Episode 13: Kevin Hanegan - The Evolution of Data Literacy in a World of Change

Guy Powell: 00:00:00

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

Guy Powell: 00:00:02

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

Guy Powell: 00:00:06

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

Guy Powell: 00:00:10

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

Guy Powell: 00:00:14

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

Guy Powell: 00:00:18

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

Guy Powell: 00:00:21

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

Guy Powell: 00:00:25

speaking with Kevin Hanegan. Kevin is a senior leader who

Guy Powell: 00:00:28

likes to use data and analytics to transform, innovate and

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continuously improve organizations to make them the

Guy Powell: 00:00:35

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

Guy Powell: 00:00:40

technology, learning and psychology. He believes the

Guy Powell: 00:00:44

world is constantly evolving, and we should always be evolving

Guy Powell: 00:00:48

and improving ourselves, in business and in our personal

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

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informed decision making, the future of learning and growth

Guy Powell: 00:00:59

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

Guy Powell: 00:01:03

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

Guy Powell: 00:01:08

being here.

Kevin Hanegan: 00:01:09

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

Kevin Hanegan: 00:01:11

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

Guy Powell: 00:01:15

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

Guy Powell: 00:01:17

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

Kevin Hanegan: 00:01:21

Yeah, I everyone probably says they have unusual

Kevin Hanegan: 00:01:24

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

Kevin Hanegan: 00:01:27

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

Kevin Hanegan: 00:01:30

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

Kevin Hanegan: 00:01:34

software programming, actually, one of the programming languages

Kevin Hanegan: 00:01:37

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

Kevin Hanegan: 00:01:40

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

Kevin Hanegan: 00:01:42

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

Kevin Hanegan: 00:01:47

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

Kevin Hanegan: 00:01:51

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

Kevin Hanegan: 00:01:54

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

Kevin Hanegan: 00:01:58

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

Kevin Hanegan: 00:02:02

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

Kevin Hanegan: 00:02:04

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

Kevin Hanegan: 00:02:09

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

Kevin Hanegan: 00:02:11

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

Kevin Hanegan: 00:02:15

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

Kevin Hanegan: 00:02:19

helping people understand how to how to evolve and leverage the

Kevin Hanegan: 00:02:24

skills that help them stay current, and continue being

Kevin Hanegan: 00:02:27

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

Kevin Hanegan: 00:02:30

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

Kevin Hanegan: 00:02:34

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

Kevin Hanegan: 00:02:37

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

Kevin Hanegan: 00:02:40

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

Kevin Hanegan: 00:02:44

kind of forget about over talent, different soft skills,

Kevin Hanegan: 00:02:47

like curiosity and creativity.

Guy Powell: 00:02:49

Yeah, absolutely. And I like your point about

Guy Powell: 00:02:52

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

Guy Powell: 00:02:57

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

Guy Powell: 00:03:01

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

Guy Powell: 00:03:05

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

Guy Powell: 00:03:08

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

Guy Powell: 00:03:11

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

Guy Powell: 00:03:15

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

Guy Powell: 00:03:18

are changing.

Kevin Hanegan: 00:03:20

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

Kevin Hanegan: 00:03:23

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

Kevin Hanegan: 00:03:25

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

Kevin Hanegan: 00:03:26

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

Kevin Hanegan: 00:03:31

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

Kevin Hanegan: 00:03:37

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

Kevin Hanegan: 00:03:41

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

Kevin Hanegan: 00:03:44

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

Kevin Hanegan: 00:03:47

look at historical events to come up with a probability or

Kevin Hanegan: 00:03:50

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

Kevin Hanegan: 00:03:52

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

Kevin Hanegan: 00:03:56

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

Kevin Hanegan: 00:03:59

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

Kevin Hanegan: 00:04:02

Everything materially changes because you don't have that

Kevin Hanegan: 00:04:05

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

Kevin Hanegan: 00:04:10

creates all this uncertainty, which again, could be an

Kevin Hanegan: 00:04:13

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

Kevin Hanegan: 00:04:15

disastrous and doubt.

Guy Powell: 00:04:17

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

Guy Powell: 00:04:20

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

Guy Powell: 00:04:23

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

Guy Powell: 00:04:28

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

Guy Powell: 00:04:30

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

Guy Powell: 00:04:33

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

Guy Powell: 00:04:37

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

Guy Powell: 00:04:42

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

Guy Powell: 00:04:45

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

Guy Powell: 00:04:49

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

Guy Powell: 00:04:54

critical component of of a corporation and certainly of

Guy Powell: 00:04:57

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

Guy Powell: 00:05:01

for everybody to be continually learning. So organizations have

Guy Powell: 00:05:05

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

Guy Powell: 00:05:08

what you understand under that?

Kevin Hanegan: 00:05:10

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

Kevin Hanegan: 00:05:14

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

Kevin Hanegan: 00:05:18

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

Kevin Hanegan: 00:05:21

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

Kevin Hanegan: 00:05:26

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

Kevin Hanegan: 00:05:29

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

Kevin Hanegan: 00:05:34

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

Kevin Hanegan: 00:05:36

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

Kevin Hanegan: 00:05:39

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

Kevin Hanegan: 00:05:42

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

Kevin Hanegan: 00:05:47

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

Kevin Hanegan: 00:05:50

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

Kevin Hanegan: 00:05:53

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

Kevin Hanegan: 00:05:57

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

Kevin Hanegan: 00:06:02

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

Kevin Hanegan: 00:06:06

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

Kevin Hanegan: 00:06:08

curious. They're always challenging. And we encourage

Kevin Hanegan: 00:06:11

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

Kevin Hanegan: 00:06:14

like, as you go through the typical schooling in

Kevin Hanegan: 00:06:18

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

Kevin Hanegan: 00:06:22

much about critical thinking or collaborative thinking or being

Kevin Hanegan: 00:06:27

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

Kevin Hanegan: 00:06:31

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

Kevin Hanegan: 00:06:34

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

Kevin Hanegan: 00:06:36

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

Kevin Hanegan: 00:06:39

supposed to question the teacher. They kind of take those

Kevin Hanegan: 00:06:41

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

Kevin Hanegan: 00:06:46

changing, so we need those different perspectives. We need

Kevin Hanegan: 00:06:49

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

Kevin Hanegan: 00:06:52

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

Kevin Hanegan: 00:06:56

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

Kevin Hanegan: 00:07:00

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

Kevin Hanegan: 00:07:04

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

Kevin Hanegan: 00:07:07

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

Kevin Hanegan: 00:07:10

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

Kevin Hanegan: 00:07:13

resilient, how to do active listening, how to challenge

Kevin Hanegan: 00:07:16

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

Kevin Hanegan: 00:07:20

emotional intelligence, if not equally, or potentially even

Kevin Hanegan: 00:07:25

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

Kevin Hanegan: 00:07:28

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

Kevin Hanegan: 00:07:30

do the soft skills in college.

Guy Powell: 00:07:33

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

Guy Powell: 00:07:36

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

Guy Powell: 00:07:42

female, just the kind of the personality of male versus

Guy Powell: 00:07:46

female and how they approach different questions and how they

Guy Powell: 00:07:50

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

Guy Powell: 00:07:53

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

Guy Powell: 00:07:56

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

Guy Powell: 00:08:00

Notice learning is that more closely related to HR or more

Guy Powell: 00:08:06

closely related to the technical side of the business.

Kevin Hanegan: 00:08:10

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

Kevin Hanegan: 00:08:13

learning was related to HR, as you were learning the

Kevin Hanegan: 00:08:15

compliance, you were doing learning and development, some

Kevin Hanegan: 00:08:19

of the soft skills, but maybe skills like project management

Kevin Hanegan: 00:08:22

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

Kevin Hanegan: 00:08:27

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

Kevin Hanegan: 00:08:30

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

Kevin Hanegan: 00:08:34

the internal learning and development, the soft skills as

Kevin Hanegan: 00:08:36

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

Kevin Hanegan: 00:08:39

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

Kevin Hanegan: 00:08:42

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

Kevin Hanegan: 00:08:45

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

Kevin Hanegan: 00:08:48

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

Kevin Hanegan: 00:08:55

fact that everyone needs to keep upskilling and relearning and

Guy Powell: 00:08:59

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

Guy Powell: 00:08:59

unlearning.

Guy Powell: 00:09:03

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

Guy Powell: 00:09:07

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

Guy Powell: 00:09:12

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

Guy Powell: 00:09:16

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

Guy Powell: 00:09:19

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

Guy Powell: 00:09:22

progress maybe and work better within the organization.

Guy Powell: 00:09:27

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

Guy Powell: 00:09:34

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

Guy Powell: 00:09:39

the need for analysts is exploding, but to your point

Guy Powell: 00:09:43

about asking the questions and digging deep into the data and

Guy Powell: 00:09:47

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

Guy Powell: 00:09:50

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

Guy Powell: 00:09:56

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

Guy Powell: 00:09:59

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

Kevin Hanegan: 00:10:02

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

Kevin Hanegan: 00:10:06

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

Kevin Hanegan: 00:10:11

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

Kevin Hanegan: 00:10:14

Everyone in the beginning of the pandemic was inundated with data

Kevin Hanegan: 00:10:19

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

Kevin Hanegan: 00:10:23

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

Kevin Hanegan: 00:10:26

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

Kevin Hanegan: 00:10:30

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

Kevin Hanegan: 00:10:36

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

Kevin Hanegan: 00:10:38

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

Kevin Hanegan: 00:10:44

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

Kevin Hanegan: 00:10:49

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

Kevin Hanegan: 00:10:52

COVID charts that showed flatten the curve, cumulative cases,

Kevin Hanegan: 00:10:56

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

Kevin Hanegan: 00:10:59

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

Kevin Hanegan: 00:11:03

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

Kevin Hanegan: 00:11:07

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

Kevin Hanegan: 00:11:10

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

Kevin Hanegan: 00:11:14

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

Kevin Hanegan: 00:11:17

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

Kevin Hanegan: 00:11:20

rolling averages seven day averages, they started saying,

Kevin Hanegan: 00:11:24

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

Kevin Hanegan: 00:11:28

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

Kevin Hanegan: 00:11:30

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

Kevin Hanegan: 00:11:34

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

Kevin Hanegan: 00:11:37

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

Kevin Hanegan: 00:11:41

organization that build those charts and graphs, everyone in

Kevin Hanegan: 00:11:46

the organizations, and with COVID, everyone in the world

Kevin Hanegan: 00:11:49

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

Kevin Hanegan: 00:11:52

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

Kevin Hanegan: 00:11:56

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

Kevin Hanegan: 00:12:00

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

Kevin Hanegan: 00:12:03

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

Kevin Hanegan: 00:12:08

or probability of getting it or someone in the family getting

Kevin Hanegan: 00:12:12

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

Kevin Hanegan: 00:12:16

someone in the family getting seriously sick or even dying?

Kevin Hanegan: 00:12:20

You then have to take those graphs and understand them,

Kevin Hanegan: 00:12:24

understand the insights, and then weigh the probabilities.

Kevin Hanegan: 00:12:28

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

Kevin Hanegan: 00:12:31

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

Kevin Hanegan: 00:12:34

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

Kevin Hanegan: 00:12:37

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

Kevin Hanegan: 00:12:40

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

Kevin Hanegan: 00:12:44

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

Kevin Hanegan: 00:12:47

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

Kevin Hanegan: 00:12:50

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

Kevin Hanegan: 00:12:54

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

Kevin Hanegan: 00:12:56

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

Kevin Hanegan: 00:13:00

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

Kevin Hanegan: 00:13:03

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

Kevin Hanegan: 00:13:06

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

Kevin Hanegan: 00:13:10

and everyone can have a different answer to the same

Kevin Hanegan: 00:13:12

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

Kevin Hanegan: 00:13:15

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

Kevin Hanegan: 00:13:20

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

Kevin Hanegan: 00:13:23

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

Guy Powell: 00:13:27

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

Guy Powell: 00:13:30

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

Guy Powell: 00:13:33

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

Guy Powell: 00:13:37

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

Guy Powell: 00:13:41

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

Guy Powell: 00:13:47

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

Guy Powell: 00:13:50

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

Guy Powell: 00:13:54

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

Guy Powell: 00:13:57

making their numbers because they had their own data. And

Guy Powell: 00:14:01

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

Guy Powell: 00:14:05

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

Guy Powell: 00:14:08

everybody was making the numbers, but the company was

Guy Powell: 00:14:11

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

Guy Powell: 00:14:14

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

Guy Powell: 00:14:16

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

Guy Powell: 00:14:21

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

Guy Powell: 00:14:26

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

Guy Powell: 00:14:28

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

Guy Powell: 00:14:31

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

Guy Powell: 00:14:35

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

Guy Powell: 00:14:37

dinged or whatever from that?

Kevin Hanegan: 00:14:39

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

Kevin Hanegan: 00:14:42

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

Kevin Hanegan: 00:14:47

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

Kevin Hanegan: 00:14:49

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

Kevin Hanegan: 00:14:53

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

Kevin Hanegan: 00:14:57

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

Kevin Hanegan: 00:15:01

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

Kevin Hanegan: 00:15:04

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

Kevin Hanegan: 00:15:07

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

Kevin Hanegan: 00:15:10

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

Kevin Hanegan: 00:15:13

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

Kevin Hanegan: 00:15:16

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

Kevin Hanegan: 00:15:21

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

Kevin Hanegan: 00:15:26

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

Kevin Hanegan: 00:15:30

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

Kevin Hanegan: 00:15:33

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

Kevin Hanegan: 00:15:36

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

Kevin Hanegan: 00:15:41

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

Kevin Hanegan: 00:15:45

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

Kevin Hanegan: 00:15:50

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

Kevin Hanegan: 00:15:52

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

Guy Powell: 00:15:56

Yeah. And although, and I agree with you about

Guy Powell: 00:15:58

understanding what the business question is, first, and then

Guy Powell: 00:16:02

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

Guy Powell: 00:16:05

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

Guy Powell: 00:16:09

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

Guy Powell: 00:16:13

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

Guy Powell: 00:16:16

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

Guy Powell: 00:16:20

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

Guy Powell: 00:16:23

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

Guy Powell: 00:16:27

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

Guy Powell: 00:16:30

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

Guy Powell: 00:16:34

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

Guy Powell: 00:16:38

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

Guy Powell: 00:16:41

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

Guy Powell: 00:16:44

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

Guy Powell: 00:16:48

is accurate.

Kevin Hanegan: 00:16:49

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

Kevin Hanegan: 00:16:49

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

Kevin Hanegan: 00:16:51

more about like building a special warehouse or lake that

Kevin Hanegan: 00:16:52

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

Kevin Hanegan: 00:16:53

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

Kevin Hanegan: 00:16:58

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

Guy Powell: 00:17:07

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

Guy Powell: 00:17:11

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

Guy Powell: 00:17:13

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

Guy Powell: 00:17:18

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

Guy Powell: 00:17:23

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

Guy Powell: 00:17:26

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

Guy Powell: 00:17:29

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

Guy Powell: 00:17:32

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

Guy Powell: 00:17:34

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

Guy Powell: 00:17:38

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

Guy Powell: 00:17:41

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

Guy Powell: 00:17:44

have you, and where it might actually become useful.

Kevin Hanegan: 00:17:49

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

Kevin Hanegan: 00:17:50

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

Kevin Hanegan: 00:17:53

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

Kevin Hanegan: 00:17:58

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

Kevin Hanegan: 00:18:01

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

Kevin Hanegan: 00:18:05

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

Kevin Hanegan: 00:18:08

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

Kevin Hanegan: 00:18:11

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

Kevin Hanegan: 00:18:13

especially today, things change so fast. The companies that

Kevin Hanegan: 00:18:16

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

Kevin Hanegan: 00:18:19

succeed.

Guy Powell: 00:18:20

Yeah. Yeah, absolutely. And that, that fast

Guy Powell: 00:18:24

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

Guy Powell: 00:18:28

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

Guy Powell: 00:18:31

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

Guy Powell: 00:18:36

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

Guy Powell: 00:18:39

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

Guy Powell: 00:18:43

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

Guy Powell: 00:18:45

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

Guy Powell: 00:18:48

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

Guy Powell: 00:18:50

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

Guy Powell: 00:18:52

well.

Kevin Hanegan: 00:18:53

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

Kevin Hanegan: 00:18:57

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

Kevin Hanegan: 00:19:02

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

Kevin Hanegan: 00:19:06

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

Kevin Hanegan: 00:19:09

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

Kevin Hanegan: 00:19:14

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

Kevin Hanegan: 00:19:16

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

Kevin Hanegan: 00:19:20

where people will see the writing on the wall so to

Kevin Hanegan: 00:19:23

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

Kevin Hanegan: 00:19:27

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

Kevin Hanegan: 00:19:31

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

Kevin Hanegan: 00:19:35

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

Kevin Hanegan: 00:19:38

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

Kevin Hanegan: 00:19:41

framework and working with different people-you get those

Kevin Hanegan: 00:19:43

different perspectives for people. If you're comfortable,

Kevin Hanegan: 00:19:46

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

Kevin Hanegan: 00:19:49

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

Kevin Hanegan: 00:19:53

team where you're comfortable challenging the people above

Kevin Hanegan: 00:19:55

you.

Guy Powell: 00:19:56

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

Guy Powell: 00:19:59

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

Guy Powell: 00:20:03

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

Guy Powell: 00:20:07

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

Guy Powell: 00:20:12

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

Guy Powell: 00:20:15

in some organizations-just by the personality of the

Guy Powell: 00:20:17

organization-that can be very difficult.

Kevin Hanegan: 00:20:21

It's one of the biggest roadblocks we see is

Kevin Hanegan: 00:20:24

this culture change. It has happened in certain

Kevin Hanegan: 00:20:28

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

Kevin Hanegan: 00:20:31

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

Kevin Hanegan: 00:20:34

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

Kevin Hanegan: 00:20:37

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

Kevin Hanegan: 00:20:40

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

Kevin Hanegan: 00:20:44

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

Kevin Hanegan: 00:20:47

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

Kevin Hanegan: 00:20:50

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

Kevin Hanegan: 00:20:52

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

Kevin Hanegan: 00:20:55

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

Kevin Hanegan: 00:20:58

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

Kevin Hanegan: 00:21:01

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

Kevin Hanegan: 00:21:04

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

Kevin Hanegan: 00:21:07

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

Kevin Hanegan: 00:21:09

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

Kevin Hanegan: 00:21:12

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

Kevin Hanegan: 00:21:15

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

Kevin Hanegan: 00:21:18

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

Kevin Hanegan: 00:21:22

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

Kevin Hanegan: 00:21:25

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

Kevin Hanegan: 00:21:28

doing that.

Guy Powell: 00:21:29

Yeah, yeah, absolutely. Let me go back a

Guy Powell: 00:21:32

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

Guy Powell: 00:21:35

marketers quite often got into marketing because they didn't

Guy Powell: 00:21:38

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

Guy Powell: 00:21:42

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

Guy Powell: 00:21:45

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

Guy Powell: 00:21:49

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

Guy Powell: 00:21:53

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

Guy Powell: 00:21:57

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

Guy Powell: 00:22:02

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

Guy Powell: 00:22:08

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

Guy Powell: 00:22:12

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

Guy Powell: 00:22:14

insights and results out of that data?

Kevin Hanegan: 00:22:18

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

Kevin Hanegan: 00:22:21

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

Kevin Hanegan: 00:22:25

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

Kevin Hanegan: 00:22:30

Most organizations, most individuals, they need to be

Kevin Hanegan: 00:22:34

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

Kevin Hanegan: 00:22:38

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

Kevin Hanegan: 00:22:43

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

Kevin Hanegan: 00:22:47

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

Kevin Hanegan: 00:22:50

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

Kevin Hanegan: 00:22:53

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

Kevin Hanegan: 00:22:57

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

Kevin Hanegan: 00:23:00

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

Kevin Hanegan: 00:23:03

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

Kevin Hanegan: 00:23:07

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

Kevin Hanegan: 00:23:11

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

Kevin Hanegan: 00:23:15

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

Kevin Hanegan: 00:23:18

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

Kevin Hanegan: 00:23:21

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

Kevin Hanegan: 00:23:24

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

Kevin Hanegan: 00:23:27

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

Kevin Hanegan: 00:23:33

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

Kevin Hanegan: 00:23:36

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

Kevin Hanegan: 00:23:39

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

Kevin Hanegan: 00:23:42

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

Kevin Hanegan: 00:23:44

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

Kevin Hanegan: 00:23:48

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

Kevin Hanegan: 00:23:52

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

Kevin Hanegan: 00:23:54

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

Kevin Hanegan: 00:23:56

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

Kevin Hanegan: 00:23:59

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

Kevin Hanegan: 00:24:03

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

Kevin Hanegan: 00:24:07

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

Kevin Hanegan: 00:24:10

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

Kevin Hanegan: 00:24:14

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

Kevin Hanegan: 00:24:16

put it together.

Guy Powell: 00:24:17

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

Guy Powell: 00:24:21

maybe some of the barriers that maybe hinder organizations from

Guy Powell: 00:24:26

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

Guy Powell: 00:24:30

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

Guy Powell: 00:24:33

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

Guy Powell: 00:24:40

and really discuss what that business question is. How to

Guy Powell: 00:24:45

really define that business question. Do you see that as

Guy Powell: 00:24:48

well? Or?

Kevin Hanegan: 00:24:50

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

Kevin Hanegan: 00:24:54

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

Kevin Hanegan: 00:24:59

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

Kevin Hanegan: 00:25:03

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

Kevin Hanegan: 00:25:06

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

Kevin Hanegan: 00:25:09

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

Kevin Hanegan: 00:25:13

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

Kevin Hanegan: 00:25:16

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

Kevin Hanegan: 00:25:21

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

Kevin Hanegan: 00:25:26

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

Kevin Hanegan: 00:25:30

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

Kevin Hanegan: 00:25:32

different results compared to what? Compared to last marketing

Kevin Hanegan: 00:25:36

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

Kevin Hanegan: 00:25:40

different channels? Are you looking across different age

Kevin Hanegan: 00:25:43

groups? What are your dimensions? Where do your

Kevin Hanegan: 00:25:44

segmentation? What does good look like? All of those

Kevin Hanegan: 00:25:48

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

Kevin Hanegan: 00:25:52

questions, and if someone doesn't know the

Kevin Hanegan: 00:25:54

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

Kevin Hanegan: 00:25:59

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

Kevin Hanegan: 00:26:01

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

Kevin Hanegan: 00:26:04

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

Kevin Hanegan: 00:26:07

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

Kevin Hanegan: 00:26:10

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

Kevin Hanegan: 00:26:12

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

Kevin Hanegan: 00:26:15

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

Kevin Hanegan: 00:26:19

you get more puzzle pieces, and then questioning everyone about

Kevin Hanegan: 00:26:22

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

Kevin Hanegan: 00:26:26

always equate it to everyone in a corporate setting is usually

Kevin Hanegan: 00:26:30

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

Kevin Hanegan: 00:26:34

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

Kevin Hanegan: 00:26:36

specific, measurable, answerable, driven by data time

Kevin Hanegan: 00:26:39

bounds, same thing.

Guy Powell: 00:26:41

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

Guy Powell: 00:26:44

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

Guy Powell: 00:26:47

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

Guy Powell: 00:26:51

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

Guy Powell: 00:26:56

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

Guy Powell: 00:26:59

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

Guy Powell: 00:27:03

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

Guy Powell: 00:27:05

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

Guy Powell: 00:27:09

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

Guy Powell: 00:27:12

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

Guy Powell: 00:27:15

supporting that question, is making sure that the data is

Guy Powell: 00:27:19

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

Guy Powell: 00:27:23

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

Guy Powell: 00:27:27

useful to make the right kind of business decisions for the

Guy Powell: 00:27:30

organization.

Kevin Hanegan: 00:27:31

Yeah. I mean...obviously tools and

Kevin Hanegan: 00:27:33

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

Kevin Hanegan: 00:27:37

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

Kevin Hanegan: 00:27:42

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

Kevin Hanegan: 00:27:45

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

Kevin Hanegan: 00:27:49

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

Kevin Hanegan: 00:27:53

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

Kevin Hanegan: 00:27:57

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

Kevin Hanegan: 00:28:01

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

Kevin Hanegan: 00:28:06

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

Kevin Hanegan: 00:28:09

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

Kevin Hanegan: 00:28:13

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

Kevin Hanegan: 00:28:18

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

Kevin Hanegan: 00:28:20

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

Kevin Hanegan: 00:28:25

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

Kevin Hanegan: 00:28:28

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

Kevin Hanegan: 00:28:31

visualizing the outcome, and then working backwards. And then

Kevin Hanegan: 00:28:35

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

Kevin Hanegan: 00:28:38

analyze customer sentiment, right? That's not necessarily

Kevin Hanegan: 00:28:41

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

Kevin Hanegan: 00:28:45

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

Kevin Hanegan: 00:28:48

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

Kevin Hanegan: 00:28:50

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

Kevin Hanegan: 00:28:53

have organizations visualize the ideal outcome and then working

Kevin Hanegan: 00:28:59

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

Kevin Hanegan: 00:29:02

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

Kevin Hanegan: 00:29:08

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

Kevin Hanegan: 00:29:10

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

Kevin Hanegan: 00:29:14

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

Kevin Hanegan: 00:29:17

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

Kevin Hanegan: 00:29:21

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

Kevin Hanegan: 00:29:25

wasn't specific enough because what actually happens is the

Kevin Hanegan: 00:29:31

total net sales were actually lower than what they were

Kevin Hanegan: 00:29:35

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

Kevin Hanegan: 00:29:39

when your questions and your challenge and you understood

Kevin Hanegan: 00:29:42

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

Kevin Hanegan: 00:29:46

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

Kevin Hanegan: 00:29:49

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

Kevin Hanegan: 00:29:52

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

Kevin Hanegan: 00:29:58

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

Kevin Hanegan: 00:30:01

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

Kevin Hanegan: 00:30:04

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

Kevin Hanegan: 00:30:07

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

Kevin Hanegan: 00:30:10

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

Kevin Hanegan: 00:30:13

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

Kevin Hanegan: 00:30:16

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

Kevin Hanegan: 00:30:19

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

Kevin Hanegan: 00:30:22

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

Kevin Hanegan: 00:30:25

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

Kevin Hanegan: 00:30:28

much money so it wasn't successful."

Guy Powell: 00:30:29

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

Guy Powell: 00:30:33

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

Guy Powell: 00:30:37

going to look at that specific business question. Because it

Guy Powell: 00:30:40

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

Guy Powell: 00:30:44

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

Guy Powell: 00:30:48

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

Guy Powell: 00:30:52

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

Guy Powell: 00:30:56

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

Guy Powell: 00:31:00

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

Guy Powell: 00:31:04

know, not only understanding what that data is that underlies

Guy Powell: 00:31:08

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

Guy Powell: 00:31:12

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

Kevin Hanegan: 00:31:14

Absolutely. garbage in, garbage out is the

Kevin Hanegan: 00:31:15

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

Kevin Hanegan: 00:31:22

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

Kevin Hanegan: 00:31:26

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

Kevin Hanegan: 00:31:29

things.

Guy Powell: 00:31:29

Yeah, yeah, absolutely. So before we close,

Guy Powell: 00:31:33

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

Guy Powell: 00:31:36

that we haven't talked about?

Kevin Hanegan: 00:31:37

I think the highlight for me is I just I

Kevin Hanegan: 00:31:41

really believe that. Solving, marketing challenges,

Kevin Hanegan: 00:31:46

organization challenges, life challenges; we have everything

Kevin Hanegan: 00:31:51

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

Kevin Hanegan: 00:31:55

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

Kevin Hanegan: 00:31:59

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

Kevin Hanegan: 00:32:01

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

Kevin Hanegan: 00:32:05

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

Kevin Hanegan: 00:32:09

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

Kevin Hanegan: 00:32:12

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

Kevin Hanegan: 00:32:18

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

Kevin Hanegan: 00:32:22

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

Kevin Hanegan: 00:32:24

We stop living confirmation bias. And when someone else is

Kevin Hanegan: 00:32:28

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

Kevin Hanegan: 00:32:32

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

Kevin Hanegan: 00:32:35

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

Kevin Hanegan: 00:32:38

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

Kevin Hanegan: 00:32:43

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

Kevin Hanegan: 00:32:46

critical than ever before.

Guy Powell: 00:32:48

Yeah, it's almost like the universities have to

Guy Powell: 00:32:51

add another year on to understand what those soft

Guy Powell: 00:32:54

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

Guy Powell: 00:32:57

teaching over the last couple of years at different universities

Guy Powell: 00:33:02

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

Guy Powell: 00:33:06

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

Guy Powell: 00:33:10

now doing these project based learning where you have three,

Guy Powell: 00:33:15

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

Guy Powell: 00:33:20

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

Guy Powell: 00:33:24

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

Guy Powell: 00:33:27

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

Guy Powell: 00:33:29

to manage the team, how to really define the business

Guy Powell: 00:33:32

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

Guy Powell: 00:33:36

supporting the ability to analyze and deliver results

Guy Powell: 00:33:40

based on what that business question was.

Kevin Hanegan: 00:33:43

Absolutely, because you go through school

Kevin Hanegan: 00:33:45

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

Kevin Hanegan: 00:33:48

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

Kevin Hanegan: 00:33:51

outcome there. What's missing sometimes is the fundamental

Kevin Hanegan: 00:33:55

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

Kevin Hanegan: 00:34:00

group project that doesn't automatically make you an active

Kevin Hanegan: 00:34:03

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

Kevin Hanegan: 00:34:06

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

Kevin Hanegan: 00:34:10

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

Kevin Hanegan: 00:34:13

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

Kevin Hanegan: 00:34:15

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

Kevin Hanegan: 00:34:19

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

Kevin Hanegan: 00:34:22

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

Kevin Hanegan: 00:34:26

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

Kevin Hanegan: 00:34:28

actually helps you and your brain process it. It was

Kevin Hanegan: 00:34:31

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

Kevin Hanegan: 00:34:34

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

Kevin Hanegan: 00:34:37

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

Kevin Hanegan: 00:34:39

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

Kevin Hanegan: 00:34:42

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

Kevin Hanegan: 00:34:44

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

Guy Powell: 00:34:48

And it is definitely a way to reinforce and hopefully

Guy Powell: 00:34:52

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

Guy Powell: 00:34:56

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

Guy Powell: 00:35:00

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

Guy Powell: 00:35:04

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

Guy Powell: 00:35:07

learn more about about your book.

Kevin Hanegan: 00:35:09

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

Kevin Hanegan: 00:35:11

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

Kevin Hanegan: 00:35:15

for anyone who wants to not be overwhelmed with information and

Kevin Hanegan: 00:35:19

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

Kevin Hanegan: 00:35:22

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

Kevin Hanegan: 00:35:25

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

Kevin Hanegan: 00:35:28

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

Kevin Hanegan: 00:35:32

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

Kevin Hanegan: 00:35:35

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

Guy Powell: 00:35:38

Fantastic. So, Turning Data into Wisdom, and

Guy Powell: 00:35:42

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

Guy Powell: 00:35:47

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

Guy Powell: 00:35:50

Backstory on Marketing. Please visit

Guy Powell: 00:35:53

marketingmachine.prorelevant.com, and download the first chapter

Guy Powell: 00:35:58

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

Guy Powell: 00:36:02

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

Guy Powell: 00:36:06

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

Guy Powell: 00:36:08

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

Guy Powell: 00:36:12

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

Kevin Hanegan: 00:36:15

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