growth mindset? I wish I had that answer. I’d probably a billionaire at this point. Um But that’s the goal at least is to get everyone to have that growth mindset. You find that you’re quite rare in the instance of combining let’s say a data approach and also a growth mindset. Um Maybe not rare and data and growth but I think intentionally I’m rare in technical and soft skills. So as I said I went to school for computer and math but then I also knew that that’s only half the equation. So then went to master school for more around organizational development. Adult learning psychology so that I can blend into I think that’s a rare approach but I do see a lot of data scientists that that do challenge things quite a bit and have a growth mindset. Yes it’s just a few people don’t have the soft skills and it’s not their fault. We don’t teach them like active listening, You probably listen 10 times more than you write and speak there right?

But have you ever taken a class and listening? I’ve done an episode on listening, believe it or not. Well, that’s perfect actually, it’s better than most, but in primary secondary school you were never taught listening yet. You spend hours a day taking courses on reading and writing. It’s the most of you. So like I’m saying it’s not everyone’s fault, but we all can change just because we didn’t learn this younger. It’s not an excuse. We kind of need to go forward. So yes, there are some people that don’t have a growth mindset. I don’t think it just maps the data scientists that I think it’s people that just haven’t seen the, the light and the value and and there’s still this time or your goals kevin, um, cheesy, But make the world more data literate, it would be great help people make a difference. I mean, that’s one of the reasons, um, I like doing this is that there is definitely an organization on business impact to this. But everything we see in the news today, everything, everything was changes in society and climate change and political issues.

This approach helps all of that stuff. And so it’s the thing that I can do to help educate people and help people make a better decision that helps the climate helps political situation saves the economy. You know, even if I’m 100 steps removed from that, I’m happy about it. So I I feel good about trying to make a difference. I don’t know if I’ve done it yet, but that’s why it’s a goal. Is it something that I want to reach towards? Well, if people do want to find more, find out more about you or perhaps click where do they go? Absolutely. For me to go to linkedin? Um just Kevin Hanegan or you can go to my website, KevinHanegan.com. Um for click I would just go to qlik.com. Q L I K . com. Check it out. Um and then also I get the data literacy projects. You can go to the data literacy project dot org and find a ton of tools, resources, community membership of how you can help improve your data literacy yourself. Thank you for that. I think you’ve been a great guest and I really enjoyed the conversation. I appreciate the time.