Artwork for podcast The Backstory on Marketing
Kevin Hanegan: The Evolution of Data Literacy in a World of Change
Episode 131st August 2022 • The Backstory on Marketing • Guy Powell
00:00:00 00:36:17

Share Episode

Shownotes

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. 

Links:

https://thedataliteracyproject.org/ 

https://www.linkedin.com/in/kevinhanegan/  

https://www.kevinhanegan.com/  

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

https://marketingmachine.prorelevant.com/

Sign up for ProRelevant Emails:

https://mailchi.mp/prorelevant/newsletter 

Link to YouTube Video:

https://www.youtube.com/watch?v=p8TJq-pdFwY

Transcripts

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

speaking with Kevin Hanegan. Kevin is a senior leader who

Guy Powell:

likes to use data and analytics to transform, innovate and

Guy Powell:

continuously improve organizations to make them the

Guy Powell:

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

Guy Powell:

technology, learning and psychology. He believes the

Guy Powell:

world is constantly evolving, and we should always be evolving

Guy Powell:

and improving ourselves, in business and in our personal

Guy Powell:

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

Guy Powell:

informed decision making, the future of learning and growth

Guy Powell:

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

Guy Powell:

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

Guy Powell:

being here.

Kevin Hanegan:

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

Kevin Hanegan:

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

Guy Powell:

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

Guy Powell:

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

Kevin Hanegan:

Yeah, I everyone probably says they have unusual

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

software programming, actually, one of the programming languages

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

helping people understand how to how to evolve and leverage the

Kevin Hanegan:

skills that help them stay current, and continue being

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

kind of forget about over talent, different soft skills,

Kevin Hanegan:

like curiosity and creativity.

Guy Powell:

Yeah, absolutely. And I like your point about

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

are changing.

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

look at historical events to come up with a probability or

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

Everything materially changes because you don't have that

Kevin Hanegan:

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

Kevin Hanegan:

creates all this uncertainty, which again, could be an

Kevin Hanegan:

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

Kevin Hanegan:

disastrous and doubt.

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

critical component of of a corporation and certainly of

Guy Powell:

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

Guy Powell:

for everybody to be continually learning. So organizations have

Guy Powell:

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

Guy Powell:

what you understand under that?

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

curious. They're always challenging. And we encourage

Kevin Hanegan:

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

Kevin Hanegan:

like, as you go through the typical schooling in

Kevin Hanegan:

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

Kevin Hanegan:

much about critical thinking or collaborative thinking or being

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

supposed to question the teacher. They kind of take those

Kevin Hanegan:

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

Kevin Hanegan:

changing, so we need those different perspectives. We need

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

resilient, how to do active listening, how to challenge

Kevin Hanegan:

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

Kevin Hanegan:

emotional intelligence, if not equally, or potentially even

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

do the soft skills in college.

Guy Powell:

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

Guy Powell:

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

Guy Powell:

female, just the kind of the personality of male versus

Guy Powell:

female and how they approach different questions and how they

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

Notice learning is that more closely related to HR or more

Guy Powell:

closely related to the technical side of the business.

Kevin Hanegan:

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

Kevin Hanegan:

learning was related to HR, as you were learning the

Kevin Hanegan:

compliance, you were doing learning and development, some

Kevin Hanegan:

of the soft skills, but maybe skills like project management

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

the internal learning and development, the soft skills as

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

fact that everyone needs to keep upskilling and relearning and

Guy Powell:

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

Guy Powell:

unlearning.

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

progress maybe and work better within the organization.

Guy Powell:

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

Guy Powell:

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

Guy Powell:

the need for analysts is exploding, but to your point

Guy Powell:

about asking the questions and digging deep into the data and

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

Everyone in the beginning of the pandemic was inundated with data

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

COVID charts that showed flatten the curve, cumulative cases,

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

rolling averages seven day averages, they started saying,

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

organization that build those charts and graphs, everyone in

Kevin Hanegan:

the organizations, and with COVID, everyone in the world

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

or probability of getting it or someone in the family getting

Kevin Hanegan:

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

Kevin Hanegan:

someone in the family getting seriously sick or even dying?

Kevin Hanegan:

You then have to take those graphs and understand them,

Kevin Hanegan:

understand the insights, and then weigh the probabilities.

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

and everyone can have a different answer to the same

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

making their numbers because they had their own data. And

Guy Powell:

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

Guy Powell:

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

Guy Powell:

everybody was making the numbers, but the company was

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

dinged or whatever from that?

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Guy Powell:

Yeah. And although, and I agree with you about

Guy Powell:

understanding what the business question is, first, and then

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

is accurate.

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

more about like building a special warehouse or lake that

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

have you, and where it might actually become useful.

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

especially today, things change so fast. The companies that

Kevin Hanegan:

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

Kevin Hanegan:

succeed.

Guy Powell:

Yeah. Yeah, absolutely. And that, that fast

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

well.

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

where people will see the writing on the wall so to

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

framework and working with different people-you get those

Kevin Hanegan:

different perspectives for people. If you're comfortable,

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

team where you're comfortable challenging the people above

Kevin Hanegan:

you.

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

in some organizations-just by the personality of the

Guy Powell:

organization-that can be very difficult.

Kevin Hanegan:

It's one of the biggest roadblocks we see is

Kevin Hanegan:

this culture change. It has happened in certain

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

doing that.

Guy Powell:

Yeah, yeah, absolutely. Let me go back a

Guy Powell:

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

Guy Powell:

marketers quite often got into marketing because they didn't

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

insights and results out of that data?

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

Most organizations, most individuals, they need to be

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

put it together.

Guy Powell:

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

Guy Powell:

maybe some of the barriers that maybe hinder organizations from

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

and really discuss what that business question is. How to

Guy Powell:

really define that business question. Do you see that as

Guy Powell:

well? Or?

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

different results compared to what? Compared to last marketing

Kevin Hanegan:

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

Kevin Hanegan:

different channels? Are you looking across different age

Kevin Hanegan:

groups? What are your dimensions? Where do your

Kevin Hanegan:

segmentation? What does good look like? All of those

Kevin Hanegan:

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

Kevin Hanegan:

questions, and if someone doesn't know the

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

you get more puzzle pieces, and then questioning everyone about

Kevin Hanegan:

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

Kevin Hanegan:

always equate it to everyone in a corporate setting is usually

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

specific, measurable, answerable, driven by data time

Kevin Hanegan:

bounds, same thing.

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

supporting that question, is making sure that the data is

Guy Powell:

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

Guy Powell:

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

Guy Powell:

useful to make the right kind of business decisions for the

Guy Powell:

organization.

Kevin Hanegan:

Yeah. I mean...obviously tools and

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

visualizing the outcome, and then working backwards. And then

Kevin Hanegan:

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

Kevin Hanegan:

analyze customer sentiment, right? That's not necessarily

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

have organizations visualize the ideal outcome and then working

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

wasn't specific enough because what actually happens is the

Kevin Hanegan:

total net sales were actually lower than what they were

Kevin Hanegan:

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

Kevin Hanegan:

when your questions and your challenge and you understood

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

much money so it wasn't successful."

Guy Powell:

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

Guy Powell:

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

Guy Powell:

going to look at that specific business question. Because it

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

know, not only understanding what that data is that underlies

Guy Powell:

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

Guy Powell:

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

Kevin Hanegan:

Absolutely. garbage in, garbage out is the

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

things.

Guy Powell:

Yeah, yeah, absolutely. So before we close,

Guy Powell:

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

Guy Powell:

that we haven't talked about?

Kevin Hanegan:

I think the highlight for me is I just I

Kevin Hanegan:

really believe that. Solving, marketing challenges,

Kevin Hanegan:

organization challenges, life challenges; we have everything

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

We stop living confirmation bias. And when someone else is

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

critical than ever before.

Guy Powell:

Yeah, it's almost like the universities have to

Guy Powell:

add another year on to understand what those soft

Guy Powell:

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

Guy Powell:

teaching over the last couple of years at different universities

Guy Powell:

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

Guy Powell:

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

Guy Powell:

now doing these project based learning where you have three,

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

to manage the team, how to really define the business

Guy Powell:

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

Guy Powell:

supporting the ability to analyze and deliver results

Guy Powell:

based on what that business question was.

Kevin Hanegan:

Absolutely, because you go through school

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

outcome there. What's missing sometimes is the fundamental

Kevin Hanegan:

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

Kevin Hanegan:

group project that doesn't automatically make you an active

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

actually helps you and your brain process it. It was

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Guy Powell:

And it is definitely a way to reinforce and hopefully

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

learn more about about your book.

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

for anyone who wants to not be overwhelmed with information and

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Kevin Hanegan:

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

Guy Powell:

Fantastic. So, Turning Data into Wisdom, and

Guy Powell:

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

Guy Powell:

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

Guy Powell:

Backstory on Marketing. Please visit

Guy Powell:

marketingmachine.prorelevant.com, and download the first chapter

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Guy Powell:

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

Kevin Hanegan:

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