About this Episode: Learn the value of soft skills in a complex data-driven world in this episode of The Backstory on Marketing with Kevin Hanegan.

About Kevin Hanegan: Kevin is a senior leader who likes to use data and analytics to transform, innovate, and continuously improve organizations to make them the best they can be. His passion is the intersection of business, technology, learning, and psychology. Kevin believes the world is constantly evolving and that we should always be evolving and improving ourselves in business and in our personal life. Through many years of working in a variety of businesses and industries, Kevin has been able to leverage technology and psychology, along with data and analytics, to improve organizational performance and transform businesses into high-performing organizations. Kevin frequently speaks and writes on topics such as data-informed decision-- making, the future of learning, and growth mindset. Kevin lives in Massachusetts with his wife Shannon and their four children. 

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https://www.kevinhanegan.com/  

https://www.youtube.com/c/DataLiteracyProject/featured  

https://marketingmachine.prorelevant.com/

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Link to YouTube Video:

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Hi, I'm Guy Powell and welcome to the next episode

of the backstory on marketing. If you haven't already done so

please visit pro relevant.com and sign up for all of these

episodes and podcasts. I am the author of the newly released

book, the post COVID marketing machine, prepare your team to

win. And you can find more information about the book at

marketing machine dot pro relevant.com. Today we'll be

speaking with Kevin Hanegan. Kevin is a senior leader who

likes to use data and analytics to transform, innovate and

continuously improve organizations to make them the

best they can be. His passion is in the intersection of business,

technology, learning and psychology. He believes the

world is constantly evolving, and we should always be evolving

and improving ourselves, in business and in our personal

lives. He is a frequent speaker and writer on topics of data

informed decision making, the future of learning and growth

and the growth mindset. And lastly, he is the author of the

book, Turning Data into Wisdom. Kevin, welcome. Thank you for

being here.

Yeah, it's a pleasure. Wow. Thanks for the

introduction. I hope I live up to all that. That sounds good.

Yeah, I'm sure you will. So what what so tell us

your backstory, how did you get into analytics and learning?

Yeah, I everyone probably says they have unusual

stories. I do think mine's a little bit unusual. So I'm

technical by nature. U ndergrad, I was math and computer science.

And I started getting my jobs out of out of university doing

software programming, actually, one of the programming languages

I used was Ada, which was, I think, dead before I even

learned it more or less, except for a couple of industries that

still used it. But I realized that I started taking some night

classes, and I realized that it's hard to keep up with the

pace of change. And this is still 20 years ago. Now the pace

of change is daily as opposed to annually and I was struggling

with learning the new concepts. In my mind, I thought it's

because there's going to be a better way to teach them. So I

went back and got more education on adult learning-how adults can

learn-which is a little bit different than how kids learn

for different reasons. And it kind of just was this passion of

technology is evolving. I still love being technical, but I love

helping people understand how to how to evolve and leverage the

skills that help them stay current, and continue being

relevant in today's time. And that's kind of led me to write

the book and led me to where I am and talk about data literacy

and data informed decision making. Because it's a balance

of, you know, some technical skills, you don't obviously have

to be a data scientist, but then also many other skills that we

kind of forget about over talent, different soft skills,

like curiosity and creativity.

Yeah, absolutely. And I like your point about

about change, I kind of liked the the saying it used to be the

change is constant, but now change is accelerating. And it's

almost like the the the the pace of acceleration is accelerating.

Because it is just everything is just coming at us like you

wouldn't believe and, and, you know, in marketing, you've got

the multiverse, and I'm sure in just technology and analytics. I

mean, it's just, it's just enormous, how fast that things

are changing.

It's crazy. And I mean, again, going back to data,

technical and learning, one of the things that relates to both

of those with the change, a lot of times when we make decisions,

or a lot of times when we're looking at what's our next

business plan, or looking at some similar, whether we're

using predictive analytics, or whether we're using our brain,

it always goes back to let's look at all of our history,

whether it's what's stored in our long term memory, or let's

look at historical events to come up with a probability or

prediction to make a decision about what to do in the future.

And the problem is, we don't know what's going to happen in

the future. I mean, your book, right? COVID. No one knew people

knew a pandemic was coming, but they didn't plan for it.

Everything materially changes because you don't have that

experience to do the modeling or do the intuition on, and so it

creates all this uncertainty, which again, could be an

opportunity if you leverage it, right. But it could also be

disastrous and doubt.

Yeah, absolutely. And, you know, one thing too, I

think that we get wrong when I think about going back, I

studied engineering and, and I really, I use a lot of math and

analytics, of course, but I don't really use any of my

analysts, my engineering skills. So it's almost like when you go

to college, it's really how to learn, you're learning how to

learn. And so you can be able to learn new things because when

you get out into the real world, it's very infrequent that you

actually use a lot of what you were taught in college,

specifically for your for your job. So So learning is is a

critical component of of a corporation and certainly of

individuals and it's kind of like... yeah, you know, a topic

for everybody to be continually learning. So organizations have

what's called now the role of a chief learning officer. Tell us

what you understand under that?

Yeah, well, like you said, the world is evolving,

there's more uncertainty. You know, I'll date myself when I

was in college cloud was a meteorology class, now it's an

IT course is, so we need to keep our employees up to date, the

skills that they're going to use the technical skills that

they're going to use to do their job. Many times, the majority of

times, they're not learning them in the university. So they have

to learn them outside. Some individuals would go back and do

that on their own. But it's important for organizations to

keep top talent to provide those skills to them and apply it in a

job setting. So you're not just teaching them the theory. But

the benefit of doing it in an actual real life setting is that

you have real life examples. But it's not just technical skills.

Like we said, the technology is evolving. When I think about

going back to school, I when I'm younger, like you, and I have

four kids, they're young, they always ask why they're always

curious. They're always challenging. And we encourage

that that's how they learn about the world. And I kind of feel

like, as you go through the typical schooling in

universities, you kind of lose those skills, you don't learn as

much about critical thinking or collaborative thinking or being

curious. You know, if, when you're in kindergarten, and you

talk back to your parent or your teacher and ask why. Usually

they go along, and they answer when you do that in high school

or college. You're not supposed to talk back, you're not

supposed to question the teacher. They kind of take those

soft skills out of us. Those are needed today because the world

changing, so we need those different perspectives. We need

to critically challenge the data, because there's so much

data out there. People always say, you know, you can make the

data say whatever you want. The data never lies, it's fact, it's

how you interpret it. And we don't get a lot of practice with

those interpretation skills. And so that's a key role for me is

yes, we have to teach the latest technologies and things going

forward. But we also have to teach people how to be

resilient, how to do active listening, how to challenge

assumptions, how to use lateral thinking, how to increase their

emotional intelligence, if not equally, or potentially even

more so than the technical skills. Because like you said,

we learned how to learn in college, we don't learn how to

do the soft skills in college.

That is very true. And I on one of my previous

interviews, and it plays as well with man, even male versus

female, just the kind of the personality of male versus

female and how they approach different questions and how they

are more or less more likely to challenge, are less likely to

challenge and more likely to ask "what do we need this for or

not?" And and I think that's a really good point that you made.

Notice learning is that more closely related to HR or more

closely related to the technical side of the business.

So I click where I work, it's both traditionally

learning was related to HR, as you were learning the

compliance, you were doing learning and development, some

of the soft skills, but maybe skills like project management

or things that were mandatory to take for compliance regions. We

kind of blend it. So we teach, obviously, our product, we teach

some of the technologies that's related to it, we help educate

the internal learning and development, the soft skills as

well. So we'll have you know, monthly seminars on various

skill topics. But I'm not saying that's how everyone does it. I

think it's it's an evolving role. The role has been around

for a while. And I think you're starting to see a pivot from

just focused on HR internal to this broader umbrella due to the

fact that everyone needs to keep upskilling and relearning and

Yeah, yeah. And I think you're right, the maybe

unlearning.

the soft skills are more on one side, or maybe shaded to the one

side, whereas the harder skills of, you know, a specific type of

analytics or a specific type or way to use a software product or

whatever, you know, that that then really applies to the

specific functions of your job as opposed to the how you can

progress maybe and work better within the organization.

Absolutely. Yeah. So now one of the things especially as you

talk about analytics, which is just, it's just exploding, and

the need for analysts is exploding, but to your point

about asking the questions and digging deep into the data and

really understanding how to ask those questions is a big deal.

So tell us about about that. And then also, as I understand that,

you're also working with what's called the data literacy

project. So maybe tell us a little bit about both of those.

Yeah, it's an effort to increase everyone's

ability to work with data. And the key thing is everyone's

different. So let's, let's take COVID as a perfect example.

Everyone in the beginning of the pandemic was inundated with data

and information. I guess taking a step back when I say data,

don't just think numbers. It's also qualitative. It's also

information, it's statements. So we couldn't go five minutes on

news without a chart or data or infographic about COVID, that

there's an element of data literacy, which is for the

creators of that information. How do you create it in the most

logical, rational way to get across an insight, that is an

actual insight. And so you might have seen in the beginning of

COVID charts that showed flatten the curve, cumulative cases,

which was scary, like if the intent was, "Let's scare

people," which it might have been- that that's great. But it

also didn't. Some of the graphs didn't take into account. Think

subtleties that someone who's a proper data analyst would know

like, it doesn't make sense to show cases on a daily basis

because they ebb and flow. And you might have issues where it's

a weekend, people don't go in take test. So they started doing

rolling averages seven day averages, they started saying,

well, wow, it looks a lot worse over here. But when they only

that, that talent only has like 200 people. So it's not really a

true sample size. So you started seeing the graphs evolving.

That's one element of data literacy. But the most important

element is, there's probably a handful of people in an

organization that build those charts and graphs, everyone in

the organizations, and with COVID, everyone in the world

sees them, and they have to interpret them. And eventually

they have to make decisions. So let's say it was the middle of

2020. And you're you had planned a family vacation, you're going

to be going through some thought process. Okay. Do I go on

vacation? If I do, what are my questions? Well, what's my risk

or probability of getting it or someone in the family getting

sick? And then realistically, what's the probability of

someone in the family getting seriously sick or even dying?

You then have to take those graphs and understand them,

understand the insights, and then weigh the probabilities.

And that's the same in an organization. It's not COVID,

its sales data, its marketing data, its lead generation data.

You're exposed to all this information; you have to make

your question. And I think one of the key things that I like to

educate people on is that the data is the data. It's how you

interpret it. And some of that is personalized, you might be

more risky. So you might say, you know, my family...we're all

healthy, we don't have any comorbidities, we're going to

take a chance. We're going to go where someone else might have

some elderly family members. They might have some more; their

decision is different. It's the same data. That's the same thing

in organizations if there's no law. It's all black and white.

And that's kind of why I love it. It is somewhere in between,

and everyone can have a different answer to the same

question. And I would never say this person is right, this

person was wrong. It's all about what their outcome is, what

their ability to handle the probability and the risk is, and

their thought process. So that's why it really fascinates me.

Yeah, absolutely. And I and you know, there's,

there's about three or four questions that that came up

while you were talking. But one was a statement, I used to be

friends with a guy that was was one of the, I think it was VP of

analytics or VP of research at Xerox, and he was the global VP.

And one of the challenges that they had early on when he when

he first started, and then he fixed it, was that everybody was

using their own data around the world. And so everybody was

making their numbers because they had their own data. And

their own data was then based on different ways that it was

collected or researched, or however they got to it. And

everybody was making the numbers, but the company was

losing money. And so I mean...there's two things there.

But one of them was one of the big challenges he had to

undertake, was to homogenize the way the data was defined across

the whole organization. And and that that was that was critical.

And then once he got that done, then it really made sense us

and, you know, when somebody would say, Well, what's your

database on and had to be based on this one data set? And if

they use anything else, then they would, you know, they get

dinged or whatever from that?

Absolutely. It's a it's a common challenge we see

in organizations and some of the time it happens, because we tend

to, and I'm not saying we shouldn't do this, but we tend

to start backwards. We start with, "let's build our data

model." Let's build our data warehouse. Let's organize it,

and then after that, let's go publicize it, and let's take

requests for questions that we can answer with it. I like to

start with the decision and the question first-what are we

trying to do? Because then you can work backwards. It's okay if

you have a data warehouse. But in that situation, when you're

working backwards, you're going to be like, "Okay. What is our

definition of sales, target pipeline, whatever it is." And

it just...it allows you to focus only on that data, ignore

anything else that kind of isn't relevant. And it tends to save

time, because things that are relevant you're not using. I saw

some study a couple of weeks ago that said, organizations that

tend to put their data in a data warehouse, maybe about 30,

between 30 to 35%, of that data ever gets touched again. So

that's 65% of the time invested on putting data that you might

need, you don't actually ever need, or maybe you need it, you

just can't use it. For whatever reason. It's wasted time.

Yeah. And although, and I agree with you about

understanding what the business question is, first, and then

working back. And quite often on data like that, what we've seen

in the in the marketing space, and when you're trying to build

a marketing machine, is you kind of have a feel that well, this

question would be interesting. But we do need a certain amount

of data before we can actually use it. Now that never gets

touched, that's a different issue. But at least you know,

you may need to wait for a year, maybe two years before you feel

you have not only enough time series data, but you also have

the data being accurate, because quite often, especially if it's

manually entered, you know, you could have errors in how they're

entering. And that takes some time for them to learn how to

enter it properly. And then all of a sudden, now you have

something that you can use and and be confident that your data

is accurate.

Absolutely. Just on that, I totally agree. And I

want to clarify, you always store the data, I was thinking

more about like building a special warehouse or lake that

might have some transformations built into it. But the raw data?

Absolutely. Because if you ask a question, and then you look down

the pipe, and then the data is empty...

Yeah. Yep. And plus, you know, you brought up when

you were talking, I was thinking that, you know, you think about

the long tail. I mean, clearly the the, that the top of that,

that, that that chart is, you know, really the important

stuff. And then, you know, maybe you know, a year or two down the

line, when you're starting to granularized and improve your,

the way you're asking the questions or detail out the way

you're asking the questions, then you need the next thing.

And then you kind of have this long tail out there, which is

maybe might be useful in the future. And then it's it really

just depends on at some point what the priorities are, what

have you, and where it might actually become useful.

Absolutely. Well, and that's a good point in

today's again, going back to COVID, everything changed. So

the data that organizations used about their customers, I'm

willing to bet a majority of it not a majority, but some of it

was different in a COVID world. Yeah. And so if they didn't have

that long tail to draw back on, they'd be starting from scratch,

like companies that already had it, even if they weren't using

it there. So, I agree with having the data set, because

especially today, things change so fast. The companies that

pivot quick will be the ones that, you know, makes you

succeed.

Yeah. Yeah, absolutely. And that, that fast

failing or fail fast is critical. I think, you know, one

of the other things on data that we've run into is, a lot of

times people will start to collect data. And they'll use it

for a couple of years, and then the world will have changed. And

then they're afraid to turn the data collection off. And it

might be costly, costing them money, but they're, you know,

they're afraid to actually turn it off. Because, well, I don't

know, maybe we do need to use that at some point in the

future. And I'm sure you've run into situations like that as

well.

We have and it's not always the case. But many

times it comes down to this. I'm generalizing here that we as

humans; we have biases. And there are different types of

bias. And one of the more common ones is business would be risk

aversion, or fear of, you know, change. There's also a bias,

which is the complete opposite, which is what you change even

when you don't want to, but for I've seen that hundreds of times

where people will see the writing on the wall so to

speak...about the new evolution of the business model. And they

just don't change the fear, but they kind of deep down inside,

no, they have to change, they have to evolve, they have to not

use that data that way anymore. But they still tend to hold on

to...and that's one of the beauties of a decision-making

framework and working with different people-you get those

different perspectives for people. If you're comfortable,

challenging, which goes back to we don't learn how to challenge

in school. You're gonna lead to better results if you're in a

team where you're comfortable challenging the people above

you.

Yeah, and I think that, and that is definitely a

skill, but also a, you know, inhibitions to be able to say to

the boss, "You know, hey, listen, hold on a second. I'm

not exactly sure whether we're doing it right or interpreting

it right or if the data is correct. And that, I will admit,

in some organizations-just by the personality of the

organization-that can be very difficult.

It's one of the biggest roadblocks we see is

this culture change. It has happened in certain

organizations, but in many of them, it is hard to stop

behaviors and do something different. And if you've been

brought up in a culture where it's a very hierarchical

organization from a culture, and you're not supposed to talk

back, and that's how you have run up...it's nearly impossible

to just all of a sudden switch and change. Even just...I

mean...My son's baseball team, someone asked the coach, like,

"why are we doing batting practice today?" And instead of

like, answering the question, so they get the perspective,

they're just like, "because I told you so." I see that because

I told you so in business to the point that people stop asking.

And the reason they're asking not every time because there are

times people ask because they're trying to be troublemakers. But

there are times they're asking, because they want to know why.

Because it helps them learn. So then the next time they won't do

it in the next time, maybe they even go early and start batting

practice or whatever they're doing in business, because they

understand why. But you're right is sometimes people don't have

the comfort level to do that. And they usually don't have it.

Because in the past, they've been, you know, scolded for

doing that.

Yeah, yeah, absolutely. Let me go back a

second, though. And one of the problems in marketing, and

marketers quite often got into marketing because they didn't

want to worry about the data. Now, it turns out the modern

marketer that especially is working on the web, or in social

or anything that's digital; there's a ton of data there. And

but there are still marketers that are afraid of data, they

got into marketing, or they got into kind of the that type of

position because they hated math, and they hated data. And

so in that case, how do you put together now? How do you work

with them to help them to really learn how to understand the

data, question the data, and then start to actually get

insights and results out of that data?

It's a million dollar question. Right is what I

try to start with; tell individuals, "you do not need to

be a data scientist, even you do not need to be a data analyst."

Most organizations, most individuals, they need to be

able to consume the data to interpret it. So I think of it

as a puzzle. Each piece of the puzzle is data. And as you put

it together, you kind of have this insight. And what we need

to educate and make people aware of is...how do you...you're

never going to have all the puzzle pieces. Imagine you have

a 10,000 piece puzzle, and it's your business answer. And 200

pieces are missing. You still have to come together. But the

puzzle. So how do you do that? Well, it's those skills we

talked about as you talk to your peers about their perspectives,

it's trial and error. It's...you try some of your challenging

assumptions, you try things out and learn from them. And so what

I try to tell people is there's so much data, the answer to all

the world's problems, we have the data somewhere, we just

haven't connected the dots, and then rallied the change

management. And that's been proven many times. We have those

answers. It more and more people that are open to saying, "okay,

I am not a data expert, but I am comfortable challenging the data

uncomfortable, working with the data. And then when I tell them

as they're already doing it every day. Did you go on

vacation last year? Yep. Well, how'd you determine? Well, I

went on Airbnb and looked at the reviews. That's data. It's not a

number, but it's a word. It's information or...okay...when you

go to the doctor, what do they do? Well, they read out my

vitals, they read out my cholesterol, they read up-

that's data. And then they're telling me what to do.

Everything you're doing today has data and just reframe it as

if someone told you that they were giving you a 10,000 piece

puzzle. When you put it together, you're able to answer

all your questions. Why would you be scared of that? You might

be scared of it, but you should be excited about learning how to

put it together.

Yeah. So you know, thinking about that, and then

maybe some of the barriers that maybe hinder organizations from

getting to the next level? You know, one of the things, one of

the skills I think...especially for the person that considers

themselves non-data...is at least learning how to specify

and really discuss what that business question is. How to

really define that business question. Do you see that as

well? Or?

100% I always say that like...when when you go to

school, you learn in forms of communication, you learn how to

read; you learn how to write. We don't take courses on listening,

believe it or not-we don't typically. We take courses.

Sometimes on speaking, but we don't take courses on the

speaker and how to lecture. We don't take courses on how to

question. And those to me are the most important things is you

have to learn how to question so that, you know, you might have a

question like, you know, how was my marketing campaign last year?

That is not a great answer or question to answer with data,

because if you ask 10 different people, you're gonna get 10

different results compared to what? Compared to last marketing

campaign? Compared to the one this time last year? Across

different channels? Are you looking across different age

groups? What are your dimensions? Where do your

segmentation? What does good look like? All of those

things...you need to have this this framework of asking those

questions, and if someone doesn't know the

answer...figuring out how much of the puzzle...maybe you do all

of that, and you get back, you know, three quarters of the

puzzle...can you make a decision with three quarters? Maybe. It

depends on the answer, right? If it's something that's life or

death, or strategic, probably not. But if it's something more

operational, like "it was a good campaign, let's run it again

next year. let's approve the budget. It's good enough." And

so it really gets back to listening to other people. So

you get more puzzle pieces, and then questioning everyone about

you know what that means to them. And being very specific, I

always equate it to everyone in a corporate setting is usually

familiar with smart objectives. You just apply the same thing to

questions. They have to be smart questions, they have to be

specific, measurable, answerable, driven by data time

bounds, same thing.

Yeah, absolutely. And although, you know, and I

think in marketing, it is a little more difficult than in

other areas of manufacturing or in other areas, it is more

difficult. That is what we do, though, and that's why it is a

challenge for marketers. When they ask, "well, how well did we

do?" Then one of the first things that we do is get into

the data and understand...well...what kind of

data do you have so that we can help you to answer the, you

know, the question that you want, and at the detail level

that you want?" And one of the big challenges we have, and then

supporting that question, is making sure that the data is

correct. So, tell us about what you do to make sure that the

data is clean, is valid. And then, in the end, of course, is

useful to make the right kind of business decisions for the

organization.

Yeah. I mean...obviously tools and

technology help, but a lot of it starts with...we've had

organizations where they don't think about data quality in the

front. So, it's like building a house; you're not going to come

to the lot and start putting a house you're gonna blueprint,

Okay, well, I need windows to get sunlight for Vitamin D. I

need a second floor, so I need stairs; I need a place to store

foods, I need a kitchen. Same thing with this is...I need to

understand what I need. Okay, I need to understand customers,

age groups. So whether I'm doing it through a survey, whether I'm

doing it through it's a membership card...when I get it,

I'm going to store their age. I'm going to store it as a drop

down in the survey. I mean, it could be as simple as, "we don't

allow freeform text because people could spill out a state."

And obviously, there's technology that fixes this. But

it's about designing what you want, what the outcome is,

visualizing the outcome, and then working backwards. And then

sometimes it's using tools, especially if you're trying to

analyze customer sentiment, right? That's not necessarily

easy to do manually. Did someone post on Twitter? And was that a

negative for the company? Was that a positive? You know that

that's where you definitely have to leverage the technology. But

it all starts with... "what is that blueprint?" So, I like to

have organizations visualize the ideal outcome and then working

back but don't think about data quality at the end; think about

it in the beginning. What do you need that data outcome to be?

And that will help you lead to the question? But then it

matches with the question. So, you'd met Lena, one of the

examples I'll mentioned working with one organization...they

asked this question, you know, "what was...how was my marketing

campaign?" And the fascinating thing about it was the question

wasn't specific enough because what actually happens is the

total net sales were actually lower than what they were

expecting. And so everyone was okay. It wasn't...it turns out

when your questions and your challenge and you understood

everything, they actually sold more volume, they had more leads

more lead conversion. What had happened is due to the program

they had put in a discounting policy, so the discounts went up

12%. So if the question was, "was the campaign successful?" I

would argue it was the challenge was the discounting, brought it

back. But if you didn't think to pause and think about the other

parts of the data that are relevant, and you didn't have

that in your data model, you would have not done the campaign

again, even though it was wildly successful. So what they did

was, was the discounting over the top..."was it useful, it's

dialogue?" And then maybe they lower it as much, knowing that

if you lower it...maybe not as many people are going to convert

those that's that kind of give and take, but at least it became

a dialogue, as opposed to saying, "no, we didn't make as

much money so it wasn't successful."

Yeah. Right. And you know, and that's where you also

have to include kind of a whole data framework as to how you're

going to look at that specific business question. Because it

could be that the discounts were right, and that the marketing

was correct. But we entered into a recession or the interest

rates went up, housing starts, you know, slowed down, or

something like that. And so, you know, you really do have to have

a complete data framework that fits in with those... with those

business decisions. And then to your point, as well as then, you

know, not only understanding what that data is that underlies

all that, but then also making sure that it's clean and vetted

and complete...and what have you to really give...you

Absolutely. garbage in, garbage out is the

know...something that's not just garbage in and garbage out.

worst to me because it's preventable, right? It leads to

bad decisions. But it's also very preventable if you do

things.

Yeah, yeah, absolutely. So before we close,

is there anything else you'd like to bring up or mention,

that we haven't talked about?

I think the highlight for me is I just I

really believe that. Solving, marketing challenges,

organization challenges, life challenges; we have everything

we need, except sometimes we as individuals don't have the right

mindset. We don't have the right soft skills of questioning, we

don't have, you know...in science they use the scientific

method. Let me have a guess and then do everything in my power

to disprove it. And if I can't disprove it, whereas what we

typically do, and it's not our fault, it's bias. It's not

intentional...is we go into a meeting with our answer, like,

"well, we need to do this." And then anytime we see anything

that validates that we're like, "that's the answer, we're done."

We stop living confirmation bias. And when someone else is

talking, and they're not agreeing with us, we're not

listening. We're thinking in our head, how we're going to reply

to them and shut them down. And we're not actually actively

listening. So to me, the biggest takeaway is, you, yourself,

families, kids...learn those soft skills. They are more

critical than ever before.

Yeah, it's almost like the universities have to

add another year on to understand what those soft

skills are. Although, what I've seen...I've done a lot of

teaching over the last couple of years at different universities

here in Georgia, and then also in North Carolina, and one of

the things that I found is that a lot of the universities are

now doing these project based learning where you have three,

or four, or six people on a team. And I think that is one of

the ways that they're actually starting to learn how to learn

those soft skills so that they can understand what...you

know...somebody else's biases are, what their issues are, how

to manage the team, how to really define the business

question, and then how to find the data that will then end up

supporting the ability to analyze and deliver results

based on what that business question was.

Absolutely, because you go through school

and you do individual projects, you go to work, you never never

alone. Yeah, so they definitely, and I feel like they have the

outcome there. What's missing sometimes is the fundamental

knowledge of those soft skills. So for example, you're doing a

group project that doesn't automatically make you an active

listener, you need to understand that, by the way, we're flawed.

We don't listen, we have a bias we tend to zone people out. But

here are strategies that you can do. One of the most important

lessons I learned is when... It's because I have ADHD... I

tend to be all over the place...is someone said... "when

someone's talking, don't think about the next question. Don't

do anything, but then recite back what they said in your own

words; it makes them realize that you're listening." But it

actually helps you and your brain process it. It was

then...like a lifesaver for me because it wasn't just being

polite. It was learning how to active listen...wasn't in a

force. It wasn't in a university. It was...I don't

even know where I heard that. But those are the things that we

have to compliment with with the projects, but I agree the

project is a great way because it's a great way to collaborate.

And it is definitely a way to reinforce and hopefully

build on those those soft skills. Well, anyway, Kevin,

thank you so much. This has barely been been great. and

really appreciate it now your book, give us the title of the

book, and then also where we can find it and how we can you know,

learn more about about your book.

Yeah, and thank you for that it's turning data

into wisdom. It's not for data scientists, not for data. It's

for anyone who wants to not be overwhelmed with information and

learn how to make better decisions. You can do it at the

organization level, or you can do it for home...personal. What

am I going to make for dinner? Where should we go on vacation?

What type of car should I buy? So you can find it on Amazon,

most major online bookstores that I've seen...either just

type in my last name or type in "Turning Data into Wisdom."

Fantastic. So, Turning Data into Wisdom, and

Kevin Hanegan. Thank you so much. And for the listeners,

please stay tuned for many other videos in this series of the

Backstory on Marketing. Please visit

marketingmachine.prorelevant.com, and download the first chapter

of my book, which is now out. It's available on Amazon as

well. So The Post-COVID Marketing Machine. And then,

lastly, don't forget to sign up for more episodes on this

podcast series. And if you like it, please rate it with five

stars. Thank you so much. And Kevin, thank you.

Yeah, thanks for having me. It's been a pleasure.